A systematic literature review of multicriteria recommender systems
| Autor: | Diego Monti, Maurizio Morisio, Giuseppe Rizzo |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Protocol (science)
Linguistics and Language Information retrieval Computer science Systematic literature review 02 engineering and technology Recommender system Language and Linguistics Field (computer science) Multicriteria recommendation Systematic review Survey Artificial Intelligence 020204 information systems 0202 electrical engineering electronic engineering information engineering Multiple criteria 020201 artificial intelligence & image processing Relevance (information retrieval) Research questions |
| Popis: | Since the first years of the 90s, recommender systems have emerged as effective tools for automatically selecting items according to user preferences. Traditional recommenders rely on the relevance assessments that users express using a single rating for each item. However, some authors started to suggest that this approach could be limited, as we naturally tend to formulate different judgments according to multiple criteria. During the last decade, several studies introduced novel recommender systems capable of exploiting user preferences expressed over multiple criteria. This work proposes a systematic literature review in the field of multicriteria recommender systems. Following a replicable protocol, we selected a total number of 93 studies dealing with this topic. We subsequently analyzed them to provide an answer to five different research questions. We considered what are the most common research problems, recommendation approaches, data mining and machine learning algorithms mentioned in these studies. Furthermore, we investigated the domains of application, the exploited evaluation protocols, metrics and datasets, and the most promising suggestions for future works. |
| Databáze: | OpenAIRE |
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