Dual-channel representation consistent recommender for session-based new item recommendation

Abstract

Session-Based Recommendations (SBR) have become a crucial branch in the recommendation field. Usually, most previous SBR methods only recommend items existing in users’ historical sessions, facing difficulties with uninteracted items. Introducing new items to users is essential, but solving this issue termed as “Session-based New Item Recommendation (SNIR)” is not an easy task. The complexity arises from accurately characterizing new items and predicting user preferences without user-item interactions. To tackle these challenges, we propose a simple yet effective model named Dual-Channel Representation Consistent Recommender (DC2R). We design a dual-channel strategy to effectively model a new item by its ontology information and inferred representation. Specifically, the ontology information is captured by integrating the inherent attributes of the item into the learning. Meanwhile, leveraging the zero-shot learning technique, we can obtain the inferred item representation from additional attributes. Dual preferences are extracted from the two representations separately to avoid preference bias issues. To better mine user preferences, a Representation Consistent Encoder is utilized to ensure that user preferences and item representations are in the same linear space, thus more precisely reflecting users’ interests. Experiments are conducted on three representative real-world datasets, and the results demonstrate the effectiveness of our proposed method. The source code of our proposed model is available at https://github.com/codeCyw/DC2R.