A Multi-clustering Hybrid Recommender System

Puntheeranurak, Sutheera; Tsuji, Hiroshi

doi:10.1109/cit.2007.54

Cited by 25 publications

(9 citation statements)

References 13 publications

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“…In this paper, the KNN [10,11] method is used to recommend the learning resources. Firstly, the all visited resources were divided into two different set: interested resource set and not interested set.…”

Section: Resource Recommendation Methodsmentioning

confidence: 99%

A Web Mining Based Network Personalized Learning System

Pang¹,

Yu²,

Wang³

2015

Proceedings of the 2015 3rd International Conference on Machinery, Materials and Information Technology Applications

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In order to solve the problems of the traditional network learning system, a network personalized learning system based on web mining is presented in this paper. The web mining technology is simply introduced. The system model is given and the work process of this model is depicted in detail. For improve the intelligence or the system, the personalized information extraction method based on web usage mining and the learning resources recommendation method based on web content mining was proposed. Experiments on the real world dataset show that the system meets the student's demands of personalized learning.

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Section: Resource Recommendation Methodsmentioning

confidence: 99%

A Web Mining Based Network Personalized Learning System

Pang¹,

Yu²,

Wang³

2015

Proceedings of the 2015 3rd International Conference on Machinery, Materials and Information Technology Applications

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“…It narrows the scale of datasets and improves the quality of recommendations. Recommender systems make use of many clustering methods such as k-means, SOM, and so forth (Sun, Lui, and Zhao 2008;Yanxiang et al 2012), to obtain item-based clustering (Qing and Byeong 2003), user-based clustering (Aggrawal and Haque 2005), and user item rating matrix co-clustering (Puntheeranuark and Hidekazu 2007). The formation of synthetic groups in the proposed group recommender system is performed by the GFU.…”

Section: Group Recommender Systemsmentioning

confidence: 99%

A Proposed Framework for Group-Based Multi-Criteria Recommendations

Dixit

Mehta

Bedi

2014

Applied Artificial Intelligence

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The Multi Criteria Group Recommender System is gaining attention. In the proposed framework, the authors intend to find solutions to two problems. First, group members assign arbitrary ratings to multiple criteria of items; these do not reflect their honest opinions. The proposed framework selects a promising group for the target user on the basis of demographic attributes. In case more than one group claims to be the promising group, the proposed framework implicitly derives multi-criteria ratings of each group member on multiple item attributes. Second, as the group size increases, difficulty in aggregating the preferences of all the group members increases, resulting in errors in group recommendations. The framework generates group recommendations from the nearest neighbor set of group members (GNN Set ), instead of generating group recommendations from all the members of the target group. Cascade TOPSIS is proposed, which selects the GNN Set from the identified group thereby retaining only those group members who are likely to provide good quality group recommendations. TOPSIS on expert group members and nonexpert group members are used to handle group decision making. The preferences of members of the GNN Set are used to generate recommendations for the target user. Using the Movie Lens dataset, significant improvement in recommendation quality is observed.

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“…In Ref. [34], the authors offered a hybrid recommender system by using the fuzzy k-means clustering algorithm. They linearly combined the results of the pure CF applied over the original and also the clustered data to improve the conventional CF.…”

Section: Clustering In Recommender Systemsmentioning

confidence: 99%

“…Several hybrid recommender systems which employ clustering algorithms have been proposed in the literature to overcome recommender systems' shortcomings [34,36,38]. In Ref.…”

Section: Clustering In Recommender Systemsmentioning

confidence: 99%

“…To overcome the cold start problem, the clustering concept is used in collaborative filtering to provide more accurate predictions [8,11,13,23,[34][35][36][37][38][39][40][41]. Although it has been shown that clustering-based CF approaches are useful methods to deal with the cold start problem, these methods also suffer from several shortcomings.…”

Section: Introductionmentioning

confidence: 99%

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