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Cluster Computing
Erschienen in: Cluster Computing 1/2015

01.03.2015

Alleviating the cold-start problem by incorporating movies facebook pages

verfasst von: Ahmad Nurzid Rosli, Tithrottanak You, Inay Ha, Kyung-Yong Chung, Geun-Sik Jo

Erschienen in: Cluster Computing | Ausgabe 1/2015

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Abstract

Recommender systems are generally known as predictive ecosystem which recommends an appropriate list of items that may imply their similar preference or interest. Nevertheless, most discussed issues in recommendation system research domain are the cold-start problem. In this paper we proposed a novel approach to address this problem by combining similarity values obtain from a movie “Facebook Pages”. To achieve this, we first compute users’ similarity according to the rating cast on our Movie Rating System. Then, we combined similarity value obtain from user’s genre interest in “Like” information extracted from “Facebook Pages”. Finally, all the similarity values are combined to produce a new user’s similarity value. Our experiment results show that our approach is outperformed in cold-start problem compared to the benchmark algorithms. To evaluate whether our system is strong enough to recommend higher accuracy recommendation to users, we also conducted prediction coverage in this research work.
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Fußnoten
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Metadaten
Titel
Alleviating the cold-start problem by incorporating movies facebook pages
verfasst von
Ahmad Nurzid Rosli
Tithrottanak You
Inay Ha
Kyung-Yong Chung
Geun-Sik Jo
Publikationsdatum
01.03.2015
Verlag
Springer US
Erschienen in
Cluster Computing / Ausgabe 1/2015
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-014-0355-2

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