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New Technology of Library and Information Service  2013, Vol. 29 Issue (10): 1-7    DOI: 10.11925/infotech.1003-3513.2013.10.01
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Review of Recommendation System Based on Linked Data
Tian Ye1,2, Zhu Zhongming1, Liu Shudong3
1. The Lanzhou Branch of National Science Library, Chinese Academy of Sciences, Lanzhou 730000, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China;
3. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
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Abstract  Firstly, this paper introduces the background and the effect of linked data in recommendation system, summarizes similarities and differences between the recommendation system based on linked data and the traditional recommendation system. This is to help readers understand the cause and application background of the recommendation system based on linked data. Secondly, this paper systematically analyses the main method of recommendation system based on linked data on basis of the general classification of recommendation system and detailed introduction of concrete application examples.
Key wordsLinked data      Ontology      Semantic Web      Recommendation system     
Received: 08 July 2013      Published: 04 November 2013
:  TP393  

Cite this article:

Tian Ye, Zhu Zhongming, Liu Shudong. Review of Recommendation System Based on Linked Data. New Technology of Library and Information Service, 2013, 29(10): 1-7.

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[1] Burke R. Hybrid Recommender Systems: Survey and Experiments[J].User Modeling and User-adapted Interaction,2002,12(4):331-370.
[2] Adomavicius G, Tuzhilin A. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-art and Possible Extensions[J]. IEEE Transactions on Knowledge and Data Engineering, 2005,17(6):734-749.
[3] Sarwar B, Karypis G, Konstan J, et al. Analysis of Recommendation Algorithms for E-commerce[C]. In: Proceedings of the 2nd ACM Conference on Electronic Commerce. New York, NY, USA: ACM, 2000:158-167.
[4] Sarwar B, Karypis G, Konstan J, et al. Item-based Collaborative Filtering Recommendation Algorithms[C].In: Proceedings of the 10th International Conference on World Wide Web. New York, NY, USA: ACM, 2001:285-295.
[5] Claypool M, Gokhale A, Miranda T,et al. Combining Content-based and Collaborative Filters in an Online Newspaper[C]. In: Proceedings of ACM SIGIR Workshop on Recommender Systems.1999.
[6] Good N, Schafer J B, Konstan J A, et al. Combining Collaborative Filtering with Personal Agents for Better Recommendations[C]. In: Proceedings of the 16th National Conference on Artificial Intelligence and the 11th Innovative Applications of Artificial Intelligence. Menlo Park, CA, USA: American Association for Artificial Intelligence, 1999:439-446.
[7] Sarwar B M, Konstan J A, Borchers A, et al. Using Filtering Agents to Improve Prediction Quality in the Grouplens Research Collaborative Filtering System[C]. In: Proceedings of the 1998 ACM Conference on Computer Supported Cooperative Work. New York, NY, USA: ACM,1998:345-354.
[8] Koren Y,Bell R,Volinsky C. Matrix Factorization Techniques for Recommender Systems[J]. Computer,2009,42(8):30-37.
[9] Ma H, King I, Lyu M R. Learning to Recommend with Social Trust Ensemble[C].In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA: ACM, 2009.
[10] Adomavicius G,Tuzhilin A. Context-aware Recommender Systems[A].//Recommender Systems Handbook[M]. Springer, 2011:217-253.
[11] Adomavicius G, Tuzhilin A. Context-aware Recommender Systems[C].In: Proceedings of the 2008 ACM Conference on Recommender Systems. New York, NY, USA: ACM, 2008:335-336.
[12] Passant A,Heitmann B,Hayes C. Using Linked Data to Build Recommender Systems[C].In: Proceedings of the 3rd ACM Conference on Recommender Systems. New York, NY,USA: ACM,2009.
[13] Di Noia T,Mirizzi R,Ostuni V C, et al. Exploiting the Web of Data in Model-based Recommender Systems[C]. In: Proceedings of the 6th ACM Conference on Recommender Systems. New York, USA: ACM, 2012:253-256.
[14] Apostolski V, Jovanoski L, Tranjanov D. Linked Data-based Social Bookmarking and Recommender System[C].In: Proceedings of ICT Innovations.2012:133-142.
[15] Policarpio S,Brunk S,Tummarello G. Implementation of a SPARQL Integrated Recommendation Engine for Linked Data with Hybrid Capabilities[C]. In: Proceedings of Artificial Intelligence Meets the Web of Data (AImWD) Workshop,at the European Conference on Artificial Intelligence (ECAI). 2012.
[16] Berners-Lee T. Linked Data-Design Issues[EB/OL].(2006-07-27).[2013-07-28].
[17] Jentzsch R C A.The Linking Open Data Cloud Diagram[EB/OL].[2013-05-10].
[18] Heitmann B,Hayes C.Using Linked Data to Build Open,Collaborative Recommender Systems[C]. In: Proceedings of AAAI Spring Symposium: Linked Data Meets Artificial Intelligence.2010.
[19] George T,Merugu S.A Scalable Collaborative Filtering Framework Based on Co-clustering[C]. In: Proceedings of the 5th IEEE International Conference on Data Mining.2005:625-628.
[20] Lémdani R, Polaillon G, Bennacer N,et al. A Semantic Similarity Measure for Recommender Systems[C].In: Proceedings of the 7th International Conference on Semantic Systems. New York, NY, USA: ACM,2011:183-186.
[21] Passant A. Dbrec―Music Recommendations Using DBpedia[C].In: Proceedings of the 9th International Semantic Web Conference(ISWC).2010:209-224.
[22] Passant A. Measuring Semantic Distance on Linking Data and Using It for Resources Recommendations[C]. In: Proceedings of AAAI Spring Symposium: Linked Data Meets Artificial Intelligence.2010.
[23] Hu W, Yan K, Jia C, et al. SmartMusic: An Online Music Recommendation System Based on Semantic Web Technology[EB/OL]. [2013-07-28].
[24] Meymandpour R,Davis J G.Recommendations Using Linked Data[C].In: Proceedings of the 5th Ph.D. Workshop on Information and Knowledge.2012:75-82.
[25] Danica D,Milan S,Philippe L.Linked Data-based Concept Recommendation: Comparison of Different Methods in Open Innovation Scenario[C].In: Proceedings of the 9th Extended Semantic Web Conference.2012:24-38.
[26] Di Noia T,Mirizzi R,Ostuni V C,et al. Linked Open Data to Support Content-based Recommender Systems[C].In: Proceedings of the 8th International Conference on Semantic Systems. 2012:1-8.
[27] Baumann S,Schirru R.Using Linked Open Data for Novel Artist Recommendations[C].In: Proceedings of the 13th International Society for Music Information Retrieval Conference.2012.
[28] Ostuni V C, Di Noia T, Mirizzi R, et al. Cinemappy: A Context-aware Mobile App for Movie Recommendations Boosted by DBpedia[C].In: Proceedings of the SeRSy. 2012:37-48.
[29] Yang R, Hu W, Qu Y Z. Using Semantic Technology to Improve Recommendation Systems Based on Slope One[C]. In: Proceedings of Semantic Web and Web Science.Springer,2013:11-23.
[30] 许海玲,吴潇,李晓东,等.互联网推荐系统比较研究[J]. 软件学报,2009,20(2):350-362.(Xu Hailing, Wu Xiao, Li Xiaodong,et al. Comparison Study of Internet Recommendation System[J].Journal of Software,2009,20(2):350-362.)
[31] Fernández-Tobías I,Cantador I,Kaminskas M, et al. A Generic Semantic-based Framework for Cross-domain Recommendation[C].In:Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems.2011:25-32.
[32] Gordea S,Lindley A,Graf R. Computing Recommendations for Long Term Data Accessibility Basing on Open Knowledge and Linked Data[EB/OL].[2013-08-02].
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