Learning Complex Users' Preferences for Recommender Systems

TL;DR

This paper introduces a personality-based recommender system to address data sparsity issues in general RSs by leveraging users' personality traits to identify similar users, enhancing recommendation quality.

Contribution

It proposes a novel personality-based approach to improve general recommender systems, specifically tackling the data sparsity problem caused by lack of common items.

Findings

Improved recommendation accuracy with personality-based user similarity

Effective handling of data sparsity in cold-start scenarios

Enhanced user satisfaction through personalized recommendations

Abstract

Recommender systems (RSs) have emerged as very useful tools to help customers with their decision-making process, find items of their interest, and alleviate the information overload problem. There are two different lines of approaches in RSs: (1) general recommenders with the main goal of discovering long-term users' preferences, and (2) sequential recommenders with the main focus of capturing short-term users' preferences in a session of user-item interaction (here, a session refers to a record of purchasing multiple items in one shopping event). While considering short-term users' preferences may satisfy their current needs and interests, long-term users' preferences provide users with the items that they may interact with, eventually. In this thesis, we first focus on improving the performance of general RSs. Most of the existing general RSs tend to exploit the users' rating patterns on common items to detect similar users. The data sparsity problem (i.e. the lack of available information) is one of the major challenges for the current general RSs, and they may fail to have any recommendations when there are no common items of interest among users. We call this problem data sparsity with no feedback on common items (DSW-n-FCI). To overcome this problem, we propose a personality-based RS in which similar users are identified based on the similarity of their personality traits.