<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In movie domain, finding the appropriate movie to watch is a challenging task. This paper proposes a recommender system that suggests movies in cinema that fit the user’s available time, location, mood and emotions. Conducted experiments for evaluation showed that the proposed method outperforms the other baselines.</p></abstract>

In movie domain, finding the appropriate movie to watch is a challenging task. This paper proposes a recommender system that suggests movies in cinema that fit the user’s available time, location, mood and emotions. Conducted experiments for evaluation showed that the proposed method outperforms the other baselines.

Hybrid Recommender System via Personalized Users’ Context

Information System Dept., Computers and Information Faculty

Computer and Information System Department

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Hybrid Recommender System via Personalized Users\u2019 Context"}

In movie domain, finding the appropriate movie to watch is a challenging task. This paper proposes a recommender system that suggests movies in cinema that fit...

Hybrid Recommender System via Personalized Users’ Context

Online veröffentlicht: 29. März 2019

Seitenbereich: 101 - 115

Eingereicht: 29. Jan. 2019

Akzeptiert: 14. Feb. 2019

DOI: https://doi.org/10.2478/cait-2019-0006

Schlüsselwörter
Movie recommender, Emotion recommendation, Hybrid recommender system, sentiment analysis, spatio-temporal recommendation

© 2019 Anthony Nosshi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Hybrid Recommender System via Personalized Users’ Context

Online veröffentlicht: 29. März 2019

Seitenbereich: 101 - 115

Eingereicht: 29. Jan. 2019

Akzeptiert: 14. Feb. 2019

DOI: https://doi.org/10.2478/cait-2019-0006

SchlüsselwörterMovie recommender, Emotion recommendation, Hybrid recommender system, sentiment analysis, spatio-temporal recommendation

© 2019 Anthony Nosshi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Movie recommender, Emotion recommendation, Hybrid recommender system, sentiment analysis, spatio-temporal recommendation