A Fast Recommender System for Cold User Using Categorized Items

[摘要] In recent years, recommender systems (RS) provide a considerable progress to users. RSs reduce the cost of a user’s time in order to reach to desired results faster. The main issue of RSs is the presence of cold users which are less active and their preferences are more difficult to detect. The aim of this study is to provide a new way to improve recall and precision in recommender systems for cold users. According to the available categories of items, prioritization of the proposed items is improved and then presented to the cold user. The obtained results show that in addition to increased speed of processing, recall and precision have an acceptable improvement.

[学科分类] 计算数学

[关键词] recommender systems;collaborative filtering;k-nearest neighbor;cold user