Patent Application
20160171228
The present invention relates generally to user profiling and user privacy in recommender systems. More specifically, the invention relates to demographic information inference.
Inferring demographics of users has been studied in different contexts, and for various types of user generated data. In the context of interaction networks, the graph structure has been shown to be useful for inferring demographics using link based information for blog and social network data from Facebook. Other works rely on the textual features derived from writings of users to infer demographics.
The major disadvantage of text-based inference is that most users do not provide written reviews, thus these methods are not applicable. Similarly, recommender systems might not get hold of the social network of the user they want to infer details about.
It can be seen that a user demographics inference method based on as little as information as possible is desired. This invention is directed to such an inference method.
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. The Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
The present invention includes a method and apparatus to obfuscate demographic information that can be determined from a user's ratings of digital content. In one embodiment, gender information may be determined from a user's movie ratings. To address privacy concerns, an obfuscation method and apparatus are presented. The obfuscation method includes training an inference engine that is in communication with an obfuscation engine. The inference engine determines demographic information using a training data set which includes movie ratings and demographic information from a plurality of other users. Then, movie ratings from the new user are received where the movie ratings from the particular user are received are without demographic information. The demographic information of the new user is determined using the trained inference engine. Extra movie ratings are then added to the user-generated ratings. The extra ratings are generated to be adverse to a finding of the user's demographic information if performed by an external inference engine. The external inference engine may be part of a recommender system which recommends movies for user viewing.
Additional features and advantages of the invention will be made apparent from the following detailed description of illustrative embodiments which proceeds with reference to the accompanying figures.
The foregoing summary of the invention, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the accompanying drawings, which are included by way of example, and not by way of limitation with regard to the claimed invention.
In the following description of various illustrative embodiments, reference is made to the accompanying drawings, which form a part thereof, and in which is shown, by way of illustration, various embodiments in the invention may be practiced. It is to be understood that other embodiments may be utilized and structural and functional modification may be made without departing from the scope of the present invention.
Profiling users through demographic information, such as gender, age, income, or ethnicity, is of great importance in targeted advertising and personalized content delivery. Recommender systems too can benefit from such information to provide personalized recommendations. However, users of recommender systems often do not volunteer this information. This may be intentional—to protect their privacy, or unintentional—out of laziness or disinterest. As such, traditional collaborative filtering methods, which extract meaningful information from patterns that emerge from collecting users' ratings from multiple users, eschew using such information, relying instead solely on ratings provided by users.
At a first glance, disclosing ratings to a recommender system may appear as a rather innocuous action. There is certainly a utility that users accrue from this disclosure—namely, the ability to discover relevant content/items. Nevertheless, there has been a fair amount of work indicating that user demographics are correlated to, and thus can be inferred from, user activity on social networks, blogs, and microblogs etc. It is thus natural to ask whether demographic information such as age, gender, ethnicity or even political orientation can also be inferred from information disclosed to collaborative filtering systems. Indeed, irrespective of a rating value, the mere fact that a user has interacted with an item (e.g., viewed a specific movie, listened to a specific song, or purchased a product) may be correlated with demographic information.
The potential success of such an inference has several important implications. On one hand, from the recommender's perspective, profiling users with respect to demographic information opens the way to several applications; beyond recommendations, such profiling can generate additional revenue through advertising, as advertisers are primarily interested in targeting specific demographic groups. The present invention is directed towards such inferring techniques. It is assumed that the information users wish to infer is their gender; nevertheless, the methods of the invention also apply when different demographic features (age, ethnicity, political orientation, etc.) are to be inferred. Also, although specific embodiments are directed to ratings on movies, this is only one example. Ratings of any type may be used, including but not limed to ratings on songs, digital games, products, restaurants, and the like. For simplicity and clarity of understanding, the example of using movie ratings to determine demographic information is primarily used, but other types of ratings are also applicable.
In the current invention context, the inference engine 135 can be a data processing device that can infer demographic information from non-demographic information provided by a user 125 who sends movie ratings to the recommender system 130. The inference engine 135 functions to process the movie ratings provided by user 125 and infer demographic information. In one example instance, the demographic information discussed is gender. But one of skill in the art will recognize that other demographic information may also be inferred according to aspects of the invention. Such demographic information may include, but is not limited to, age, ethnicity, political orientation, and the like.
According to an aspect of the invention, as described below, the inference engine 135 operates using training data acquired via users 1, 2 to n (105, 110 to 115 respectively). These users provide movie rating data as well as demographic information to the inference engine 135 via the recommender system 130. The training data set may be acquired over time as users 105 through 115 use the recommender system. Alternately, the inference engine can input a training data set in one or more data loads directly imported via an input port 136. Port 136 may be used to input a training data set from a network, a disk drive, or other data source containing the training data.
Inference engine 135 utilizes algorithms to process the training data set. The inference engine 135 subsequently utilizes user 125 (user X) inputs containing movie ratings. Movie ratings contain one or more of a movie identification, such as a movie title or a movie index or reference number and a rating value to infer demographic information concerning user 125. A “movie title”, or more generically “movie identifier” as used in this discussion, is an identifier, such as a name or title or a database index of the movie, show, documentary, series episode, digital game, or other digital content viewed by user 125. A rating value is a subjective measure of the viewed digital content as judged by user 125. Normally, rating values are quality assessments made by the user 125 and are graded on a scale from 1 to 5; 1 being a low subjective score and 5 being a high subjective score. Those of skill in the art will recognize that other may equivalently be used such as a 1 to 10 numeric scale, an alphabetical scale, a five star scale, a ten half star scale, or a word scales ranging from “bad” to “excellent”. Note that according to aspects of the invention, the information provided by user 125 does not contain demographic information and the inference engine 135 determines the user 125's demographic information from only her movie ratings.
According to an aspect of the invention, a training data set is used to teach the inference engine 135. The training data set may be available to both the recommended system 130 as well as the inference engine 135. A characterization of the training data set is now provided. The training dataset comprises a set of N={1, . . . , N} users each of which has given ratings to a subset of the movies in the catalog M. Denoted by Si⊂M is the set of movies for which the rating of a user i∈N is in the dataset, and by ri,j, j∈Si, the rating given by user i∈N to movie j∈M. Moreover, for each i∈N the training set also contains a binary variable yi∈{0,1} indicating the gender of the user (bit 0 is mapped to male users). The training data set is assumed unadulterated: neither ratings nor gender labels have been tampered with or obfuscated.
The recommender mechanism throughout the paper is assumed to be matrix factorization since this is commonly used in commercial systems. Although matrix factorization is utilized as an example, any recommender mechanism may be used. Alternate recommender mechanisms include the neighborhood method (clustering of users), contextual similarity of items, or other mechanism known to those of skill in the art. Ratings for the set M\S0 are generated by appending the provided ratings to the rating matrix of the training set and factorizing it. More specifically, we associate with each user i∈N∪{0} a latent feature vector ui∈d. Associated with each movie j∈M is a latent feature vector vj∈
d. The regularized mean square error is defined to be
where μ is the average rating of the entire dataset. The vectors ui, vj are constructed by minimizing the MSE through gradient descent. Values of d=20 and λ=0.3 are used. Having profiled thusly both users and movies, the rating of user 0 is predicted for movie j∈M\S0′ through <u0,vj>=μ.
Two example training datasets are considered; Flixster and Movielens. Flixster is a publicly available online social network for rating and reviewing movies. Flixster allows users to enter demographic information into their profiles and share their movie ratings and reviews with their friends and the public. The dataset has 1M users, of which only 34.2K users share their age and gender. This subset of 34.2K users is considered, who have rated 17K movies and provided 5.8M ratings. The 12.8K males and 21.4K females have provided 2.4M and 3.4M ratings, respectively. Flixster allows users to provide half star ratings, however, to be consistent across the evaluation datasets, the ratings are rounded up to be integers from 1 to 5. Another data set is Movielens. This second dataset is publicly available from the Grouplens™ research team. The dataset consists of 3.7K movies and 1M ratings by 6K users. The 4331 males and 1709 females provided 750K and 250K ratings, respectively.
To determine demographic information, classifiers are used in the inference engine. As expressed above, demographic information can include many characteristics. The determination of gender as an example demographic is expressed as one embodiment in the current invention. However, the determination of different or multiple demographic characteristics of a user is within the scope of the present invention.
To train classifiers, they are associated with each user i∈N in the training set a characteristic vector xi∈M such that xij=rij, if j∈Si and xij=0, otherwise. Recall that the binary variable yi indicates user i's gender, which serves as the dependent variable in classification. Denote by X∈
N×M is the matrix of characteristic vectors, and by Y∈{0,1}N the vector of genders.
Three different types of classifiers are examined: Bayesian classifiers, support vector machines (SVM), and logistic regression. In the Bayesian setting, several different generative models are examined; for all models, assume that points (xi,yi) are sampled independently from the same joint distribution P(x,y). Given P, the predicted label ŷ∈{0,1} attributed to characteristic vector x is the one with maximum likelihood, i.e.,
ŷ=argmaxy∈{0,1}P(y|x)=argmaxy∈{0,1}P(x,y) (1)
The class prior classification is now described. The class prior classification serves as a base-line method for assessing the performance of the other classifiers. Given a dataset with unevenly distributed gender classes of the population, this basic classification strategy is to classify all users as having the dominant gender. This is equivalent to using equation (1) under the generative model P(y|x)=P(y), estimated from the training set as:
P(y)=|{i∈N:yi=y}|/N. (2)
The Bernoulli Naïve Bayes is classification is now described. Bernoulli Naïve Bayes is a simple method that ignores the actual rating value. In particular, it assumes that a user rates movies independently and the decision to rate or not is a Bernoulli random variable. Formally, given a characteristic vector x, we define the rating indicator vector {tilde over (x)}∈M to be such that {tilde over (x)}j=1x
P({tilde over (x)}j|y)=|{i∈N:{tilde over (x)}ij={tilde over (x)}ĵyi=y}|/|{i:yi=y}| (3)
The Multinomial Naïve Bayes classification is now described. A drawback of Bernoulli Naïve Bayes is that it ignores rating values. One way of incorporating them is through Multinomial Naïve Bayes, which is often applied to document classification tasks. Intuitively, this method extends Bernoulli to positive integer values by treating, e.g. a five-star rating as 5 independent occurrences of the Bernoulli random variable. Movies that receive high ratings have thus a larger impact on the classification. Formally, the generative model is given by P(x,y)=P(y)Πj∈MP(xj|y) where P(xj|y)=P({tilde over (x)}j|y)x
A mixed Naïve Bayes is now described according to an aspect of the invention. An alternative to above-described Multinomial, which the inventors refer to as Mixed Naïve Bayes. This model is based on the assumption that, users give normally distributed ratings. More specifically,
P(xj|{tilde over (x)}j=1,y)=(2πσy2)−1/2e−(x
For each movie j, an estimate of the mean μyj is from the dataset as the average rating of movie j given by users of gender y, and the variance σy2 is estimated as the variance of all ratings given by users of gender y. The joint likelihood used in equation (1) is then given by P(x,y)=P(y)Πj∈MP({tilde over (x)}j|y)P(xj|{tilde over (x)}j,y) where P(y), P({tilde over (x)}j|y) are estimated through equations (2) and (3), respectively. The conditional P(xj|{tilde over (x)}j,y) is given by equation (4) when a rating is provided (i.e., {tilde over (x)}j=1) and, trivially, by P(xj=0|{tilde over (x)}j=0,y)=1, when it is not.
The use of logistic regression in the current invention is now described. A significant drawback of all of the above Bayesian methods is that they assume that movie ratings are independent. To address that, the inventors applied logistic regression. Recall that linear regression yields a set of coefficients β={β0, β1, . . . , βM}. The classification of a user i∈N with characteristic vector xi is performed by first calculating the probability pi=(1+e−(β
In machine learning, support vector machines (SVM) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, and are used for classification and regression analysis. Intuitively, an SVM finds a hyperplane that separates users belonging to different genders in a way that minimizes the distance of incorrectly classified users from the hyperplane as is well known in the art. An SVM holds many of the advantages of logistic regression; it does not assume independence in the feature space and produces coefficients. Since the feature space (number of movies) is already quite large, linear SVMs are used in the classifier evaluations. Performing a logarithmic search over the parameter space (C), the inventors find that C=1 gave the best results.
All algorithms were evaluated on both the Flixster and Movielens datasets. 10-fold cross validation were used and the average precision and recall were computed for the two the mean Receiver Operating Characteristic (ROC) curve computed across the folds. For the ROC, the true positive ratio is computed as the ratio of males correctly classified out of the males in the dataset, and the false positive ratio is computed as the ratio incorrectly classified males out of the females in the dataset. Table 1 provides a summary of the classification results for 3 metrics: AUC, precision and recall. Table 2 shows the same results separated per-gender. The ROC curves are given in
As seen from the ROC curves, the SVM and logistic regression perform better, across both datasets, than any of the Bayesian models since the regression curves for SVM and logistic dominate the others. In particular, logistic regression performed the best for Flixster while SVM performed best for Movielens. The performance of the Bernoulli, mixed, and multinomial models do not different significantly from one another. These findings are further confirmed via the AUC values in Table 1. This table also shows the weakness of the simple class prior model that is easily outperformed by all other methods.
In general, precision in a classification task is the number of true positives (i.e. the number of items correctly labeled as belonging to the positive class) divided by the total number of elements labeled as belonging to the positive class (i.e. the sum of true positives and false positives, which are items incorrectly labeled as belonging to the class). Recall in this context is defined as the number of true positives divided by the total number of elements that actually belong to the positive class (i.e. the sum of true positives and false negatives, which are items which were not labeled as belonging to the positive class but should have been).
In terms of precision and recall, Table 2 shows that logistic regression outperforms all other models for Flixster users and both genders. For the Movielens users, SVM performs better than all other algorithms, while logistic regression is second best. In general, the inference performs better for the gender that is dominant in each dataset (female in Flixster and male in Movielens). This is especially evident for SVM, which exhibits very high recall for the dominate class and low recall for the dominated class. The mixed model improves significantly on the Bernoulli model and results similarly to the multinomial. This indicates that the usage of a Gaussian distribution might not be a sufficiently accurate estimation for the distribution of the ratings.
The impact of user ratings with respect to the rating value itself (number of stars or other subjective scale) versus the simple binary event “watched or not” is assessed by applying logistic regression and SVM on a binary matrix, denoted by {tilde over (X)}, in which ratings are replaced by 1. Table 1 shows the performance of these two methods on X and {tilde over (X)}. Interestingly, SVM and logistic regression performed only slightly better when using X rather than {tilde over (X)} as input, with less than 2% improvement on all measures. In fact, Table 2 indicates that although using X performs better than using {tilde over (X)} for the dominant class, it is worse for the dominated class. Similarly, the Bernoulli model, which also ignores the rating values, performed relatively close to Multinomial and Mixed. This implies that whether or not a movie is included in one's profile is nearly as impactful as the value of star rating given for the movie.
The effect of the training set size was evaluated. Since 10-fold cross validation was used, the training set is large relative to the evaluation set. The Flixster data is used to assess the effect that the number of users in the training set size has on the inference accuracy. In addition to the 10-fold cross validation giving 3000 users in the evaluation set, a 100-fold cross validation was performed using a 300-user evaluation set. Additionally, incrementally increasing the training set, starting from 100 users and adding 100 more users on each iteration was performed.
The movie-gender correlation was considered. The coefficients computed by logistic regression expose movies that are most correlated with males and females. Table 3 lists the top 10 movies correlated with each gender for Flixster; similar observations as the ones below hold for Movielens. The movies are ordered based on their average rank across the 10-folds. Average rank was used since the coefficients can vary significantly between folds, but the order of movies does not. The top gender correlated movies are quite different depending on whether X or {tilde over (X)} is used as input. For example, out of the top 100 most female and male correlated movies, only 35 are the same for males across the two inputs, and 27 are the same for females; the comparison yielded a Jaccard distance of 0.19 and 0.16, respectively. Many of the movies in both datasets align with the stereotype that action and horror movies are more correlated with males, while drama and romance are more correlated with females. However, gender inference is not straightforward because the majority of popular movies are well liked by both genders.
Table 3 shows that in both datasets some of the top male correlated movies have plots that involve gay males, (such as Latter Days, Beautiful Thing, and Eating Out); we observed the same results when using {tilde over (X)}. The main reason for this is that all of these movies have a relatively small number of ratings, ranging from a few tens to a few hundreds. In this case it is sufficient for a small variance in the rating distributions between genders with respect to the class priors, to make the movie highly correlated with the class.
Having fully characterized the SVM and linear regression classifiers on the two available data sets, and having favorable results, a novel method and apparatus is invented to realize an inference engine.
The method 300 of
At step 315, a new user that is not in the training data set, such as user 125, interacts with the recommender system 130 and provides only ratings. As described above, these ratings can be, for example, movie ratings having movie identifier information and subjective rating value information. The ratings provided by user 125 are without demographic information that is sought by the inference engine. After the new user 125 inputs her ratings into the recommender system, then, at step 320 the inference engine 135 uses a classification algorithm to determine the new user's demographic information based on the new user's ratings. The classification algorithm is preferably one of support vector machines (SVM), or logistic regression as discussed earlier.
Having determined the new user's demographic information, the determined demographic information, such as gender, may be used for many useful purposes. Two examples are provided in
Either or both of step 325 or 330 may be taken as useful actions taken to exploit the demographic information extracted from the ratings provided by the new user 125. Steps 315 through 330 may be repeated for each new user that utilizes the services of the recommender system 130. A user that receives an enhanced recommendation or an advertisement from the recommender system would receive the enhanced recommendation or advertisement on a display device associated with the user, such as user 125. Such user display devices are well known and include display devices associated with home television systems, stand alone televisions, personal computers, and handheld devices, such as personal digital assistants, laptops, tablets, cell phones, and web notebooks.
Processor 420 provides computation functions for the inference engine 135. The processor can any form of CPU or controller that utilizes communications between elements of the inference engine to control communication and computation processes for the inference engine. Those of skill in the art recognize that bus 415 provides a communication path between the various elements of inference engine 135 and that other point to point interconnections are also feasible.
Program memory 430 can provide a repository for memory related to the method 300 of
Estimator 450 may be separate or part of processor 420 and functions to provide calculation resources for determination of the demographic information from a new user's ratings. As such, estimator 450 can provide computation resources for the classifier, preferably either SVM or logistic regression. The estimator can provide interim calculations to data memory 440 or processor 420 in the determination of a new user's demographic information. Such interim calculations include the probability of the demographic information related to the new user given only her rating information. The estimator 450 may be hardware, but is preferably a combination of hardware and firmware or software.
Given a relatively small training set, the inference algorithms correctly predict the gender of users with a precision of 70%-80%. However, the above discussed technique for determining demographic information from a user's ratings may invoke privacy concerns for the user. Some users may find it desirable to obfuscate their demographic information from reliable determination. An obfuscation mechanism to protect detectable demographic information from reliable detection is addressed below.
In another embodiment, a content aggregator that serves to distribute content to a user could also act to preserve a user's demographic information by providing an obfuscation engine along with the content aggregation service.
Processor 592 provides computation functions for the obfuscation engine 599. The processor can be any form of CPU or controller that utilizes communications between elements of the obfuscation engine to control communication and computation processes for the obfuscation engine. Those of skill in the art recognize that bus 597 provides a communication path between the various elements of obfuscation engine 599 and that other point to point interconnection options instead of a bus architecture are also feasible.
Program memory 593 can provide a repository for memory related to the method 600 of
The inference engine 596 may be separate or part of processor 592 and functions to provide calculation resources for determination of the demographic information from a new user's ratings. As such, the inference engine may be similar to that of
Characterization of the obfuscation engine is now described. A user, such as user 125, indexed by 0, views and rates digital content items such as movies. The universe of movies the user can rate is assumed to comprise a catalog of M movies; the user rates a subset S0 of the catalog M={1, 2, . . . , M}. Denoted by r0j∈ is the rating of movie j∈S0 and the user's rating profile is defined as the set of (movie, ranking) pairs H0≡{(j, r0j): j∈S0}. Referring to
More specifically, the obfuscated rating profile H′0 is assumed to be submitted to a recommender system 130 that has a module that implements a gender inference engine 135. The recommender system 135 uses H′0 to predict the user's ratings on M\S′0, and potentially, recommend movies that might be of interest to the user. The gender inference engine 135 is a classification mechanism, that uses the same H′0 to profile and label the user as either male of female.
Though the implementation of the recommender system 130 might be publicly known, the obfuscation engine 126 and gender inference engine 135 are not. As a first step in this problem, the simple approach is taken that both recommender system 130 and inference engine 135 are oblivious to the fact that any kind of obfuscation is taking place. Both mechanisms take the profile H′ at “face value” and do not reverse-engineer the “true” profile H.
As discussed above, the recommender system 130 and inference engine 135 have access to the training dataset. It is assumed that the training data set is unadulterated; neither ratings nor demographic information, such as gender labels, have been tampered with or obfuscated. The obfuscation engine 126 may also have a partial view of the training set. In one embodiment, the training dataset is public, and the obfuscation engine 126 has full access to it.
In general, a confidence value of the classifier used in the inference engine 135 is the obstacle that an obfuscation engine needs to overcome when trying to hide demographic information, such as gender, from the classifier. The obfuscation engine attempts to lower this confidence value of the classifier in the inference engine 135. Therefore, an evaluation of whether the classifier has different confidence values when it outputs a correct or incorrect classification is undertaken. With respect to evaluation of a classifier used for the inference engine,
The obfuscation engine has a mechanism that takes as input a user i's rating profile Hi, a parameter k that represents the number of permitted alterations, and information from the training set to output an altered rating profile H′i such that it is hard to infer the gender of the user while minimally impacting the quality of recommendations received. In general, such a mechanism can alter Hi by adding, deleting or changing movie ratings. Focus is placed on the setting in which the obfuscation engine is only allowed to add k movie ratings, since deleting movies is impractical in most services and changing ratings is less effective than adding ratings when the viewing event is a strong predictor of the user's demographic attributes. Because users have different numbers of movies rated in their profiles (and some may have a small number), a fixed number k is not used but rather an added number that corresponds to a given percentage of movies in a user's rating profile. In order to add movies into a user's profile, the obfuscation engine needs to make two non-trivial decisions; which movies should be added and what should be the rating assigned to each movie.
These added movie ratings as termed extra ratings. The extra ratings are adverse to a correct determination of the demographic information of the user. The rating values in the rating pairs (title, rating value) of the extra ratings are not assigned as “noise” but have some useful value. For example, if this rating corresponds to the average rating over all users, or the predicted rating (using matrix factorization) for a specific user, then the rating value is a reasonable predictor of how the user may have rated if she had watched the movie.
To establish an obfuscation scheme, it is first assumed that the obfuscation mechanisms have full access to the training dataset, and the mechanism can use the training data set to derive information for selecting movies and ratings to add. Concerning movie selection for an obfuscation engine, the inventors have selected three strategies for selecting movies. Each strategy takes as input a set of movies Si rated by the user i, the number of movies k to be added, and ordered lists LM and LF of male and female correlated movies, respectively, and outputs an altered set of movies S′i, where Si⊂S′i. The lists LM and LF are stored in decreasing order of the value of a scoring function w: LM∪LF→ where w(j) indicates how strongly correlated a movie j∈LM∪LF is with the associated gender. A concrete example of the scoring function is to set w(j)=βj, where βj is the coefficient of movie j obtained by learning a logistic regression model from the training dataset. This instantiation of the scoring function is used for evaluation. Additionally, assume that k<min(|LM|, |LF|)−|Si| and LM∩LF=Ø.
The movie selection process is as follows. For a given female (or, male) user i, initialize S′i=Si. Each strategy repeatedly picks a movie j from LM (or, LF), and if j∉S′i it adds j to S′i, until k movies have been added. The set S′i is the desired output. The three strategies differ in how a movie is picked from the ordered lists of movies.
Considering a Random Strategy, for a given female (male) user i, pick a movie j uniformly at random from the list corresponding to the opposite gender LM (LF), irrespective of the score of the movie. Considering a Sampled Strategy, one can sample a movie based on the distribution of the scores associated with the movies in the list corresponding to the opposite gender. For instance, if there are three movies j1, j2, j3 in LM with scores 0.5, 0.3, 0.2, respectively, then j1 will be picked with probability 0.5 and so on. Considering a Greedy Strategy, one can pick the movie with the highest score in the list corresponding to the opposite gender.
Considering rating assignment in the rating value pair (title, rating value), it was noted earlier that the binary event of including or excluding a movie in a profile (indicating watched or not) was a signal for gender inference nearly as strong as the ratings. This has important ramifications for obfuscation mechanisms that need to do two things: decide which movies to add to a user profile, and decide which rating value to give a. movie. This finding suggests that the choice of which movies to add could have a large impact on impeding gender inference. However if the actual ratings do not impact gender inference much, then we could select a rating value that helps maintain the quality of recommendations. Given that the binary event of including or excluding a movie in a profile was itself a signal for gender inference, then one can assign ratings to the extra movies that have a low impact on the recommendations provided to a user via the recommender system 130. Two rating assignments are proposed; average movie rating and predicted rating.
In the average movie rating, the obfuscation mechanism uses the available training data to compute the average rating for all movies j∈S′i−Si and add them to user i's altered rating profile H′i. In the predicted rating, the obfuscation mechanism computes the latent factors of movies by performing matrix factorization on the training dataset, and uses those to predict a user's ratings. The predicted ratings for all movies j∈S′i−Si are added to H′i.
Earlier it was assumed the obfuscation engine 126 had unrestricted access to the training set. However, the mechanisms described above require access only to the following quantities: (a) for movie selection: ordered lists of male and female correlated movies, and (b) for rating assignment: average movie ratings, and movie latent factors to predict user movie ratings. Note that this information can be found from publicly available datasets, such as the Netflix Prize™ dataset. Assuming that users in such public datasets are statistically similar overall to those in a particular recommender system, then there is no longer the need to assume full access to the training data set specifically used in the inference engine 135.
An evaluation of all the permutations of movie selection and rating assignment strategies proposed above was performed. Values of k are evaluated corresponding to 1%, 5% and 10% |Si| for each user i. The movie scores in lists LM and LF are set to the corresponding logistic regression coefficients.
There is a privacy gain that obfuscation brings via the reduced performance in gender inference. Table 4 shows the accuracy of inference for all three movie selection strategies (i.e., random, sampled and greedy) when the rating assigned is the average movie rating. The accuracy is computed using 10 fold cross validation, where the model is trained on unadulterated data, and tested on obfuscated data. Since the accuracy of inference is the highest for the logistic regression classifier, it would be the natural choice as the inference mechanism for a recommender system. On adding just 1% extra ratings using the greedy strategy, the accuracy drops to 15% (that is an 80% decrease) and with 10% extra ratings the accuracy is close to zero for the Flixster dataset, as compared with the accuracy of 76.5% on the unadulterated data. The tradeoff in privacy and utility for different obfuscation mechanisms show that just 1% additional ratings to a user's profile decreases the inference accuracy by 80%.
Therefore, if the obfuscation mechanism selects movies according to the greedy strategy, adding a small number of movies is sufficient to obfuscate gender. Even when the movies are chosen using the random strategy (which ignores movie scores and hence, the logistic regression coefficients), just 10% additional movies correlated with the opposite gender are sufficient to decrease the accuracy of gender inference by 63% (from 76.5% to 28.5% accuracy). Similar trends are observed for the Movielens dataset.
The obfuscation mechanism above uses ordered lists that correspond well to the inference mechanism's notion of male or female correlated movies. However, in general, the obfuscation mechanism does not know which inference algorithm is used and thus lists such as LM and LF may have a weaker match to such a notion interior to the inference algorithm. The obfuscation mechanism is evaluated under such a scenario; with Multinomial Naïve Bayes and SVM classifiers. The obfuscation still performs well as we see in Table 4, the inference accuracy of the Multinomial classifier drops from 71% to 42.1% for Flixster, and from 76% to 60% for the Movielens dataset (with 10% extra ratings and the greedy strategy).
The impact on the recommendation quality that the user will observe if she obfuscates her gender was considered. This impact is measured by computing the root mean square error (RMSE) of matrix factorization on a held-out test set of 10 ratings for each user. Again, a 10 fold cross validation was performed, where the data for users in 9 folds is unadulterated, and one of the folds has users with additional noisy ratings. That is, H′ is used for a tenth of the users, and H for the rest. This is equivalent to evaluating the change in RMSE for 10% of the users in the system who obfuscate their gender. Overall, the inventors observed that obfuscation has negligible impact on RMSE. For Flixster, compared to the case of no extra ratings, the RMSE increases with additional ratings, although negligibly. For the Movielens training dataset, a slight decrease in RMSE with extra ratings occurs. This may occur because by adding extra ratings we increase the density of the original rating matrix which may turn improve the performance of matrix factorization solutions. Another explanation could be that the extra ratings are not arbitrary, but somewhat meaningful (i.e., the average across all users). The key observation is that for both datasets, the change in RMSE is not significant, a maximum of 0.015 for Flixster (with random strategy and 10% extra ratings), and 0.058 for Movielens (with sampled strategy and 10% extra ratings). Thus, the obfuscation engine preserves recommender system quality of recommendations to the user.
The privacy-utility tradeoff of the proposed obfuscation is examined, where the desired high privacy corresponds to a low accuracy of gender inference, and a high utility corresponds to a low RMSE which is often used as a proxy for high quality recommendations. Upon evaluation, the inventors find that for the Flixster training dataset, utility decreases as privacy increases. As described above, using the Movielens training data set, the utility increases however only slightly as privacy increases, the utility increases. The obfuscation mechanism can lead to a substantial reduction in gender inference accuracy yet only incurs very small changes to the quality of the recommendations.
Preserving recommendation quality is an appealing feature for the obfuscation engine. In one evaluation, the tradeoff when the rating assignment corresponds to the “predicted ratings” approach is considered. The motivation behind this rating assignment is that, in principle, this obfuscation results in no change in RMSE as compared with the RMSE on unaltered data. In other words, there is no tradeoff to be made on the utility front with this choice of rating assignment. Table 5 shows the accuracy of gender inference when this rating assignment is used. The results are similar those in Table 4 where the rating assignment is the average movie rating. For the Movielens training data set, the accuracy of gender inference is slightly lower with predicted ratings; for example, for the greedy strategy with 1% extra ratings, the accuracy of the logistic regression classifier reduces from 57.7% to 48.4%—and this benefit comes without sacrificing the quality of recommendations. In conclusion, the experimental evaluation shows that with small amount of additional ratings, it is possible to protect a user's gender by obfuscation, with an insignificant change to the quality of recommendations received by the user.
After training the inference engine, the obfuscation engine is ready for use by a new user. At step 615, a new user, who is not one of the users in the training data set, provides ratings to the obfuscation engine. As a result, the obfuscation engine receives ratings, such as movie ratings. The received movie ratings are only rating pairs of (title, rating value) and are without demographic information of the new user.
At step 620, the inference engine, such as 575 or 596, uses a classification algorithm to determine the new users' demographic information based on the user's ratings. At step 625, the obfuscation engine generates ratings that are adverse to an accurate determination of demographic information by another inference engine. That is, the generated ratings are extra ratings that can be added to the ratings of the user that help obfuscate detectable demographic information of the user. By simple example, if the inference engine infers the gender of user 125 as female, then the extra ratings generated by the obfuscation engine will provide data that incorrectly infers the user's gender. Accordingly, an external inference engine, such as one in a recommender system, would be unable to accurately determine the gender demographic information of new user 125. Thus, the extra ratings are adverse to an accurate detection of the demographic information of the new user.
The extra ratings are transmitted to a recommender system (RS) by the obfuscation engine at step 630. This has the effect of obscuring the demographic information of the user 125 as detected by an inference engine in the recommender system 130. This obfuscation occurs because the external inference engine, such as 135 of
Although specific architectures are shown for the implementation of an obfuscation engine in
This application claims priority to U.S. Provisional Application No. 61/662,618 entitled “Method and Apparatus For Obfuscating User Demographics Based on Ratings”, filed on 21 Jun. 2012, which is hereby incorporated by reference in its entirety for all purposes.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US2013/440890 | 6/10/2013 | WO | 00 |
| Number | Date | Country | |
|---|---|---|---|
| 61662618 | Jun 2012 | US |
