Abstract
Information collected from the social network is recently used to improve a performance of recommender systems to an individual user or a group. During selecting the items among the group members, the relationships (e.g., position, dependency, and the strength of the social ties) often has an important role than the individual preference in the group. Hence, we propose a novel recommendation method based on social affinity between two users. This recommendation method consists of (i) the similarity calculation between movies based on weighted feature, (ii) the generation of initial affinity network graph, and (iii) the computation of user’s affinity to group based on the graph. Experimental results on synthetic dataset show that our proposed method can discover social affinities efficiently.
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Notes
- 1.
IMDB, http://www.imdb.com
- 2.
NAVER MOVIE, http://movie.naver.com/
- 3.
Facebook, https://www.facebook.com/
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Acknowledgement
This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2015-H8501-15-1018) supervised by the IITP (Institute for Information&communications Technology Promotion). Also, this work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2014R1A2A2A05007154). Also, this research was supported by SW Master’s course of hiring contract Program grant funded by the Ministry of Science, ICT and Future Planning (H0116-15-1013).
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© 2016 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Hong, M., Jung, J.J., Lee, M. (2016). Social Affinity-Based Group Recommender System. In: Vinh, P., Alagar, V. (eds) Context-Aware Systems and Applications. ICCASA 2015. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 165. Springer, Cham. https://doi.org/10.1007/978-3-319-29236-6_12
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DOI: https://doi.org/10.1007/978-3-319-29236-6_12
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