A collaborative filtering method for interactive platforms

Interactive platforms such as Last.fm and Steam are currently playing an increasingly important role in ecommerce. The most important feature in an interactive platform is streaming data, which contain an enormous amount of information regarding a user's interests at any time. However, previous recommender systems have been unable to deal with streaming data well. Therefore, we propose a collaborative filtering approach that uses the sliding window technique. Furthermore, we found that sliding only on interaction time results in a better performance. Moreover, we propose a method called equal ratio filling to handle suboptimal streaming data and other optimization strategies. Finally, we evaluated our approach using the stream dataset. As the results indicate, our approach performs better than other conventional approaches.