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Authors: Aleksandra Karpus 1 ; Tommaso Di Noia 2 ; Paolo Tomeo 2 and Krzysztof Goczyla 1

Affiliations: 1 Gdańsk University of Technology, Poland ; 2 Polytechnic University of Bari, Italy

ISBN: 978-989-758-203-5

Keyword(s): Recommender Systems, Context Awareness, Conditional Preferences, Rating Prediction, Cold-start Problem.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Collaborative Filtering ; Context Discovery ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Symbolic Systems ; User Profiling and Recommender Systems

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.

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Paper citation in several formats:
Karpus, A.; Di Noia, T.; Tomeo, P. and Goczyla, K. (2016). Rating Prediction with Contextual Conditional Preferences.In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016) ISBN 978-989-758-203-5, pages 419-424. DOI: 10.5220/0006083904190424

@conference{kdir16,
author={Aleksandra Karpus. and Tommaso Di Noia. and Paolo Tomeo. and Krzysztof Goczyla.},
title={Rating Prediction with Contextual Conditional Preferences},
booktitle={Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)},
year={2016},
pages={419-424},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006083904190424},
isbn={978-989-758-203-5},
}

TY - CONF

JO - Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)
TI - Rating Prediction with Contextual Conditional Preferences
SN - 978-989-758-203-5
AU - Karpus, A.
AU - Di Noia, T.
AU - Tomeo, P.
AU - Goczyla, K.
PY - 2016
SP - 419
EP - 424
DO - 10.5220/0006083904190424

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