Why Not Together? A Multiple-Round Recommender System for Queries and Items

TL;DR

This paper introduces MAGUS, a recursive multi-round recommender system that leverages both queries and items to improve recommendation accuracy and user engagement across multiple interaction rounds.

Contribution

The paper presents MAGUS, a novel recursive framework that integrates query and item information for enhanced multi-round recommendation performance.

Findings

MAGUS improves recommendation efficiency across 12 methods.

Integrating queries with items enhances user preference identification.

The recursive framework is adaptable to various recommendation algorithms.

Abstract

A fundamental technique of recommender systems involves modeling user preferences, where queries and items are widely used as symbolic representations of user interests. Queries delineate user needs at an abstract level, providing a high-level description, whereas items operate on a more specific and concrete level, representing the granular facets of user preference. While practical, both query and item recommendations encounter the challenge of sparse user feedback. To this end, we propose a novel approach named Multiple-round Auto Guess-and-Update System (MAGUS) that capitalizes on the synergies between both types, allowing us to leverage both query and item information to form user interests. This integrated system introduces a recursive framework that could be applied to any recommendation method to exploit queries and items in historical interactions and to provide recommendations for both queries and items in each interaction round. Empirical results from testing 12 different recommendation methods demonstrate that integrating queries into item recommendations via MAGUS significantly enhances the efficiency, with which users can identify their preferred items during multiple-round interactions.