Unified Representation Learning for Discrete Attribute Enhanced Completely Cold-Start Recommendation

Abstract

Recommender systems face a daunting challenge when entities (users or items) without any historical interactions, known as the “Completely Cold-Start Problem”. Due to the absence of collaborative signals, Collaborative Filtering (CF) schema fails to deduce user preferences or item characteristics for such cold entities. A common solution is incorporating auxiliary discrete attributes as the bridge to spread collaborative signals to cold entities. Most previous works involve embedding collaborative signals and discrete attributes into different spaces before aligning them for information propagation. Nevertheless, we argue that the separate embedding approach disregards potential high-order similarities between two signals. Furthermore, existing alignment modules typically narrow the geometric-based distance, lacking in-depth exploration of semantic overlap between collaborative signals and cold entities. In this paper, we propose a novel discrete attribute-enhanced completely cold-start recommendation framework, which aims to improve recommendation performance by modeling heterogeneous signals in a unified space. Specifically, we first construct a heterogeneous user-item-attribute graph and capture high-order similarities between heterogeneous signals in a graph-based message-passing manner. To achieve better information alignment, we propose two self-supervised alignment modules from the semantic mutual information and user-item preference perspective. Extensive experiments on six real-world datasets in two types of discrete attribute scenarios consistently verify the effectiveness of our framework.