13-11-2012, 01:51 PM
Improving Aggregate Recommendati on Diversity Using Ranking- Based
Techniques
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.