Abstract: This research focuses on the use of a recommender
system in decision support by means of a used car dealer case study
in Bangkok Metropolitan. The goal is to develop an effective used car
purchasing system for dealers based on the above premise. The
underlying principle rests on content-based recommendation from a
set of usability surveys. A prototype was developed to conduct
buyers- survey selected from 5 experts and 95 general public. The
responses were analyzed to determine the mean and standard
deviation of buyers- preference. The results revealed that both groups
were in favor of using the proposed system to assist their buying
decision. This indicates that the proposed system is meritorious to
used car dealers.
Abstract: This study proposes novel hybrid social network analysis and collaborative filtering approach to enhance the performance of recommender systems. The proposed model selects subgroups of users in Internet community through social network analysis (SNA), and then performs clustering analysis using the information about subgroups. Finally, it makes recommendations using cluster-indexing CF based on the clustering results. This study tries to use the cores in subgroups as an initial seed for a conventional clustering algorithm. This model chooses five cores which have the highest value of degree centrality from SNA, and then performs clustering analysis by using the cores as initial centroids (cluster centers). Then, the model amplifies the impact of friends in social network in the process of cluster-indexing CF.
Abstract: Currently searching through internet is very popular especially in a field of academic. A huge of educational information such as research papers are overload for user. So community-base web sites have been developed to help user search information more easily from process of customizing a web site to need each specifies user or set of user. In this paper propose to use association rule analyze the community group on research paper bookmarking. A set of design goals for community group frameworks is developed and discussed. Additionally Researcher analyzes the initial relation by using association rule discovery between the antecedent and the consequent of a rule in the groups of user for generate the idea to improve ranking search result and development recommender system.