A Time-Sensitive Collaborative Filtering Model in Recommendation Systems
Limei Sun, Ejike Ifeanyi Michael, Shen Wang, Yue Li · 2016
Collaborative filtering is one of the most widely-used algorithms in recommendation systems. In user-based collaborative filtering algorithm, current users' nearest neighbors are used to recommend items because they have similar preference, but users' preference varies with time, which often affects the accuracy of the recommendation. As a result of the varying users' preference, many researches about recommendation systems are focusing on the time factor, to find a way to make up for the change in preferences of users. The existing time-related algorithms usually add time factor in the training phase and make this procedure more complicated. To catch the newest preference of the users and improve the accuracy of the recommendation without complicating the training phase, a timesensitive collaborative filtering model is proposed in this paper, which keeps the original training phase and make some changes in the prediction phase. During the recommendation process, the proposed model orders the items by time for each user as a sequence. The sequence is called time-behavior sequence. First it finds the last item from current user's time-behavior sequence which represents the newest preference of the current user. Secondly, it locates the item in nearest neighbors' timebehavior sequence and saves the timestamp of the item. Lastly, it recommends the items whose timestamps are greater than the saved timestamp from the nearest neighbors' time-behavior sequence. Experiments on the MovieLens dataset show that the proposed time-sensitive collaborative filtering model gives better recommendation quality than the traditional user-based collaborative filtering recommendation algorithm. It can catch the newest preferences of the users and increase the accuracy of recommendation, without changing the training phase.