online evolutionary collaborative filtering
Collaborative filtering algorithms attempt to predict a user's interests based on his past feedback. In real world applications, a user's feedback is often continuously collected over a long period of time. It is very common for a user's interests or an item's popularity to change over a long period of time. Therefore, the underlying recommendation algorithm should be able to adapt to such changes accordingly. However, most existing algorithms do not distinguish current and historical data when predicting the users' current interests. In this paper, we consider a new problem - online evolutionary collaborative filtering , which tracks user interests over time in order to make timely recommendations. We extended the widely used neighborhood based algorithms by incorporating temporal information and developed an incremental algorithm for updating neighborhood similarities with new data. Experiments on two real world datasets demonstrated both improved effectiveness and efficiency of the proposed approach.
collaborative filtering ranking latent class model