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. 2023 Mar 22:6:1157899.
doi: 10.3389/fdata.2023.1157899. eCollection 2023.

A survey on multi-objective recommender systems

Dietmar Jannach  1 , Himan Abdollahpouri  2
Affiliations

A survey on multi-objective recommender systems

Dietmar Jannach et al. Front Big Data. .

Abstract

Recommender systems can be characterized as software solutions that provide users with convenient access to relevant content. Traditionally, recommender systems research predominantly focuses on developing machine learning algorithms that aim to predict which content is relevant for individual users. In real-world applications, however, optimizing the accuracy of such relevance predictions as a single objective in many cases is not sufficient. Instead, multiple and often competing objectives, e.g., long-term vs. short-term goals, have to be considered, leading to a need for more research in multi-objective recommender systems. We can differentiate between several types of such competing goals, including (i) competing recommendation quality objectives at the individual and aggregate level, (ii) competing objectives of different involved stakeholders, (iii) long-term vs. short-term objectives, (iv) objectives at the user interface level, and (v) engineering related objectives. In this paper, we review these types of multi-objective recommendation settings and outline open challenges in this area.

Keywords: beyond-accuracy optimization; evaluation; multistakeholder recommendation; recommender systems; short-term and long-term objectives.

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Conflict of interest statement

HA is employed by Spotify Inc. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Taxonomy of different types of multi-objective recommendation settings.
See this image and copyright information in PMC

References

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