Recent Advances in Diversified Recommendation

TL;DR

This paper reviews recent advances in diversified recommendation, emphasizing its importance for user satisfaction, business benefits, and exploring future research directions in the field.

Contribution

It provides a comprehensive taxonomy of diversity definitions, summarizes optimization approaches, and discusses future research trends in diversified recommender systems.

Findings

Diversity enhances user satisfaction and product visibility.

Various definitions and measurement methods for diversity are categorized.

Key future research directions are identified.

Abstract

With the rapid development of recommender systems, accuracy is no longer the only golden criterion for evaluating whether the recommendation results are satisfying or not. In recent years, diversity has gained tremendous attention in recommender systems research, which has been recognized to be an important factor for improving user satisfaction. On the one hand, diversified recommendation helps increase the chance of answering ephemeral user needs. On the other hand, diversifying recommendation results can help the business improve product visibility and explore potential user interests. In this paper, we are going to review the recent advances in diversified recommendation. Specifically, we first review the various definitions of diversity and generate a taxonomy to shed light on how diversity have been modeled or measured in recommender systems. After that, we summarize the major optimization approaches to diversified recommendation from a taxonomic view. Last but not the least, we project into the future and point out trending research directions on this topic.