In this work, we present a federated version of the state-of-the-art Neural Collaborative Filtering (NCF) approach for item recommendations. The system, named FedNCF, enables learning without requiring users to disclose or transmit their raw data. Data localization preserves data privacy and complies with regulations such as GDPR. Although model training local dissemination, transmission client...

  One of the most famous methods for recommendation is user-based Collaborative Filtering (CF). This system compares active user’s items rating with historical rating records of other users to find similar users and recommending items which seems interesting to these similar users and have not been rated by the active user. As a way of computing recommendations, the ultimate goal of the user-ba...

Recommender systems are often based on collaborative filtering. Previous researches on collaborative filtering mainly focus on one single recommender or formulating hybrid with different approaches. In consideration of the problems of sparsity, recommender error rate, sample weight update, and potential, we adapt AdaBoost and propose two novel boosting frameworks for collaborative filtering. Ea...

Web Service mining has become one of the predominant areas of Service Oriented Architecture. Web service discovery methods include syntactic based system and semantic based system. In the proposed work, both syntactic and semantic based approach is followed. The most widely used recommender technique is collaborative filtering. In this paper, we proposed architecture for Web Service Selection b...

Collaborative filtering is a new method for retrieving information. Instead of basing the search on the content in the information units, a collaborative filter or recommender system uses databases of user ratings as a basis for predictions. From our user studies we see that collaborative filtering can be of great help to users when they search for information. In order to be truly useful colla...

Being a distance education institution, our current infrastructure does not allow group or collaborative work on undergraduate level. Although students are allowed to work together and assist each other, each student is required to submit individual attempts for assignments and/or projects. Assignments that are so similar that we could not accept them as individual attempts are considered cheat...