Combining Privileged Information to Improve Context-Aware Recommender Systems

TL;DR

This paper introduces a method to enhance context-aware recommender systems by integrating privileged information from topic hierarchies, constructed using an extended clustering method that combines traditional and privileged data, leading to improved recommendations.

Contribution

It proposes a novel approach to incorporate privileged information into topic hierarchies for better context-aware recommendations, addressing the challenge of automatic contextual information acquisition.

Findings

Privileged information improves recommendation accuracy.

Combining domain terms and named entities yields better results.

Topic hierarchies enhance context understanding in recommender systems.

Abstract

A recommender system is an information filtering technology which can be used to predict preference ratings of items (products, services, movies, etc) and/or to output a ranking of items that are likely to be of interest to the user. Context-aware recommender systems (CARS) learn and predict the tastes and preferences of users by incorporating available contextual information in the recommendation process. One of the major challenges in context-aware recommender systems research is the lack of automatic methods to obtain contextual information for these systems. Considering this scenario, in this paper, we propose to use contextual information from topic hierarchies of the items (web pages) to improve the performance of context-aware recommender systems. The topic hierarchies are constructed by an extension of the LUPI-based Incremental Hierarchical Clustering method that considers three types of information: traditional bag-of-words (technical information), and the combination of named entities (privileged information I) with domain terms (privileged information II). We evaluated the contextual information in four context-aware recommender systems. Different weights were assigned to each type of information. The empirical results demonstrated that topic hierarchies with the combination of the two kinds of privileged information can provide better recommendations.