Abstract
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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Notes
- 1.
Each user can have ratings of different items in the test set.
- 2.
We used 40 for k.
- 3.
We used 291 latent factors,1501 iterations, 0.01834 for learning rate and 0.01467 for regularization.
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Acknowledgments
This work was executed within the Immosite.com project, an innovation project co-funded by Flanders Innovation & Entrepreneurship (project nr. HBC.2020.2674) and with involvement from industrial partners Immosite and g-company. The authors also acknowledge support from the Flemish Government (AI Research Program).
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Gharahighehi, A., Nakano, F.K., Vens, C. (2023). An Adaptive Hybrid Active Learning Strategy with Free Ratings in Collaborative Filtering. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2022. Lecture Notes in Networks and Systems, vol 542. Springer, Cham. https://doi.org/10.1007/978-3-031-16072-1_39
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