A model-based approach to user preference discovery in multi-criteria recommender system using genetic programming
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- @Article{Gupta:2022:CCPE,
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author = "Shweta Gupta and Vibhor Kant",
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title = "A model-based approach to user preference discovery in
multi-criteria recommender system using genetic
programming",
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journal = "Concurrency and Computation: Practice and Experience",
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year = "2022",
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volume = "34",
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number = "11",
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pages = "e6899",
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month = "15 " # may,
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keywords = "genetic algorithms, genetic programming, collaborative
filtering, multi-criteria ratings, preference ratings,
recommender system",
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ISSN = "1532-0634",
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URL = "https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.6899",
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DOI = "doi:10.1002/cpe.6899",
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abstract = "Multi-criteria recommender systems (MCRSs) provide
suggestions to users based on their preferences to
various criteria. Incorporation of criteria ratings
into recommendation framework can provide quality
recommendations to users because these ratings can
elicit users preferences efficiently. However,
elicitation of user's overall preference based on
criteria ratings is a key issue in MCRS. Even though
several aggregation methods for the elicitation of
users overall preference have been investigated in the
literature, no method has been shown the superiority
under all circumstances. Therefore, we propose a model
based approach to user preference discovery in
multi-criteria RS using genetic programming (GP). In
this work, we suggest three-stage process to generate
recommendations to users. First, we learn user
preference transformation function to aggregate
criteria ratings by using GP, and then we use the
preference function, so derived, for computing
similarities in MCRS. Finally, items are recommended to
users. Experimental results on Yahoo! Movies dataset
show the superiority of our proposed approach in
comparison to other aggregation approaches.",
- }
Genetic Programming entries for
Shweta Gupta
Vibhor Kant
Citations