Generating Self-Serendipity Preference in Recommender Systems for Addressing Cold Start Problems

TL;DR

This paper introduces GS$^2$-RS, a novel recommender system that generates self-serendipity preferences to improve diversity and relevance in recommendations, effectively addressing cold-start and filter-bubble issues.

Contribution

The paper proposes a new generative model that creates self-serendipity preferences to enhance recommendation diversity and cold-start handling, outperforming existing methods.

Findings

GS$^2$-RS significantly improves serendipity metrics.

The model maintains stable accuracy comparable to state-of-the-art.

Experiments demonstrate effectiveness on benchmark datasets.

Abstract

Classical accuracy-oriented Recommender Systems (RSs) typically face the cold-start problem and the filter-bubble problem when users suffer the familiar, repeated, and even predictable recommendations, making them boring and unsatisfied. To address the above issues, serendipity-oriented RSs are proposed to recommend appealing and valuable items significantly deviating from users' historical interactions and thus satisfying them by introducing unexplored but relevant candidate items to them. In this paper, we devise a novel serendipity-oriented recommender system (\textbf{G}enerative \textbf{S}elf-\textbf{S}erendipity \textbf{R}ecommender \textbf{S}ystem, \textbf{GS$^2$-RS}) that generates users' self-serendipity preferences to enhance the recommendation performance. Specifically, this model extracts users' interest and satisfaction preferences, generates virtual but convincible neighbors' preferences from themselves, and achieves their self-serendipity preference. Then these preferences are injected into the rating matrix as additional information for RS models. Note that GS$^2$-RS can not only tackle the cold-start problem but also provides diverse but relevant recommendations to relieve the filter-bubble problem. Extensive experiments on benchmark datasets illustrate that the proposed GS$^2$-RS model can significantly outperform the state-of-the-art baseline approaches in serendipity measures with a stable accuracy performance.