Online Personalized Recommendation Based On Streaming Implicit User Feedback

Since user preference is drifting over time, modeling temporary dynamic recommender system has been proven to be valuable for accurate recommendation performance. However, user feedback is continuously updating while the traditional recommender system is trained off-line in batch mode so that it cant capture user taste change in time. In this paper, we build a dynamic real-time recommendation model based on implicit user feedback stream to improve both the recommendation accuracy and training efficiency. Moreover, our model has obvious advantages over the traditional approaches in diversity, interpretability, and strong robustness to hostile attack. Finally, we conduct experiments on two real world datasets to validate the effectiveness of our proposed method and demonstrate the superior performance when compared with state-of-the-art approach.