Visualized recommender system based on Freebase (Freebase 기반의 추천 시스템 시각화)

Myungduk Hong (홍명덕); 하인애; JO GEUN SIK (조근식)

Visualized recommender system based on Freebase

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2013, 18(10), pp.23-37
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science

Myungduk Hong ¹, 하인애 ¹, JO GEUN SIK ¹

¹인하대학교

Accredited

ABSTRACT

In this paper, the proposed movie recommender system constructs trust network, which is similar to social network, using user's trust information that users explicitly present. Recommendation on items is performed by using relation degree between users and information of recommended item is provided by a visualization method. We discover the hidden relationships via the constructed trust network. To provide visualized recommendation information, we employ Freebase which is large knowledge base supporting information such as movie, music, and people in structured format. We provide three visualization methods as the followings: ⅰ) visualization based on movie posters with the number of movies that user required. ⅱ) visualization on extra information such as director, actor and genre and so on when user selected a movie from recommendation list. ⅲ) visualization based on movie posters that is recommended by neighbors who a user selects from trust network. The proposed system considers user's social relations and provides visualization which can reflect user's requirements. Using the visualization methods, user can reach right decision making on items. Furthermore, the proposed system reflects the user's opinion through recommendation visualization methods and can provide rich information to users through LOD(Linked Open Data) Cloud such as Freebase, LinkedMDB and Wikipedia and so on.

KEYWORDS

Visualized recommender system, Freebase(LOD), Trust-network, user modeling

Citation status

* References for papers published after 2023 are currently being built.

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This paper was written with support from the National Research Foundation of Korea.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65