Advertisement

Content and Expert Recommendation System Using Improved Collaborative Filtering Method for Social Learning

  • Kyungsun Kim
  • Kyounguk Lee
  • Jinwoo Park
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8210)

Abstract

Social Learning as a new concept of learning model emphasizes an individual’s activity and formation of relationships with other people. On the contrary, traditional recommendation system provides a target user with the appropriate recommendation information after analyzing a user’s preference based on the user’s profiles and rating histories. These kinds of systems need to modify recommendation algorithm; these traditional recommendation systems are limited to only two attributes - user profiles and rating histories – that includes the problem of recommendation reliability and accuracy. In this paper, we present a user-context based collaborative filtering (UCCF) using user-context and social relationships. The UCCF analyzes user-context and social relationships, and generates a similar user group which uses the user’s recommendation score from similar user groups. The UCCF reflects strong ties of users who have similar tendency and improves reliability and accuracy of the content and expert recommendation system.

Keywords

Item-Based Collaborative Filtering Social Network Analysis Semantic Ontology Compound Knowledge Case-Based Reasoning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Banduar, A.: Social-Learning Theory of Identificatory Processes, Stanford UniversityGoogle Scholar
  2. 2.
    Linden, G., Smith, B., York, J.: Amazon.com Recommendations, Item-to-Item Collaborative FilteringGoogle Scholar
  3. 3.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-Based Collaborative Filtering Recommendation AlgorithmsGoogle Scholar
  4. 4.
    A Programmer’s Guide to Data Mining, http://guidetodatamining.com/
  5. 5.
    Lemire, D., Maclachlan, A.: Slope One Predictors for Online Rating-Based Collaborative Filtering (February 7, 2005)Google Scholar
  6. 6.
    An Expert Recommendation System using Ontology-based Social Network AnalysisGoogle Scholar
  7. 7.
    Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge (1994)CrossRefGoogle Scholar
  8. 8.
    Mika, P.: Flink: Semantic Web Technology for the Extraction and Analysis of Social Networks. Journal of Web Semantics (2005)Google Scholar
  9. 9.
    Chee, S.H.S., Han, J., Wang, K.: Rectree: An efficient collaborative filtering method. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) DaWaK 2001. LNCS, vol. 2114, pp. 141–151. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  10. 10.
  11. 11.
  12. 12.

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Kyungsun Kim
    • 1
  • Kyounguk Lee
    • 1
  • Jinwoo Park
    • 1
  1. 1.R&D CenterDiquestSeoulKorea

Personalised recommendations