Coupled collaborative filtering for context-aware recommendation

Publication Type:
Conference Proceeding
Citation:
Proceedings of the National Conference on Artificial Intelligence, 2015, 6 pp. 4172 - 4173
Issue Date:
2015-06-01
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail9272-44898-1-PB.pdf Published version756.5 kB
Adobe PDF
Context-aware features have been widely recognized as important factors in recommender systems. However, as a major technique in recommender systems, traditional Collaborative Filtering (CF) does not provide a straightforward way of integrating the context-aware information into personal recommendation. We propose a Coupled Collaborative Filtering (CCF) model to measure the contextual information and use it to improve recommendations. In the proposed approach, coupled similarity computation is designed to be calculated by inter-item, intra-context and inter-context interactions among item, user and context-ware factors. Experiments based on different types of CF models demonstrate the effectiveness of our design.
Please use this identifier to cite or link to this item: