%0 Journal Article %1 Adomavicius:2012:IAR:2197072.2197127 %A Adomavicius, Gediminas %A Kwon, YoungOk %C Piscataway, NJ, USA %D 2012 %I IEEE Educational Activities Department %J IEEE Trans. on Knowl. and Data Eng. %K diversity recommender %N 5 %P 896--911 %R 10.1109/TKDE.2011.15 %T Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques %U http://dx.doi.org/10.1109/TKDE.2011.15 %V 24 %X Recommender systems are becoming increasingly important to individual users and businesses for providing personalized recommendations. However, while the majority of algorithms proposed in recommender systems literature have focused on improving recommendation accuracy (as exemplified by the recent Netflix Prize competition), other important aspects of recommendation quality, such as the diversity of recommendations, have often been overlooked. In this paper, we introduce and explore a number of item ranking techniques that can generate substantially more diverse recommendations across all users while maintaining comparable levels of recommendation accuracy. Comprehensive empirical evaluation consistently shows the diversity gains of the proposed techniques using several real-world rating data sets and different rating prediction algorithms.

@article{Adomavicius:2012:IAR:2197072.2197127, abstract = {Recommender systems are becoming increasingly important to individual users and businesses for providing personalized recommendations. However, while the majority of algorithms proposed in recommender systems literature have focused on improving recommendation accuracy (as exemplified by the recent Netflix Prize competition), other important aspects of recommendation quality, such as the diversity of recommendations, have often been overlooked. In this paper, we introduce and explore a number of item ranking techniques that can generate substantially more diverse recommendations across all users while maintaining comparable levels of recommendation accuracy. Comprehensive empirical evaluation consistently shows the diversity gains of the proposed techniques using several real-world rating data sets and different rating prediction algorithms.}, acmid = {2197127}, added-at = {2019-02-19T16:18:13.000+0100}, address = {Piscataway, NJ, USA}, author = {Adomavicius, Gediminas and Kwon, YoungOk}, biburl = {https://www.bibsonomy.org/bibtex/28f7216285a53caed35c83b5a035ac52a/brusilovsky}, description = {Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques}, doi = {10.1109/TKDE.2011.15}, interhash = {0ad3a9d435c68c1cb8f2c4e9501fbf37}, intrahash = {8f7216285a53caed35c83b5a035ac52a}, issn = {1041-4347}, issue_date = {May 2012}, journal = {IEEE Trans. on Knowl. and Data Eng.}, keywords = {diversity recommender}, month = may, number = 5, numpages = {16}, pages = {896--911}, publisher = {IEEE Educational Activities Department}, timestamp = {2019-02-19T16:18:13.000+0100}, title = {Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques}, url = {http://dx.doi.org/10.1109/TKDE.2011.15}, volume = 24, year = 2012 }