Identifying Controversial Pairs in Item-to-Item Recommendations
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- Identifying Controversial Pairs in Item-to-Item Recommendations
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A Context-Aware User-Item Representation Learning for Item Recommendation
Both reviews and user-item interactions (i.e., rating scores) have been widely adopted for user rating prediction. However, these existing techniques mainly extract the latent representations for users and items in an independent and static manner. That ...
Item-Based Collaborative Filtering Recommendation Algorithm Combining Item Category with Interestingness Measure
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Item cold-start recommendations: learning local collective embeddings
RecSys '14: Proceedings of the 8th ACM Conference on Recommender systemsRecommender systems suggest to users items that they might like (e.g., news articles, songs, movies) and, in doing so, they help users deal with information overload and enjoy a personalized experience. One of the main problems of these systems is the ...
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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
- SIGecom: Special Interest Group on Economics and Computation
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Association for Computing Machinery
New York, NY, United States
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