Novel Recommendation of User-Based Collaborative Filtering

作者：Liang Zhang, Peng Li, C. A. Phelan · 发表于：Journal of Digital Information Management · 年份：2014 · 被引用次数：5 · 研究领域：Recommender Systems and Techniques、Customer churn and segmentation、Digital Marketing and Social Media

Recommendation system has been widely used in various types of e-commerce sites. One of the most successful examples is the collaborative filtering algorithm. However, the traditional algorithms only aim at accuracy and ignore these factors closely related with customer satisfaction, such as novelty etc. In this paper, we defined novelty of item from the perspective of the users, designed the corresponding offline experiment scheme and evaluation metrics. The dissimilarity and the time-popularity were embedded in the traditional collaborative filtering algorithm, the ability of predicting user's future needs and coverage of recommended list were obviously improved, and the ability of recommended long tail items were also enhanced. Subject Categories and Descriptors H.2.8 (Database Applications) Data Mining; H.5.3 (Group and Organization Interfaces) Collaborative Computing