Time context unifying collaborative filtering
Haoyuan Huo, Ping Zhu, Hua Zhang · 2016
With the advanced information technology and rapidly changed information technology, recommend technique quickly becomes a hot topic and is widely used in social networking and business website. Collaborative filtering (CF) is the most important recommend technique. Towards the problem of personalized recommendation, this paper proposed a collaborative filtering algorithm based on context similarity for timeliness by incorporating time context information into collaborative filtering recommendation process. Furthermore, this paper use a linear ensemble to associate the user-based and item-based models so that the four elements (user and user's neighbors, selected item and its adjacent items in database) have inter-influence on one another and thus improve the effectiveness and accuracy of our algorithm. Experimental results indicate that the proposed algorithm has increased the accuracy of recommendation system which means our model is suitable for personalized time context recommendation system.