A Distributed Collaborative Filtering Recommendation Model for P2P Networks
نویسندگان
چکیده
Conventional collaborative filtering(CF) recommendation applies the user-based centralized architecture. This architecture has some problems of sparsity and scalability, in addition to not fit the current popular P2P architecture. Therefore, this paper proposes a distributed model to implement the CF algorithm by maintaining the user’s record information distributedly in each nodes throughout the network, constructing a DHT, applying the Chord algorithm to realize locating of the record and designing the corresponding communication policy to obtain data needed.
منابع مشابه
A New Similarity Measure Based on Item Proximity and Closeness for Collaborative Filtering Recommendation
Recommender systems utilize information retrieval and machine learning techniques for filtering information and can predict whether a user would like an unseen item. User similarity measurement plays an important role in collaborative filtering based recommender systems. In order to improve accuracy of traditional user based collaborative filtering techniques under new user cold-start problem a...
متن کاملDisTriB: Distributed Trust Management Model Based on Gossip Learning and Bayesian Networks in Collaborative Computing Systems
The interactions among peers in Peer-to-Peer systems as a distributed collaborative system are based on asynchronous and unreliable communications. Trust is an essential and facilitating component in these interactions specially in such uncertain environments. Various attacks are possible due to large-scale nature and openness of these systems that affects the trust. Peers has not enough inform...
متن کاملProCF: Probabilistic Collaborative Filtering for Reciprocal Recommendation
Similarity in people to people (P2P) recommendation in social networks is not symmetric, where both entities of a relationship are involved in the reciprocal process of determining the success of the relationship. The widely used memory-based collaborative filtering (CF) has advantages of effectiveness and efficiency in traditional item to people recommendation. However, the critical step of co...
متن کاملDisTriB: Distributed Trust Management Model Based on Gossip Learning and Bayesian Networks in Collaborative Computing Systems
The interactions among peers in Peer-to-Peer systems as a distributed collaborative system are based on asynchronous and unreliable communications. Trust is an essential and facilitating component in these interactions specially in such uncertain environments. Various attacks are possible due to large-scale nature and openness of these systems that affects the trust. Peers has not enough inform...
متن کاملA Collaborative Filtering Recommendation Mechanism for Peer-to-Peer Video Sharing
Almost all collaborative filtering recommendation systems based on C/S mode have to face the problems of one-point-failure and unscalable. This study proposes a scalable collaborative filtering recommendation mechanism for video sharing in unstructured Peer-to-Peer (P2P) networks. The mechanism is named as CFRPV, which can recommend videos in distributed way. The CFRPV mechanism includes four p...
متن کامل