A dynamic multi-level collaborative filtering method for improved recommendations

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• 作者：Nikolaos Polatidis ; ^{npolatidis@uom.edu.gr} ; Christos K. Georgiadis ^{geor@uom.edu.gr}
• 关键词：Collaborative filtering ; Similarity ; Dynamic multi-level ; Recommender systems
• 刊名：Computer Standards & Interfaces
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：51
• 期：Complete
• 页码：14-21
• 全文大小：890 K
• 卷排序：51

文摘

We propose a method that improves collaborative filtering recommendations. It may use either a static or a dynamic multi-level approach. It is based on positive and negative adjustments of the users’ similarity values. Both approaches have been experimentally evaluated using three real datasets. Our approaches produce results of better quality when compared to alternatives.