Attenuated and normalized item-item product network for sequential recommendation

PeerJ Computer Science

Youfang Lin¹  Weiqiang Di¹  Zhihao Wu¹ 

[1] School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China;

关键词: Sequential recommendation;    Recommendation;    Item co-occurrence;    Item-item product;   

DOI  :  10.7717/peerj-cs.867

来源: DOAJ

【 摘 要 】

Sequential recommendation has become a research trending that exploits user’s recent behaviors for recommendation. The user-item interactions contain a sequential dependency that we need to capture to better recommend. Item-item Product (IIP), which models item co-occurrence, has shown good potential by characterizing the pairwise item relationships. Generally, recent behaviors have a greater impact on the current than long-term historical behaviors. And the decaying rate of influence around infrequent behaviors is fast. However, IIP ignores such a phenomenon when considering item-item relevance and leads to suboptimal performance. In this paper, we propose an attenuated IIP mechanism which is position-aware and decays the influence of historical items at an exponential rate. Besides, In order to make up for scenarios where the influence is not in a monotonous decline trend, we add another normalized IIP mechanism to complement the attenuated IIP mechanism. It also strengthen the model’s ability in discriminating favorite items under the sparse data condition by enlarging the gap of matching degree between items. Experiments conducted on five real-world datasets demonstrate that our proposed model achieves better performance than a set of state-of-the-art sequential recommendation models.

【 授权许可】

Unknown