Abstract
The majority of algorithms proposed in recommender systems literature has focused on improving recommendation accuracy, other important aspects of recommendation quality, the diversity of recommendations, have often been overlooked. Number of item ranking techniques that can generatesubstantially 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. We present an unsupervised, online record matching method, UDD, which can effectively identify duplicates from the query result
records of multiple Web databases
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