Independent Representation of Side Information for Sequential Recommendation

Abstract

Sequential recommendation systems aim to predict users' subsequent interactions by analyzing their historical behavior. Significant advancements have been achieved in this field through the utilization of models such as RNN, GRU, and models incorporating the self-attention mechanism. Most state-of-the-art methods enhance recommendation effectiveness by incorporating side information, such as category or brand, within the attention layer to enrich item representations. However, in our pilot experiments, we found that increasing the amount of side information could negatively impact the recommendation performance when integrated directly into the attention layer due to the learning of interdependency among them. To alleviate this problem, we propose Independent Representation of side Information on each item for Sequential recommendation (IRIS). IRIS learns pairs of both item and side information independently, without any fusion among side information in attention layer. In contrast to existing approaches, IRIS avoids direct fusion among side information in the attention layer, opting instead for a novel strategy of learning independent pairs of both item and side information. Our proposed method constructs an attention layer specifically designed to learn these pairs, thus avoiding potential distortions to item representations caused by intermixing side information. Experiments on four public datasets show that IRIS outperforms state-of-the-art sequential recommendation models. Ablation study also indicates robust scalability of side information within IRIS.