Abstract :
Faced with hundreds of thousands of news articles in the news websites, it is difficult for users to find the news articles they are interested in. Therefore, various news recommender systems were built. In the news recommendation, these news articles read by a user is typically in the form of a time sequence. However, traditional news recommendation algorithms rarely consider the time sequence characteristic of user browsing behaviors. Therefore, the performance of traditional news recommendation algorithms is not good enough in predicting the next news article which a user will read. To solve this problem, this paper proposes a time-ordered collaborative filtering recommendation algorithm (TOCF), which takes the time sequence characteristic of user behaviors into account. Besides, a new method to compute the similarity among different users, named time-dependent similarity, is proposed. To demonstrate the efficiency of our solution, extensive experiments are conducted along with detailed performance analysis.
Keywords :
Web sites; collaborative filtering; recommender systems; TOCF; news Web sites; news recommendation systems; time sequence characteristic; time-dependent similarity; time-ordered collaborative filtering recommendation algorithm; user browsing behaviors; Algorithm design and analysis; Collaboration; Filtering algorithms; Prediction algorithms; Recommender systems; Time measurement; time sequence; time-dependent similarity; time-ordered collaborative filtering;
