Background knowledge for recommenders
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The document proposes an approach called DaVI that treats additional dimensions like time as virtual items to improve recommender systems. DaVI can be applied to item-based collaborative filtering techniques and association rules techniques. The results show DaVI improved accuracy of recommendations for playlists by up to 34% for collaborative filtering and 14.5% for association rules.
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Background knowledge for recommenders
- 1. Explointing Background Knowledge in recommender systems: the DaVI approach Marcos A. Domingues, Alípio M. Jorge, Carlos Soares marcos@liaad.up.pt, amjorge@fc.up.pt, csoares@fep.up.pt
- 4. Recommender Systems Item-base collaborative filtering technique Association rules technique
- 5. DaVI on Item-Based Collaborative Filtering Technique
- 7. Results - CF Listener Playlist 34% 24%
- 8. Results - AR Listener Playlist 14.5% 23,5%