MIC: Model-agnostic Integrated Cross-channel Recommenders

TL;DR

MIC introduces a model-agnostic approach that leverages multi-channel mutual information to improve large-scale recommendation systems by modeling diverse user-item and user-user/item-item correlations.

Contribution

It proposes a universal, integrated cross-channel method that enhances recommendation accuracy by capturing latent interactions across multiple channels without architectural constraints.

Findings

MIC boosts performance of state-of-the-art models on real-world benchmarks.

Deployment demonstrates MIC's scalability and flexibility in industrial settings.

Extensive experiments confirm MIC's effectiveness in large-scale recommendation tasks.

Abstract

Semantically connecting users and items is a fundamental problem for the matching stage of an industrial recommender system. Recent advances in this topic are based on multi-channel retrieval to efficiently measure users' interest on items from the massive candidate pool. However, existing work are primarily built upon pre-defined retrieval channels, including User-CF (U2U), Item-CF (I2I), and Embedding-based Retrieval (U2I), thus access to the limited correlation between users and items which solely entail from partial information of latent interactions. In this paper, we propose a model-agnostic integrated cross-channel (MIC) approach for the large-scale recommendation, which maximally leverages the inherent multi-channel mutual information to enhance the matching performance. Specifically, MIC robustly models correlation within user-item, user-user, and item-item from latent interactions in a universal schema. For each channel, MIC naturally aligns pairs with semantic similarity and distinguishes them otherwise with more uniform anisotropic representation space. While state-of-the-art methods require specific architectural design, MIC intuitively considers them as a whole by enabling the complete information flow among users and items. Thus MIC can be easily plugged into other retrieval recommender systems. Extensive experiments show that our MIC helps several state-of-the-art models boost their performance on two real-world benchmarks. The satisfactory deployment of the proposed MIC on industrial online services empirically proves its scalability and flexibility.