Learning users' interests by quality classification in market-based recommender systems

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...

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Learning Users' Interests in a Market-Based Recommender System

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...

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Exploiting Past Users’ Interests and Predictions in an Active Learning Method for Dealing with Cold Start in Recommender Systems

This paper focuses on the new users cold-start issue in the context of recommender systems. New users who do not receive pertinent recommendations may abandon the system. In order to cope with this issue, we use active learning techniques. These methods engage the new users to interact with the system by presenting them with a questionnaire that aim to understand their preferences to the relate...

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Do recommender systems benefit users?

Recommender systems are present in many web applications to guide our choices. They increase sales and benefit sellers, but whether they benefit customers by providing relevant products is questionable. Here we introduce a model to examine the benefit of recommender systems for users, and found that recommendations from the system can be equivalent to random draws if one relies too strongly on ...

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Learning Users Interests for Providing Relevant Information

In this paper we present a system that attempts to learn the surfing users interests without asking him for any feedback. The system uses the user profile to conducts an independent search of the Internet, trying to find pages that the user have not read yet and might find interesting. The system consists of 3 modules: (1) The data-collecting module, which collects the data to be processed int...

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Learning users' interests by quality classification in market-based recommender systems

نویسندگان

چکیده

منابع مشابه

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

Learning Users' Interests in a Market-Based Recommender System

Exploiting Past Users’ Interests and Predictions in an Active Learning Method for Dealing with Cold Start in Recommender Systems

Do recommender systems benefit users?

Learning Users Interests for Providing Relevant Information

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