GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Communications of the ACM
Referral Web: combining social networks and collaborative filtering
Communications of the ACM
Data mining: concepts and techniques
Data mining: concepts and techniques
Expertise recommender: a flexible recommendation system and architecture
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Transactional information systems: theory, algorithms, and the practice of concurrency control and recovery
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Privacy through pseudonymity in user-adaptive systems
ACM Transactions on Internet Technology (TOIT)
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Searching for expertise in social networks: a simulation of potential strategies
GROUP '05 Proceedings of the 2005 international ACM SIGGROUP conference on Supporting group work
Usage derived recommendations for a video digital library
Journal of Network and Computer Applications
Yago: a core of semantic knowledge
Proceedings of the 16th international conference on World Wide Web
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
On social networks and collaborative recommendation
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
A conflict resolution methodology for collective ubiquitous context-aware applications
CSCWD '09 Proceedings of the 2009 13th International Conference on Computer Supported Cooperative Work in Design
Journal of Network and Computer Applications
Content-free collaborative learning modeling using data mining
User Modeling and User-Adapted Interaction
Cost-aware travel tour recommendation
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
A communication infrastructure to ease the development of mobile collaborative applications
Journal of Network and Computer Applications
User Models in Dialog Systems
Editorial: Collaboration computing technologies and applications
Journal of Network and Computer Applications
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Collaborative recommendation (CR) is a popular method of filtering items that may interest social users by referring to the opinions of friends and acquaintances in the network and computer applications. However, CR involves a cold-start problem, in which a newly established recommender system usually exhibits low recommending accuracy because of insufficient data, such as lack of ratings from users. In this study, we rigorously identify active users in social networks, who are likely to share and accept a recommendation in each data cluster to enhance the performance of the recommendation system and solve the cold-start problem. This novel modified CR method called div-clustering is presented to cluster Web entities in which the properties are specified formally in a recommendation framework, with the reusability of the user modeling component considered. We improve the traditional k-means clustering algorithm by applying supplementary works such as compensating for nominal values supported by the knowledge base, as well as computing and updating the k value. We use the data from two different cases to test for accuracy and demonstrate high quality in div-clustering against a baseline CR algorithm. The experimental results of both offline and online evaluations, which also consider in detail the volunteer profiles, indicate that the CR system with div-clustering obtains more accurate results than does the baseline system.