Similar or Related: Spectral-Based Item Relationship Mining With Graph Convolutional Network for Complementary Recommendation

Abstract

Complementary recommendation, which aims to recommend frequently copurchased items to users, has gained significant attention. Unlike traditional similarity-based recommendations, complementary recommendation focus on items that are related but not necessarily similar (e.g., computers and keyboards), that aligns with users’ purchasing habits. However, most of current complementary recommendation systems fail to effectively differentiate or measure these two types of relationships. In this article, we propose similar or related: spectral-based item relationship mining with graph convolutional network for complementary recommendation (SR-Rec). First, we design two spectral-based filters to fully mine the similarity and relevance information of items, thereby achieving effective discrimination between the two types of relationships. Then, we compute similarity and relevance scores between items separately, and employ a pairwise self-attention mechanism to measure the impact of these relationships on the final recommendations. Experimental results on three public open-source datasets demonstrate that SR-Rec outperforms state-of-the-art performance in complementary recommendation.