Towards Popularity-Aware Recommendation: A Multi-Behavior Enhanced Framework with Orthogonality Constraint

TL;DR

This paper introduces PopSI, a popularity-aware recommendation framework that leverages multi-behavior side information and an orthogonality constraint to improve accuracy and reduce popularity bias in top-K recommendations.

Contribution

The paper proposes a novel multi-behavior tensor-based recommendation model with an orthogonality constraint to address popularity bias and improve recommendation performance.

Findings

PopSI outperforms state-of-the-art debias methods on real-world datasets.

PopSI achieves a good balance between accuracy and debiasing.

PopSI demonstrates scalability and effectiveness in practical applications.

Abstract

Top-$K$ recommendation involves inferring latent user preferences and generating personalized recommendations accordingly, which is now ubiquitous in various decision systems. Nonetheless, recommender systems usually suffer from severe \textit{popularity bias}, leading to the over-recommendation of popular items. Such a bias deviates from the central aim of reflecting user preference faithfully, compromising both customer satisfaction and retailer profits. Despite the prevalence, existing methods tackling popularity bias still have limitations due to the considerable accuracy-debias tradeoff and the sensitivity to extensive parameter selection, further exacerbated by the extreme sparsity in positive user-item interactions. In this paper, we present a \textbf{Pop}ularity-aware top-$K$ recommendation algorithm integrating multi-behavior \textbf{S}ide \textbf{I}nformation (PopSI), aiming to enhance recommendation accuracy and debias performance simultaneously. Specifically, by leveraging multiple user feedback that mirrors similar user preferences and formulating it as a three-dimensional tensor, PopSI can utilize all slices to capture the desiring user preferences effectively. Subsequently, we introduced a novel orthogonality constraint to refine the estimated item feature space, enforcing it to be invariant to item popularity features thereby addressing our model's sensitivity to popularity bias. Comprehensive experiments on real-world e-commerce datasets demonstrate the general improvements of PopSI over state-of-the-art debias methods with a marginal accuracy-debias tradeoff and scalability to practical applications. The source code for our algorithm and experiments is available at \url{https://github.com/Eason-sys/PopSI}.