Auto-detecting groups based on textual similarity for group recommendations

TL;DR

This paper introduces a method for automatically detecting user groups for group recommendations by analyzing textual similarity in user reviews, enhancing recommendation quality by considering intrinsic textual information.

Contribution

The paper proposes a novel approach that leverages textual similarity of review texts to automatically identify user groups, improving group recommendation effectiveness.

Findings

Improved recommendation accuracy over baseline models

Effective user clustering based on review texts

Validated on real-world datasets

Abstract

In general, recommender systems are designed to provide personalized items to a user. But in few cases, items are recommended for a group, and the challenge is to aggregate the individual user preferences to infer the recommendation to a group. It is also important to consider the similarity of characteristics among the members of a group to generate a better recommendation. Members of an automatically identified group will have similar characteristics, and reaching a consensus with a decision-making process is preferable in this case. It requires users-items and their rating interactions over a utility matrix to auto-detect the groups in group recommendations. We may not overlook other intrinsic information to form a group. The textual information also plays a pivotal role in user clustering. In this paper, we auto-detect the groups based on the textual similarity of the metadata (review texts). We consider the order in user preferences in our models. We have conducted extensive experiments over two real-world datasets to check the efficacy of the proposed models. We have also conducted a competitive comparison with a baseline model to show the improvements in the quality of recommendations.