ILCR: Item-based Latent Factors for Sparse Collaborative Retrieval

TL;DR

This paper introduces ILCR, an item-based latent factor model for sparse collaborative retrieval that improves ranking accuracy by integrating item-based information and employing scalable BPR optimization, validated on real-world datasets.

Contribution

The paper proposes a novel item-based latent factor model for collaborative retrieval that better handles sparsity and employs BPR for improved optimization.

Findings

Outperforms existing models on Last.fm and Yelp datasets.

Effectively handles sparse query-user-item interactions.

Demonstrates scalability and efficiency in real-world scenarios.

Abstract

Interactions between search and recommendation have recently attracted significant attention, and several studies have shown that many potential applications involve with a joint problem of producing recommendations to users with respect to a given query, termed $Collaborative$ $Retrieval$ (CR). Successful algorithms designed for CR should be potentially flexible at dealing with the sparsity challenges since the setup of collaborative retrieval associates with a given $query$ $\times$ $user$ $\times$ $item$ tensor instead of traditional $user$ $\times$ $item$ matrix. Recently, several works are proposed to study CR task from users' perspective. In this paper, we aim to sufficiently explore the sophisticated relationship of each $query$ $\times$ $user$ $\times$ $item$ triple from items' perspective. By integrating item-based collaborative information for this joint task, we present an alternative factorized model that could better evaluate the ranks of those items with sparse information for the given query-user pair. In addition, we suggest to employ a recently proposed scalable ranking learning algorithm, namely BPR, to optimize the state-of-the-art approach, $Latent$ $Collaborative$ $Retrieval$ model, instead of the original learning algorithm. The experimental results on two real-world datasets, (i.e. \emph{Last.fm}, \emph{Yelp}), demonstrate the efficiency and effectiveness of our proposed approach.