Open AccessPosted Content
A Survey Paper on Recommender Systems
TLDR
This paper provides ways to evaluate efficiency, scalability and accuracy of recommender system, and analyzes different algorithms of user based and item based techniques for recommendation generation.Abstract:
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.read more
Citations
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Journal ArticleDOI
Recommender Systems
TL;DR: In this article, a review of recent developments in recommender systems and discuss the major challenges and major challenges of recommender system and their potential impacts and future directions are discussed, and they compare and evaluate available algorithms and examine their roles in the future developments.
Journal ArticleDOI
Dealing with the new user cold-start problem in recommender systems: A comparative review
TL;DR: A classification that divides the relevant studies addressing the new user cold-start problem into three major groups and summarize their advantages and disadvantages in a tabular format is presented and NHSM achieves better accuracy and computational time than the relevant methods.
Journal ArticleDOI
User based Collaborative Filtering using fuzzy C-means
Hamidreza Koohi,Kourosh Kiani +1 more
TL;DR: A fuzzy C-means approach has been proposed for user-based Collaborative Filtering and its performance against different clustering approaches has been assessed and empirical results indicate that a combination of Center of Gravity defuzzified Fuzzy Clustering and Pearson correlation coefficient can yield better recommendation results, compared to other techniques.
References
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Book
Data Mining: Concepts and Techniques
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
Proceedings ArticleDOI
Item-based collaborative filtering recommendation algorithms
TL;DR: This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
Journal ArticleDOI
Evaluating collaborative filtering recommender systems
TL;DR: The key decisions in evaluating collaborative filtering recommender systems are reviewed: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole.
Journal Article
Industry Report: Amazon.com Recommendations: Item-to-Item Collaborative Filtering.
TL;DR: This work compares three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods, and their algorithm, which is called item-to-item collaborative filtering.