Ranking Approaches Based On Improving Aggregate Recommendation Diversity Algorithms
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2015, Vol 3, Issue 6
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 has focused on improving recommendation accuracy, other important aspects of recommending quality, such as the diversity of recommendations, have often been overlooked. In this paper, we introduce and explore a number of item ranking approaches that can generate recommendations that have substantially higher aggregate diversity across all users while maintaining comparable levels of recommendation accuracy. Comprehensive empirical evaluation consistently shows the diversity gains of the proposed approaches using several real-world rating data sets and different rating prediction algorithms.
Authors and Affiliations
L. Pravin Kumar, K. Ramesh
Keywords
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