User evaluation of a market-based recommender system

A social recommender system based on matrix factorization considering dynamics of user preferences

With the expansion of social networks, the use of recommender systems in these networks has attracted considerable attention. Recommender systems have become an important tool for alleviating the information that overload problem of users by providing personalized recommendations to a user who might like based on past preferences or observed behavior about one or various items. In these systems...

A Market-Based Recommender System

User Evaluation of a Conversational Recommender System

Conversational recommender systems (CRSs) approach user preference acquisition from a conversational point of view, where preferences are captured and put to use in the course of on-going natural language dialogue. The approach is motivated by its aim to make interaction efficient and natural, to acquire preferences from the user in a context when she is motivated to give them, as well as to fa...

Evolutionary User Clustering Based on Time-Aware Interest Changes in the Recommender System

The plenty of data on the Internet has created problems for users and has caused confusion in finding the proper information. Also, users' tastes and preferences change over time. Recommender systems can help users find useful information. Due to changing interests, systems must be able to evolve. In order to solve this problem, users are clustered that determine the most desirable users, it pa...

Recommender System Based on User-generated Content

Recommender systems apply statistical and knowledge discovery techniques to the problem of making recommendations during live user interaction. This paper describes a novel approach of building recommender systems for the Web with the aid of usergenerated content. Recently certain communities of Internet users have engaged in creating high quality peer reviewed content for the Web. In our appro...