Abstract
Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user. Often, better recommendations can be generated if the context of the recommendation is known, e.g., in a music RS,
the user mood or activity. However, to adapt the recommendations to the context the dependency of the user preferences from the contextual conditions must be modeled. This requires explicit user evaluations/ratings for items in
alternative contexts. In this work we investigate a novel approach for collecting and using contextually dependent ratings in recommender systems. We introduce the concept of “best context”, i.e., the contextual conditions most suited for a particular item to be recommended. We designed an interface for collecting such data for music tracks. The collected data was then used to evaluate the quality of several “best context” prediction methods based on user-to-user collaborative filtering. The results, in opposition to what we expected, show that the notion of best context is user dependent. Moreover, among the approaches we tried, the best performing one uses a k-nearest neighbors classifier where the user-to- user similarity measures the agreement of two
users in assigning the best context to items.
the user mood or activity. However, to adapt the recommendations to the context the dependency of the user preferences from the contextual conditions must be modeled. This requires explicit user evaluations/ratings for items in
alternative contexts. In this work we investigate a novel approach for collecting and using contextually dependent ratings in recommender systems. We introduce the concept of “best context”, i.e., the contextual conditions most suited for a particular item to be recommended. We designed an interface for collecting such data for music tracks. The collected data was then used to evaluate the quality of several “best context” prediction methods based on user-to-user collaborative filtering. The results, in opposition to what we expected, show that the notion of best context is user dependent. Moreover, among the approaches we tried, the best performing one uses a k-nearest neighbors classifier where the user-to- user similarity measures the agreement of two
users in assigning the best context to items.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2nd Workshop on Context Aware Recommender Systems |
| State | Published - 2010 |