Diversifying Personalized Recommendation with User-session Context
Diversifying Personalized Recommendation with User-session Context
Liang Hu, Longbing Cao, Shoujin Wang, Guandong Xu, Jian Cao, Zhiping Gu
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 1858-1864.
https://doi.org/10.24963/ijcai.2017/258
Recommender systems (RS) have become an integral part of our daily life. However, most current RS often repeatedly recommend items to users with similar profiles. We argue that recommendation should be diversified by leveraging session contexts with personalized user profiles. For this, current session-based RS (SBRS) often assume a rigidly ordered sequence over data which does not fit in many real-world cases. Moreover, personalization is often omitted in current SBRS. Accordingly, a personalized SBRS over relaxedly ordered user-session contexts is more pragmatic. In doing so, deep-structured models tend to be too complex to serve for online SBRS owing to the large number of users and items. Therefore, we design an efficient SBRS with shallow wide-in-wide-out networks, inspired by the successful experience in modern language modelings. The experiments on a real-world e-commerce dataset show the superiority of our model over the state-of-the-art methods.
Keywords:
Machine Learning: Learning Preferences or Rankings
Knowledge Representation, Reasoning, and Logic: Preference modelling and preference-based reasoning
Machine Learning: Neural Networks
Natural Language Processing: Natural Language Processing