Rethinking Rule-Based Approaches in Session-Based Recommendation

Existing approaches to session-based recommendation are focused on using advanced deep neural networks (DNNs). Recent studies have shown that some traditional methods can outperform some DNN-based models. However, in recent years, few studies have tried to build traditional models. In this paper, we investigate this issue and propose a concise rule-based method for session-based recommendation. Specifically, we make item adjacent and N-gram-based rules to extract frequenting, sequential and other patterns of the limited historical information to construct item correlation dictionaries. Then, we exploit these dictionaries at the inference stage by constructing candidate item generation and item set fusion rules to acquire candidate items. By this means, we can leverage short-term user-item interaction records to generate candidate items for anonymous sessions. Finally, we sort the candidate items to make a recommendation. Extensive experimental results on three real public datasets show that our method can significantly outperform existing traditional methods and even outperforms several representative DNN-based models. The model just costs a few seconds to train and inference, and occupies less memory space than that. Compared with the previous models, it has a huge lead in time and space.