Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Improving Case-Based Recommendation: A Collaborative Filtering Approach
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Proceedings of the 10th international conference on Intelligent user interfaces
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Boosting collaborative filtering based on statistical prediction errors
Proceedings of the 2008 ACM conference on Recommender systems
Exploiting user similarity based on rated-item pools for improved user-based collaborative filtering
Proceedings of the third ACM conference on Recommender systems
A Spatial User Similarity Measure for Geographic Recommender Systems
GeoS '09 Proceedings of the 3rd International Conference on GeoSpatial Semantics
Recommending based on rating frequencies
Proceedings of the fourth ACM conference on Recommender systems
List-wise learning to rank with matrix factorization for collaborative filtering
Proceedings of the fourth ACM conference on Recommender systems
Mining mood-specific movie similarity with matrix factorization for context-aware recommendation
Proceedings of the Workshop on Context-Aware Movie Recommendation
Expert Systems with Applications: An International Journal
Collaborative filtering with collective training
Proceedings of the fifth ACM conference on Recommender systems
Improving the performance of recommender system by exploiting the categories of products
DNIS'11 Proceedings of the 7th international conference on Databases in Networked Information Systems
Using inferred tag ratings to improve user-based collaborative filtering
Proceedings of the 27th Annual ACM Symposium on Applied Computing
A collaborative filtering recommendation system by unifying user similarity and item similarity
WAIM'11 Proceedings of the 2011 international conference on Web-Age Information Management
Information Sciences: an International Journal
Improving recommendation accuracy based on item-specific tag preferences
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
A novel user-based collaborative filtering method by inferring tag ratings
ACM SIGAPP Applied Computing Review
Unifying rating-oriented and ranking-oriented collaborative filtering for improved recommendation
Information Sciences: an International Journal
How should I explain? A comparison of different explanation types for recommender systems
International Journal of Human-Computer Studies
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Collaborative filtering (CF) is a successful approach for building online recommender systems. The fundamental process of the CF approach is to predict how a user would like to rate a given item based on the ratings of some nearest-neighbor users (user-based CF) or nearest-neighbor items (item-based CF). In the user-based CF approach, for example, the conventional prediction procedure is to find some nearest-neighbor users of the active user who have rated the given item, and then aggregate their rating information to predict the rating for the given item. In reality, due to the data sparseness, we have observed that a large proportion of users are filtered out because they don't rate the given item, even though they are very close to the active user. In this paper we present a recursive prediction algorithm, which allows those nearest-neighbor users to join the prediction process even if they have not rated the given item. In our approach, if a required rating value is not provided explicitly by the user, we predict it recursively and then integrate it into the prediction process. We study various strategies of selecting nearest-neighbor users for this recursive process. Our experiments show that the recursive prediction algorithm is a promising technique for improving the prediction accuracy for collaborative filtering recommender systems.