GACOforRec: Session-Based Graph Convolutional Neural Networks Recommendation Model
Mingge Zhang, Zhenyu Yang · IEEE Access · 2019
The biggest challenge to recommendation systems based on user preferences is how to improve the ability of the recommendation system to mine and analyse user preferences and behaviours. In this process, we must not only consider the continuation of the user's long-term preference but also improve the system's ability to accommodate short-term preferences and discrete preferences. To this end, we focus on the performance of time factors of user preferences. However, the issue we are concerned about has not received much attention in the existing research. We propose a new recommendation model based on the perspective of user sessions, namely GACOforRec. This model can handle long-term and stable preferences at the same time and preserve the hierarchy of potential preferences. We conducted a large number of comparative experiments on two real datasets, and the results show that GACOforRec is significantly better than other state-of-the-art methods in the study of user sessions.