Content and Expert Recommendation System Using Improved Collaborative Filtering Method for Social Learning
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.
KeywordsItem-Based Collaborative Filtering Social Network Analysis Semantic Ontology Compound Knowledge Case-Based Reasoning
Unable to display preview. Download preview PDF.
- 1.Banduar, A.: Social-Learning Theory of Identificatory Processes, Stanford UniversityGoogle Scholar
- 2.Linden, G., Smith, B., York, J.: Amazon.com Recommendations, Item-to-Item Collaborative FilteringGoogle Scholar
- 3.Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-Based Collaborative Filtering Recommendation AlgorithmsGoogle Scholar
- 4.A Programmer’s Guide to Data Mining, http://guidetodatamining.com/
- 5.Lemire, D., Maclachlan, A.: Slope One Predictors for Online Rating-Based Collaborative Filtering (February 7, 2005)Google Scholar
- 6.An Expert Recommendation System using Ontology-based Social Network AnalysisGoogle Scholar
- 8.Mika, P.: Flink: Semantic Web Technology for the Extraction and Analysis of Social Networks. Journal of Web Semantics (2005)Google Scholar