A Market - Based Approach to Recommender Systems by Yan Zheng WEI

A Market-Based Recommender System

A New WordNet Enriched Content-Collaborative Recommender System

The recommender systems are models that are to predict the potential interests of users among a number of items. These systems are widespread and they have many applications in real-world. These systems are generally based on one of two structural types: collaborative filtering and content filtering. There are some systems which are based on both of them. These systems are named hybrid recommen...

Market-Based Recommender Systems: Learning Users' Interests by Quality Classification

Recommender systems are widely used to cope with the problem of information overload and, consequently, many recommendation methods have been developed. However, no one technique is best for all users in all situations. To combat this, we have previously developed a market-based recommender system that allows multiple agents (each representing a different recommendation method or system) to com...

Merging Similarity and Trust Based Social Networks to Enhance the Accuracy of Trust-Aware Recommender Systems

In recent years, collaborative filtering (CF) methods are important and widely accepted techniques are available for recommender systems. One of these techniques is user based that produces useful recommendations based on the similarity by the ratings of likeminded users. However, these systems suffer from several inherent shortcomings such as data sparsity and cold start problems. With the dev...

Providing a model based on Recommender systems for hospital services (Case: Shariati Hospital of Tehran)

Background and objectives: In the increasingly competitive market of the healthcare industry, the organizations providing health care services are highly in need of systems that will enable them to meet their clients' needs in order to achieve a high degree of patient satisfaction. To this end, health managers need to identify the factors affecting patient satisfaction focus. T...