Abstract
Collaborative Filtering Algorithm is one of the most successful recommender technologies, and has been widely used in E-commerce. However, traditional Collaborative Filtering often focus on user-item ratings, but ignore the information implicated in user activity which means how and how often a user makes operations in a system, so it misses some important information to improve the prediction quality. To solve this problem, we bring user activity factor into collaborative filtering and propose a new collaborative filtering algorithm based on user activity level (UACF). Finally, experiments have shown that our new algorithm UACF improves the precision of traditional collaborative filtering.
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