Data Mining - Concepts and Techniques.

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances In this article, we review recent developments in recommender systems and discuss the major challenges We compare and evaluate available algorithms and examine their roles in the future developments In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems Potential impacts and future directions are discussed We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers

Recommender Systems

Dealing with the new user cold-start problem in recommender systems: A comparative review

User based Collaborative Filtering using fuzzy C-means

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Exploiting User Demographic Attributes for Solving Cold-Start Problem in Recommender System

Recommender systems apply data mining techniques and prediction algorithms to predict users' interest on information, products and services among the tremendous amount of available items. The vast growth of information on the Internet as well as number of visitors to websites add some key challenges to recommender systems. These are: producing accurate recommendation, handling many recommendations efficiently and coping with the vast growth of number of participants in the system. Therefore, new recommender system technologies are needed that can quickly produce high quality recommendations even for huge data sets. 
To address these issues we have explored several collaborative filtering techniques such as the item based approach, which identify relationship between items and indirectly compute recommendations for users based on these relationships. The user based approach was also studied, it identifies relationships between users of similar tastes and computes recommendations based on these relationships. 
In this paper, we introduce the topic of recommender system. It provides ways to evaluate efficiency, scalability and accuracy of recommender system. The paper also analyzes different algorithms of user based and item based techniques for recommendation generation. Moreover, a simple experiment was conducted using a data mining application -Weka- to apply data mining algorithms to recommender system. We conclude by proposing our approach that might enhance the quality of recommender systems.

William Nzoukou

Papers

A Survey Paper on Recommender Systems