Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Visualized recommender system based on Freebase

Freebase 기반의 추천 시스템 시각화

  • Hong, Myung-Duk (Dept. of Computer and Information Engineering, Inha University) ;
  • Ha, Inay (Dept. of Computer and Information Engineering, Inha University) ;
  • Jo, Geun-Sik (School of Computer and Information Engineering, Inha University)
  • 홍명덕 (인하대학교 컴퓨터정보공학과) ;
  • 하인애 (인하대학교 컴퓨터정보공학과) ;
  • 조근식 (인하대학교 컴퓨터정보공학과)
  • Received : 2013.08.08
  • Accepted : 2013.09.06
  • Published : 2013.10.31
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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.

본 논문에서는 영화 추천을 위해 사용자들이 명시적으로 표시한 신뢰 정보를 이용하여 소셜 네트워크와 유사하게 신뢰 네트워크를 생성하고, 그 사용자들의 연결 정도를 이용하여 추천 시스템에 적용하며, 추천 정보는 시각화 방법을 이용하여 제공하는 방법을 제안한다. 이를 통해 사용자가 명시적으로 신뢰 관계를 표현한 신뢰 네트워크에서 숨겨진 신뢰 관계를 추론한다. 시각화된 추천 정보는 영화, 음악, 인물 등 다양한 토픽에 대한 정보를 구조화된 형태로 제공하는 Freebase를 이용하였으며, 시각화 방법은 다음 3가지와 같다. (1) 사용자가 제공받고자 하는 영화의 수만큼 영화 포스터로 시각화하고, (2) 추천된 영화 중 특정 영화를 선택하면 영화 감독, 주연 배우, 장르 등의 부가적인 정보를 시각화하여 제공한다. 마지막으로 (3) 신뢰 기반의 사용자들 중 임의로 몇 명을 이웃 사용자로 선택하여 추천한다. 본 논문에서는 시각화 방법을 적용함으로써 추천 수 또는 이웃 사용자의 수, 그리고 부가 정보 요청 등 사용자의 의견(요구)을 바탕으로 추천하기 때문에 사용자의 의사결정 능력을 향상시킬 수 있다. 뿐만 아니라 본 논문에서 제안하는 추천 시각화 방법을 통해 동적으로 사용자들의 요구를 반영할 수 있고, Freebase, LinkedMDB, 위키피디아 등 현존하는 LOD의 정보 재사용을 통해 보다 풍부하게 추천 정보를 제공할 수 있다.

Keywords

References

  1. Se-yeoung Park, Hyeon-Jae Lee, Jun-Myeon Jo, "Intelligent search service technology trends and industry outlook", Korea Communications Agency, PM Issue Report, No. 2, Issue. 4, May. 2013.
  2. Oliver Oechslein, Thomas Hess, "Incorporating Social Networking Information in Recommender Systems: The Development of a Classification Framework", 26th Bled eConference eInnovations: Challenges and Impacts for Individuals, Organizations and Society, pp. 287-298, Jun. 2013.
  3. Suyun Wei, Ning Ye, Shuo Zhang, Xia Huang, Jian Zhu, "Collaborative Filtering Recommendation Algorithm Based on Item Clustering and global Similarity", 2012 Fifth International Conference on Business Intelligence and Financial Engineering, pp. 69-72, Aug. 2012.
  4. Tevfik Aytekin, Mahmut Ozge Karakaya, "Clustering-based diversity improvement in top-N recommendation", Journal of Intelligent Information Systems, Springer, pp. 1-18, Jun. 2013.
  5. Yongli Ren, Gang Li, Wanlei Zhou, "A learning method for Top-N recommendation with incomplete data", Social Network Analysis and Mining, Springer, pp. 1-14, Feb. 2013.
  6. Ernesto Diaz-Aviles, Lucas Drumond, Lars Schmidt-Thieme, Wolfgang Nejdl, "Real-time top-n recommendation in social streams", Proceedings of the sixth ACM conference on Recommender systems, pp. 59-66, Sep. 2012.
  7. Michail Vlachos, Daniel Svonava, "Graph Embeddings for Movie Visualization and Recommendation", Proceedings First International Workshop on Interfaces for Recommender Systems, pp. 56-59, Sep. 2012.
  8. Jason J. Jung, "Visualizing Recommendation Flow on Social Network", Journal of Universal Computer Science, Vol. 11, Issue. 11, pp. 1780-1791, Nov. 2005.
  9. Inay Ha, Kyeong-Jin Oh, Myung-Duk Hong, Geun-Sik Jo, "Social filtering using social relationship for movie recommendation", The 4th international conference on Computational Collective Intelligence: technologies and applications, Vol. Part I, LNAI 7653, pp. 395-404, Nov. 2012.
  10. Tim Berners-Lee, "Linked Data", http://www.w3.org/DesignIssues/LinkedData.html
  11. Won Seok Oh, "Increase the value of the data linked data", Korean Semantic Web Conference, Dec. 2010.
  12. Oktie Hassanzadeh, Mariano Consens, "Linked Movie Data Base", Proceedings of the WWW2009 Workshop on Linked Data on the Web, Apr. 2009.
  13. Tommaso Di Noia, Roberto Mirizzi, Vito Claudio Ostuni, Davide Romito, Markus Zanker, "Linked Open Data to support Content-based Recommender Systems", 8th International Conference on Semantic Systems, Sep. 2012.
  14. Roberto Mirizzi, Tommaso Di Noia, Azzurra Ragone, Vito Claudio Ostuni, Eugenio Di Sciascio, "Movie Recommendation with DBpedia", 3rd Italian Information Retrieval Workshop, Jan. 2012.
  15. Tommaso Di Noia, Roberto Mirizzi, Vito Claudio Ostuni, Davide Romito, "Exploiting the Web of Data in Model-based Recommender Systems", 6th ACM Conference on Recommender System, pp. 253-256, Sep. 2012.
  16. Federica Cena, Elisa Chiabrando, Andrea Crevola, Martina Deplano, Cristina Gena, and Francesco Osborne, "A Proposal for an Open Local Movie Recommender", 6th International Workshop on Personalized Accessto Cultural Heritage, Jun. 2013.
  17. Oktie Hassanzadeh, Mariano Consens, "Linked Movie Data Base", LOD Triplification Challenge at I-Semantics 2008, Apr. 2008.
  18. Michail Vlachos, Daniel Svonava, "Graph Embeddings for Movie Visualization and Recommendation", Workshop on Interfaces for Recommender Systems 2012, in conjunction with the 6th ACMconference on Recommender Systems, pp. 56-59, Sep. 2012.
  19. Daniel Svonava, Michail Vlachos, "Visualization and Recommendation of Similar Movies on the IMDB Movie Database.", Dec. 2010.
  20. Svetlin Bostandjiev, John O'Donovan, Tobias Hollerer, "TasteWeights: A Visual Interactive Hybrid Recommender System", The 6th ACM conference on Recommender Systems, pp. 35-42, Sep. 2012.
  21. John O'Donovan, Brynjar Gretarsson, Barry Smyth, "A Visual Interface for Social Information Filtering", International Conference on Computational Science and Engineering, Vol.4, pp. 74-81, Aug. 2009.
  22. David Gotz, Zhen Wen, "Behavior-Driven Visualization Recommendation", The 14th international conference on Intelligent User Interfaces, pp. 315-324, Feb. 2009.
  23. Katrien Verbert, Denis Para, Peter Brusilovsky, Erik Duval, "Visualizing Recommendations to Support Exploration, Transparency and Controllability", The 18th international conference on Intelligent User Interfaces, pp. 351-362, Mar. 2013.
  24. Herlocker, J. L., Konstan, J. A., Terveen, L. G., Riedl, J. T., "Evaluating Collaborative Filtering Recommender Systems", ACM Transactions on Information Systems, Vol. 22(1), pp. 5-53, Jan. 2004. https://doi.org/10.1145/963770.963772
  25. Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, Jamie Taylor, "Freebase: A Collaboratively Created Graph Database For Structuring Human Knowledge", Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 1247-1249, Jun. 2008.