TF: A Novel Filtering Approach to Find Temporal Frequent Itemsets in Recommender Systems
Sijie Wei, Kan Li · 2017
In recent years, information overload has become a serious problem. There are many recommender system algorithms which help people make decisions about what they want. However, many traditional recommender system algorithms ignore temporal information. In order to utilize temporal information, we propose a new method to find Temporal Frequent Itemsets and improve traditional recommender system algorithms. Our method can combine well with other algorithms. In addition, our method is tend to recommend newly-risen items and avoid to recommend out-of-date items for users. We use our method in two real-world datasets. The results show that the performance of our algorithm is more excellent than the performance of state-of-the-art algorithms.