Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Probabilistic Memory-Based Collaborative Filtering
IEEE Transactions on Knowledge and Data Engineering
EigenRank: a ranking-oriented approach to collaborative filtering
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
A Survey of Accuracy Evaluation Metrics of Recommendation Tasks
The Journal of Machine Learning Research
Multilabel classification with meta-level features
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
List-wise learning to rank with matrix factorization for collaborative filtering
Proceedings of the fourth ACM conference on Recommender systems
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Most existing recommender systems can be classified into two categories: collaborative filtering and content-based filtering. Hybrid recommender systems combine the advantages of the two for improved recommendation performance. Traditional recommender systems are rating-based. However, predicting ratings is an intermediate step towards their ultimate goal of generating rankings or recommendation lists. Learning to rank is an established means of predicting rankings and has recently demonstrated high promise in improving quality of recommendations. In this paper, we propose LRHR, the first attempt that adapts learning to rank to hybrid recommender systems. LRHR first defines novel representations for both users and items so that they can be content-comparable. Then, LRHR identifies a set of novel meta-level features for learning purposes. Finally, LRHR adopts RankSVM, a pairwise learning to rank algorithm, to generate recommendation lists of items for users. Extensive experiments on benchmarks in comparison with the state-of-the-art algorithms demonstrate the performance gain of our approach.