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Abstract
Exploiting contextual information is considered a good solution to improve the quality of recommendations, aiming at suggesting more relevant items for a specific context. On the other hand, recommender systems research still strive for solving the cold-start problem, namely where not enough information about users and their ratings is available. In this paper we propose a new rating prediction algorithm to face the cold-start system scenario, based on user interests model called contextual conditional preferences. We present results obtained with three publicly available data sets in comparison with several state-of-the-art baselines. We show that usage of contextual conditional preferences improves the prediction accuracy, even when all users have provided a few feedbacks, and hence small amount of data is available.
Citations
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Web of Science
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Authors (4)
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Tommaso Di Noia
- Politecnico di Bari Dipartimento di Ingegneria Elettrica e dell’Informazione
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Paolo Tomeo MSc. Eng.
- Politecnico di Bari Dipartimento di Ingegneria Elettrica e dell’Informazione
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Keywords
Details
- Category:
- Conference activity
- Type:
- materiały konferencyjne indeksowane w Web of Science
- Title of issue:
- Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. - Vol. 1 strony 419 - 424
- Language:
- English
- Publication year:
- 2016
- Bibliographic description:
- Karpus A., Noia T., Tomeo P., Goczyła K..: Rating Prediction with Contextual Conditional Preferences, W: Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. - Vol. 1, 2016, SCITEPRESS,.
- DOI:
- Digital Object Identifier (open in new tab) 10.5220/0006083904190424
- Verified by:
- Gdańsk University of Technology
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