Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering

TL;DR

This paper introduces ConCF, a novel framework for one-class collaborative filtering that leverages multiple heterogeneous objectives through a multi-branch architecture to improve recommendation accuracy without extra inference costs.

Contribution

ConCF is the first framework to exploit complementarity from heterogeneous objectives via collaborative multi-head training for improved OCCF performance.

Findings

ConCF significantly outperforms baseline models on real-world datasets.

The multi-branch approach enhances model generalization.

Consensus-guided training improves recommendation accuracy.

Abstract

Over the past decades, for One-Class Collaborative Filtering (OCCF), many learning objectives have been researched based on a variety of underlying probabilistic models. From our analysis, we observe that models trained with different OCCF objectives capture distinct aspects of user-item relationships, which in turn produces complementary recommendations. This paper proposes a novel OCCF framework, named ConCF, that exploits the complementarity from heterogeneous objectives throughout the training process, generating a more generalizable model. ConCF constructs a multi-branch variant of a given target model by adding auxiliary heads, each of which is trained with heterogeneous objectives. Then, it generates consensus by consolidating the various views from the heads, and guides the heads based on the consensus. The heads are collaboratively evolved based on their complementarity throughout the training, which again results in generating more accurate consensus iteratively. After training, we convert the multi-branch architecture back to the original target model by removing the auxiliary heads, thus there is no extra inference cost for the deployment. Our extensive experiments on real-world datasets demonstrate that ConCF significantly improves the generalization of the model by exploiting the complementarity from heterogeneous objectives.