An Adaptive Hybrid Active Learning Strategy with Free Ratings in Collaborative Filtering Article Swipe
Alireza Gharahighehi
,
Felipe Kenji Nakano
,
Celine Vens
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2203.05954
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2203.05954
Recommender systems are information retrieval methods that predict user preferences to personalize services. These systems use the feedback and the ratings provided by users to model the behavior of users and to generate recommendations. Typically, the ratings are quite sparse, i.e., only a small fraction of items are rated by each user. To address this issue and enhance the performance, active learning strategies can be used to select the most informative items to be rated. This rating elicitation procedure enriches the interaction matrix with informative ratings and therefore assists the recommender system to better model the preferences of the users. In this paper, we evaluate various non-personalized and personalized rating elicitation strategies. We also propose a hybrid strategy that adaptively combines a non-personalized and a personalized strategy. Furthermore, we propose a new procedure to obtain free ratings based on the side information of the items. We evaluate these ideas on the MovieLens dataset. The experiments reveal that our proposed hybrid strategy outperforms the strategies from the literature. We also propose the extent to which free ratings are obtained, improving further the performance and also the user experience.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2203.05954
- https://arxiv.org/pdf/2203.05954
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4221139755
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4221139755Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2203.05954Digital Object Identifier
- Title
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An Adaptive Hybrid Active Learning Strategy with Free Ratings in Collaborative FilteringWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-03-11Full publication date if available
- Authors
-
Alireza Gharahighehi, Felipe Kenji Nakano, Celine VensList of authors in order
- Landing page
-
https://arxiv.org/abs/2203.05954Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2203.05954Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2203.05954Direct OA link when available
- Concepts
-
MovieLens, Collaborative filtering, Computer science, Recommender system, Machine learning, Artificial intelligence, Information retrievalTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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