Evolutionary approaches for weight optimization in collaborative filtering-basedrecommender systems

Optimization of Collaborative Filtering Systems

A collaborative filtering system (CFS) makes recommendations to users via the similarity and proximity between the profiles and taking into account their historical valuations. In contrast to most of the CFS, which are based on the approach-based users, we adopt the approach based items to improve the quality of recommendation, this process seems flexible and allowed us to integrate other sourc...

Local Approaches for Collaborative Filtering

Scalable Collaborative Filtering Approaches for Large Recommender Systems

The collaborative filtering (CF) using known user ratings of items has proved to be effective for predicting user preferences in item selection. This thriving subfield of machine learning became popular in the late 1990s with the spread of online services that use recommender systems, such as Amazon, Yahoo! Music, and Netflix. CF approaches are usually designed to work on very large data sets. ...

Improved Neighborhood Formation Approaches for Collaborative Filtering

E-commerce, buying and selling of products by electronic means, has become popular due to the emergence of World Wide Web. One of the vital components of e-commerce systems is recommender systems (RSs). The RS is employed as a part of e-commerce system to help users in finding products of their interest from a huge number of available products. The Collaborative filtering (CF) approach is one o...

Collaborative Filtering Recommender Systems

Recommender systems are an important part of the information and e-commerce ecosystem. They represent a powerful method for enabling users to filter through large information and product spaces. Nearly two decades of research on collaborative filtering have led to a varied set of algorithms and a rich collection of tools for evaluating their performance. Research in the field is moving in the d...