A survey on multi-objective recommender systems
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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Big Data |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fdata.2023.1157899/full |
| _version_ | 1797866067075268608 |
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| author | Dietmar Jannach Himan Abdollahpouri |
| author_facet | Dietmar Jannach Himan Abdollahpouri |
| author_sort | Dietmar Jannach |
| collection | DOAJ |
| description | 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.1 |
| first_indexed | 2024-04-09T23:18:07Z |
| format | Article |
| id | doaj.art-ff5ca8222d2c4e3bbd37f8beaafa95c6 |
| institution | Directory Open Access Journal |
| issn | 2624-909X |
| language | English |
| last_indexed | 2024-04-09T23:18:07Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Big Data |
| spelling | doaj.art-ff5ca8222d2c4e3bbd37f8beaafa95c62023-03-22T05:34:37ZengFrontiers Media S.A.Frontiers in Big Data2624-909X2023-03-01610.3389/fdata.2023.11578991157899A survey on multi-objective recommender systemsDietmar Jannach0Himan Abdollahpouri1Department of Artificial Intelligence and Cybersecurity, University of Klagenfurt, Klagenfurt, AustriaSpotify, Inc., New York, NY, United StatesRecommender 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.1https://www.frontiersin.org/articles/10.3389/fdata.2023.1157899/fullrecommender systemsevaluationmultistakeholder recommendationbeyond-accuracy optimizationshort-term and long-term objectives |
| spellingShingle | Dietmar Jannach Himan Abdollahpouri A survey on multi-objective recommender systems Frontiers in Big Data recommender systems evaluation multistakeholder recommendation beyond-accuracy optimization short-term and long-term objectives |
| title | A survey on multi-objective recommender systems |
| title_full | A survey on multi-objective recommender systems |
| title_fullStr | A survey on multi-objective recommender systems |
| title_full_unstemmed | A survey on multi-objective recommender systems |
| title_short | A survey on multi-objective recommender systems |
| title_sort | survey on multi objective recommender systems |
| topic | recommender systems evaluation multistakeholder recommendation beyond-accuracy optimization short-term and long-term objectives |
| url | https://www.frontiersin.org/articles/10.3389/fdata.2023.1157899/full |
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