The Fault in Our Recommendations: On the Perils of Optimizing the Measurable
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- The Fault in Our Recommendations: On the Perils of Optimizing the Measurable
Recommendations
Agent-based collaborative filtering based on fuzzy recommendations
Recommender systems intend to provide suggestions based on the opinion of several sources of information. But personalised suggestions based on past user's likes and dislikes require a distributed approach. In this way, agents may automatically collect ...
Expediting Exploration by Attribute-to-Feature Mapping for Cold-Start Recommendations
RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender SystemsThe item cold-start problem is inherent to collaborative filtering (CF) recommenders where items and users are represented by vectors in a latent space. It emerges since CF recommenders rely solely on historical user interactions to characterize their ...
Using Implicit Feedback for Neighbors Selection: Alleviating the Sparsity Problem in Collaborative Recommendation Systems
WebMedia '17: Proceedings of the 23rd Brazillian Symposium on Multimedia and the WebThe most popular Recommender systems (RSs) employ Collaborative Filtering (CF) algorithms where users explicitly rate items. Based on these ratings, a user-item rating matrix is generated and used to select the items to be recommended for a target user. ...
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- SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
- SIGAI: ACM Special Interest Group on Artificial Intelligence
- SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
- SIGIR: ACM Special Interest Group on Information Retrieval
- SIGCHI: ACM Special Interest Group on Computer-Human Interaction
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Association for Computing Machinery
New York, NY, United States
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