The invention relates to a method and an apparatus for generating an explanation for a recommendation, and more specifically to a method and an apparatus for generating an explanation for a recommendation based on the content of a recommended item and an item forming the basis for the recommendation.
Today recommendation systems are used in several contexts, e.g. e-commerce, VOD (Video On Demand), etc. The purpose of recommendation systems is to guide the user to new items that he might enjoy based on what he has already bought. A state of the art recommendation system can be found, for example, in G. Adomavicius et al.: “Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions”, IEEE Trans. Knowl. Data Eng. Vol. 17 (2005), pp. 734-749. Recommendation systems are usually collaborative. This means that the recommendations for a user are determined based on notes given by other users for the articles. An important advantage of the collaborative filtering approach is that it is not based on machine analyzable content. Therefore, collaborative filtering is capable of recommending complex items, such as movies, without requiring any understanding of the items.
However, as collaborative recommendation systems do not take into account the content of the recommended item they are unable to explain the process that led to the new recommendation in terms of similarities with other items that the user knows. Several studies have shown that users would like to have more information to understand why a specific item is being recommended. For example, the article J. L. Herlocker et al.: “Evaluating collaborative filtering recommender systems”, ACM Trans. Inf. Syst. Vol. 22 (2004), pp. 5-53. demonstrates that users welcome explanations based on the similarity with items they already know and appreciated.
It is thus an object of the present invention to propose a solution for generating an explanation for a recommendation.
According to the invention, a method for generating an explanation for a given recommendation of a multimedia content item selected from a catalog of multimedia content items, the recommendation being provided to a user, comprises the steps of:
According to the invention an explanation for a recommendation is generated, which shows how a recommended multimedia content item, such as a movie, a television show or series, a piece of music, and a book, is linked to a previously highly rated one. Concepts are associated to the multimedia content items, e.g. persons, places, events, topics, etc., based solely on their content. The concepts are advantageously derived from synopses of these items or from metadata associated to the recommended item and the previously accessed item, respectively. These metadata include, for example, the director or the author or the composer of the item, an actress, actor, protagonist or character, or the producer. At least some of the above concepts or metadata will typically be available, so that the comparing steps can generally be performed. The concepts themselves are arranged in a directed graph, which represents the relationships between the concepts. For example, Albert Camus is a famous French writer, Paris is the capital of France, etc. By determining a path between the concepts associated to the recommended item and the previously accessed item, respectively, a logical explanation for the recommendation is given. This logical explanation does not necessarily have any relation with the reason why the recommended item has actually been recommended. Generating a semantically valid explanation for a recommendation yields several advantages. It helps to generate a better acceptance of the recommendation system by building trust with the user. It will also increase the probability for the user to buy the recommended item. Finally, it makes unexpected items more acceptable to the user.
The vertices of the graph are derived from concepts of one or more knowledge bases. The edges of the graph are preferably derived from links or cross references within the concepts of the one or more knowledge bases. Knowledge bases that are readily available for this purpose are, for example, the online encyclopedia Wikipedia (http://en.wikipedia.org) or the Internet Movie Database IMDb (http://www.imdb.com). After selecting interesting concepts based on their content, e.g. person, place, event, topic, these are placed as vertices in the directed graph. The internal cross references or hyperlinks found in the knowledge base are used to build edges with an associated context, e.g. in the case of Wikipedia the extract of the article where the link is mentioned. The available knowledge bases allow to easily populate the directed graph with a large number of concepts. At the same time the cross references or hyperlinks inherently represent a useful relationship between the source and the target of the cross reference or the hyperlink.
Advantageously, an apparatus for generating an explanation for a recommendation of a multimedia content item selected from a catalog of multimedia content items, the recommendation being provided to a user, is adapted to perform a method as described for generating the explanation.
For a better understanding the invention shall now be explained in more detail in the following description with reference to the figures. It is understood that the invention is not limited to this exemplary embodiment and that specified features can also expediently be combined and/or modified without departing from the scope of the present invention as defined in the appended claims. In the figures:
In the following the invention shall be explained on the basis of a movie recommendation system for a VOD catalog. Of course, the invention is not limited to movie recommendation. The general idea is likewise applicable to other recommendation contexts.
A method for generating an explanation according to the general idea of the invention is schematically depicted in
Once the graph is available, the items of the catalog are associated 2 to the vertices of the graph. For this purpose the items of the catalog are compared to the vertices of the graph, using, for example, their associated textual metadata. The comparison is performed until the item matches a concept in the graph. In principle an item may match more than one concept. In this case the concept that is considered to be most relevant is preferably chosen. The resulting associations are then also stored in the memory for future use.
Once the above preliminary steps are finished, explanations for items that are recommended by a recommendation system are built 3 by randomly selecting another item from the catalog that the user appreciated. The recommended item and the selected item are then linked together using a short path algorithm, e.g. Dijkstra's algorithm or an equivalent algorithm.
An apparatus 4 adapted to perform the above described method is schematically depicted in
In
The generation of an explanation for a recommended item based on the vertices of the graph is schematically illustrated in
An exemplary explanation for the movie “Die Hard 2” is illustrated in
Number | Date | Country | Kind |
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11306294.7 | Oct 2011 | EP | regional |
Filing Document | Filing Date | Country | Kind |
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PCT/EP2012/068750 | 9/24/2012 | WO | 00 |