Markov Logic Network Based Social Relation Inference for Personalized Social Search (original) (raw)

Abstract

Most recommendation systems are based on historical data and profile files. The most common method is collaboration filtering. After analysis, a collaboration filtering recommender system differentiates one user profile from another to determine what to recommend. There has been substantial research on personalized social search. However, previous research has neglected semantic social information, making no use of definite relations between objects. This problem can be solved using ontology and inference rules. In this paper, Markov-logic-network (MLN)-based social relation inference is performed using social user information, such as country, age, and preference. In addition, this paper evaluates whether the inference results regarding social relations have been correctly predicted based on social user data. The user’s personal and business relations are inferred based on MLNs and a social network comprised of user profile data.

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References

  1. Clements, M.: Personalization of social media, FDIA(BCS IRSG Symposium: Future Directions in Information Access (2007)
    Google Scholar
  2. Choi, C., et al.: Travel ontology for intelligent recommendation system. In: Third Asia International Conference on Modelling & Simulation, AMS 2009. IEEE (2009)
    Google Scholar
  3. Choi, C., et al.: Travel ontology for recommendation system based on semantic web. In: The 8th International Conference on Advanced Communication Technology, ICACT 2006, vol. 1. IEEE (2006)
    Google Scholar
  4. http://en.wikipedia.org/wiki/Social_network_analysis
  5. Choi, C., Choi, J., Kim, P.: Ontology based Access Control Model for Security Policy Reasoning in Cloud Computing. Journal of Supercomputing 67(3), 711–722 (2014)
    Article Google Scholar
  6. Choi, C., Hwang, M., Choi, D., Choi, J., Kim, P.: Automatic Document Tagging using Online Knowledge Base. Information: An International Interdisciplinary Journal 14(5), 1709–1720 (2011)
    Google Scholar
  7. Choi, J., Choi, C., Choi, D., Kim, J., Kim, P.: Automatic Extraction of Semantic Concept-Relation Triple Pattern from Wikipedia Articles. Information: An International Interdisciplinary Journal 15(7), 2755–2770 (2012)
    Google Scholar
  8. http://research.microsoft.com/en-us/projects/socialinformationretrieval/
  9. http://en.wikipedia.org/wiki/Social_search
  10. Social Networks: Building Smart Communities through Network Weaving, Valdis Krebs (2002)
    Google Scholar
  11. Choi, C., et al.: Probabilistic spatio-temporal inference for motion event understanding. Neurocomputing 122, 24–32 (2013)
    Article Google Scholar
  12. de Oliveira, P.C.: Probabilistic Reasoning in the Semantic Web using Markov Logic. MSc Thesis (July 2009)
    Google Scholar

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Authors and Affiliations

  1. Division of Undeclared Majors, Chosun University, 309, Pilmun-daero, Donggu, Gwangju, South Korea
    Junho Choi & Pankoo Kim
  2. Departmrent of Computer Engineering, Chosun University, 309, Pilmun-daero, Donggu, Gwangju, South Korea
    Chang Choi & Eunji Lee

Authors

  1. Junho Choi
  2. Chang Choi
  3. Eunji Lee
  4. Pankoo Kim

Corresponding author

Correspondence toJunho Choi .

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Editors and Affiliations

  1. Autonomous University of Madrid, Madrid, Spain
    David Camacho
  2. Hanyang University, Seoul, Korea, Republic of (South Korea)
    Sang-Wook Kim
  3. Wrocław University of Technology, Wroclaw, Poland
    Bogdan Trawiński

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© 2015 Springer International Publishing Switzerland

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Choi, J., Choi, C., Lee, E., Kim, P. (2015). Markov Logic Network Based Social Relation Inference for Personalized Social Search. In: Camacho, D., Kim, SW., Trawiński, B. (eds) New Trends in Computational Collective Intelligence. Studies in Computational Intelligence, vol 572. Springer, Cham. https://doi.org/10.1007/978-3-319-10774-5\_19

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