Computational geometry: an introduction
Computational geometry: an introduction
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Factor in the neighbors: Scalable and accurate collaborative filtering
ACM Transactions on Knowledge Discovery from Data (TKDD)
Factorizing personalized Markov chains for next-basket recommendation
Proceedings of the 19th international conference on World wide web
Training and testing of recommender systems on data missing not at random
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative filtering via euclidean embedding
Proceedings of the fourth ACM conference on Recommender systems
Build your own music recommender by modeling internet radio streams
Proceedings of the 21st international conference on World Wide Web
Efficient retrieval of recommendations in a matrix factorization framework
Proceedings of the 21st ACM international conference on Information and knowledge management
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Most recommenders research aims at personalized systems, which suggest items based on user profiles. However, in reality many systems deal with item-oriented recommendations. In such setups, given a single item of interest, the system needs to provide other related items, following patterns like "people who liked this also liked...". While item-oriented systems are central in their importance, they have been approached so far using very basic tools. We identify several hurdles faced by standard approaches to the item-oriented task. First, the sparseness of observed activities prevents establishing reliable similarity relations for many item pairs. Second, we address a scalability challenge at the retrieval stage present in many real-world systems: Given an item inventory, which may encompass millions of items, it is desired to identify the most related item pairs in a sub-quadratic time. This work addresses these two challenges, thereby improving both accuracy and scalability of item-oriented recommenders. Additionally, we propose an empirical evaluation scheme for comparing the quality of different solutions with encouraging results.