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article

Distributional semantic pre-filtering in context-aware recommender systems

Authors:

Francesco Ricci,

Luigi CeccaroniAuthors Info & Claims

User Modeling and User-Adapted Interaction, Volume 26, Issue 1

Pages 1 - 32

https://doi.org/10.1007/s11257-015-9158-2

Published: 01 March 2016 Publication History

Abstract

Context-aware recommender systems improve context-free recommenders by exploiting the knowledge of the contextual situation under which a user experienced and rated an item. They use data sets of contextually-tagged ratings to predict how the target user would evaluate (rate) an item in a given contextual situation, with the ultimate goal to recommend the items with the best estimated ratings. This paper describes and evaluates a pre-filtering approach to context-aware recommendation, called distributional-semantics pre-filtering (DSPF), which exploits in a novel way the distributional semantics of contextual conditions to build more precise context-aware rating prediction models. In DSPF, given a target contextual situation (of a target user), a matrix-factorization predictive model is built by using the ratings tagged with the contextual situations most similar to the target one. Then, this model is used to compute rating predictions and identify recommendations for that specific target contextual situation. In the proposed approach, the definition of the similarity of contextual situations is based on the distributional semantics of their composing conditions: situations are similar if they influence the user's ratings in a similar way. This notion of similarity has the advantage of being directly derived from the rating data; hence it does not require a context taxonomy. We analyze the effectiveness of DSPF varying the specific method used to compute the situation-to-situation similarity. We also show how DSPF can be further improved by using clustering techniques. Finally, we evaluate DSPF on several contextually-tagged data sets and demonstrate that it outperforms state-of-the-art context-aware approaches.

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Cited By

Li HChen JZhao JYao LZhang RYang LLu X(2024)CTITFInformation Sciences: an International Journal10.1016/j.ins.2024.120838676:COnline publication date: 1-Aug-2024
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Musto CSpillo GSemeraro G(2024)Harnessing distributional semantics to build context-aware justifications for recommender systemsUser Modeling and User-Adapted Interaction10.1007/s11257-023-09382-x34:3(659-690)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s11257-023-09382-x
Zhong JNegre E(2022)Towards Better Representation of Context Into Recommender SystemsInternational Journal of Knowledge-Based Organizations10.4018/IJKBO.29508012:2(1-12)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.4018/IJKBO.295080
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cover image User Modeling and User-Adapted Interaction

User Modeling and User-Adapted Interaction Volume 26, Issue 1

March 2016

101 pages

ISSN:0924-1868

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Copyright © Copyright © 2016 Springer Science+Business Media Dordrecht.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 March 2016

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Cited By

Li HChen JZhao JYao LZhang RYang LLu X(2024)CTITFInformation Sciences: an International Journal10.1016/j.ins.2024.120838676:COnline publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1016/j.ins.2024.120838
Musto CSpillo GSemeraro G(2024)Harnessing distributional semantics to build context-aware justifications for recommender systemsUser Modeling and User-Adapted Interaction10.1007/s11257-023-09382-x34:3(659-690)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s11257-023-09382-x
Zhong JNegre E(2022)Towards Better Representation of Context Into Recommender SystemsInternational Journal of Knowledge-Based Organizations10.4018/IJKBO.29508012:2(1-12)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.4018/IJKBO.295080
Zheng YFlorez Arias GBellogin ABoratto LSantos OArdissono LKnijnenburg B(2022)A Family of Neural Contextual Matrix Factorization Models for Context-Aware RecommendationsAdjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3511047.3536404(1-6)Online publication date: 4-Jul-2022
https://dl.acm.org/doi/10.1145/3511047.3536404
Zheng Y(2022)Non-dominated differential context modeling for context-aware recommendationsApplied Intelligence10.1007/s10489-021-03027-552:9(10008-10021)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1007/s10489-021-03027-5
Singh M(2020)Scalability and sparsity issues in recommender datasets: a surveyKnowledge and Information Systems10.1007/s10115-018-1254-262:1(1-43)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1007/s10115-018-1254-2
Zheng YJose AHung CPapadopoulos G(2019)Context-aware recommendations via sequential predictionsProceedings of the 34th ACM/SIGAPP Symposium on Applied Computing10.1145/3297280.3297639(2525-2528)Online publication date: 8-Apr-2019
https://dl.acm.org/doi/10.1145/3297280.3297639
Vahidi Ferdousi ZColazzo DNegre E(2018)CBPF: Leveraging Context and Content Information for Better RecommendationsAdvanced Data Mining and Applications10.1007/978-3-030-05090-0_32(381-391)Online publication date: 16-Nov-2018
https://dl.acm.org/doi/10.1007/978-3-030-05090-0_32
Gasparic MMurphy GRicci F(2017)A context model for IDE-based recommendation systemsJournal of Systems and Software10.1016/j.jss.2016.09.012128:C(200-219)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1016/j.jss.2016.09.012

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