Local Optimality of User Choices and Collaborative Competitive Filtering

TL;DR

This paper introduces Collaborative Competitive Filtering (CCF), a novel framework that models user choice behavior by capturing local competition effects, leading to improved recommendation accuracy over traditional collaborative filtering methods.

Contribution

The paper proposes a new CCF framework that incorporates local competition effects into user preference modeling, enhancing recommendation performance beyond existing CF approaches.

Findings

CCF outperforms standard CF in offline evaluations.

CCF demonstrates significant improvements in online recommendation tasks.

The model effectively captures local competition effects in user choices.

Abstract

While a user's preference is directly reflected in the interactive choice process between her and the recommender, this wealth of information was not fully exploited for learning recommender models. In particular, existing collaborative filtering (CF) approaches take into account only the binary events of user actions but totally disregard the contexts in which users' decisions are made. In this paper, we propose Collaborative Competitive Filtering (CCF), a framework for learning user preferences by modeling the choice process in recommender systems. CCF employs a multiplicative latent factor model to characterize the dyadic utility function. But unlike CF, CCF models the user behavior of choices by encoding a local competition effect. In this way, CCF allows us to leverage dyadic data that was previously lumped together with missing data in existing CF models. We present two formulations and an efficient large scale optimization algorithm. Experiments on three real-world recommendation data sets demonstrate that CCF significantly outperforms standard CF approaches in both offline and online evaluations.