GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Collaborative filtering via gaussian probabilistic latent semantic analysis
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Unifying user-based and item-based collaborative filtering approaches by similarity fusion
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Improving Accuracy of Recommender System by Clustering Items Based on Stability of User Similarity
CIMCA '06 Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce
Improving the prediction accuracy of recommendation algorithms: Approaches anchored on human factors
Interacting with Computers
A Hybrid User and Item-Based Collaborative Filtering with Smoothing on Sparse Data
ICAT '06 Proceedings of the 16th International Conference on Artificial Reality and Telexistence--Workshops
Effective missing data prediction for collaborative filtering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Trust-inspiring explanation interfaces for recommender systems
Knowledge-Based Systems
Collaborative recommender systems: Combining effectiveness and efficiency
Expert Systems with Applications: An International Journal
Unified relevance models for rating prediction in collaborative filtering
ACM Transactions on Information Systems (TOIS)
Information Sciences: an International Journal
Wireless mesh networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
eSciGrid: A P2P-based e-science Grid for scalable and efficient data sharing
Future Generation Computer Systems
A new collaborative filtering metric that improves the behavior of recommender systems
Knowledge-Based Systems
A hybrid collaborative filtering recommendation mechanism for P2P networks
Future Generation Computer Systems
A user-item relevance model for log-based collaborative filtering
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
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In last decade, there is a paradigm shift in technology in the sense that large numbers of users over the internet share the valuable information with others. Users working in this field work at different levels for information sharing. As these users share the information with each other, there is a need of efficient collaborative mechanism among them to achieve efficiency and accuracy at each level. So to achieve high level of efficiency and accuracy, a distributed context aware collaborative filtering (CF) approach for service selection is proposed in this paper. Users profiles are created as a database repository from the previous data of different users and their respective interests. For the new user who wants to avail a particular service, system matches the request with the existing users profiles and if the match is found then a suitable service is recommended to him based upon his profile. To select the relevant contents of user choice that match his profile with the existing users, a Distributed Filtering Metric (DFM) is included which is based upon user input. Moreover, the intersection of existing users profiles and their interests is also included in this metric to have high level of accuracy. Specifically, we have taken an example of movie selection as a service offered to the users by some network. The underlying network chosen is Wireless Mesh Networks (WMNs) which are emerged as a new powerful technology in recent years due to the unique features such as low deployment cost and easy maintenance. A novel Context Aware Service Selection (CASS) algorithm is proposed. The performance of the proposed algorithm is evaluated with respect to efficiency and accuracy. The results obtained show that the proposed approach has high level of efficiency and accuracy.