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Recommender system

About: Recommender system is a(n) research topic. Over the lifetime, 27227 publication(s) have been published within this topic receiving 598064 citation(s). The topic is also known as: recommendation system & recommendation engine.

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Papers
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Journal ArticleDOI: 10.1109/TKDE.2005.99
Abstract: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.

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Topics: Recommender system (59%), Collaborative filtering (57%), Cold start (55%) ...read more

9,202 Citations


Open accessProceedings ArticleDOI: 10.1145/371920.372071
01 Apr 2001-
Abstract: Recommender systems apply knowledge discovery techniques to the problem of making personalized recommendations for information, products or services during a live interaction. These systems, especially the k-nearest neighbor collaborative ltering based ones, are achieving widespread success on the Web. The tremendous growth in the amount of available information and the number of visitors to Web sites in recent years poses some key challenges for recommender systems. These are: producing high quality recommendations, performing many recommendations per second for millions of users and items and achieving high coverage in the face of data sparsity. In traditional collaborative ltering systems the amount of work increases with the number of participants in the system. New recommender system technologies are needed that can quickly produce high quality recommendations, even for very large-scale problems. To address these issues we have explored item-based collaborative ltering techniques. Item-based techniques rst analyze the user-item matrix to identify relationships between di erent items, and then use these relationships to indirectly compute recommendations for users. In this paper we analyze di erent item-based recommendation generation algorithms. We look into di erent techniques for computing item-item similarities (e.g., item-item correlation vs. cosine similarities between item vectors) and di erent techniques for obtaining recommendations from them (e.g., weighted sum vs. regression model). Finally, we experimentally evaluate our results and compare them to the basic k-nearest neighbor approach. Our experiments suggest 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.

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  • Figure 4: Impact of the similarity computation measure on item-based collaborative ltering algorithm.
    Figure 4: Impact of the similarity computation measure on item-based collaborative ltering algorithm.
  • Figure 8: Recommendation time and throughput comparison between model-based scheme and full item-item scheme. The comparisons are shown at three di erent x values.
    Figure 8: Recommendation time and throughput comparison between model-based scheme and full item-item scheme. The comparisons are shown at three di erent x values.
  • Figure 1: The Collaborative Filtering Process.
    Figure 1: The Collaborative Filtering Process.
  • Figure 5: Sensitivity of the parameter x on the neighborhood size
    Figure 5: Sensitivity of the parameter x on the neighborhood size
  • Figure 7: Sensitivity of the model size on item-based collaborative ltering algorithm
    Figure 7: Sensitivity of the model size on item-based collaborative ltering algorithm
Topics: Collaborative filtering (62%), Recommender system (61%), Slope One (59%) ...read more

7,756 Citations


Journal ArticleDOI: 10.1109/MC.2009.263
01 Aug 2009-IEEE Computer
Abstract: As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels

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Topics: Matrix decomposition (59%), Recommender system (58%), Matrix completion (53%) ...read more

7,690 Citations


Journal ArticleDOI: 10.1145/963770.963772
Abstract: Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: 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. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated.

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Topics: Recommender system (63%), Collaborative filtering (60%), Slope One (57%) ...read more

5,338 Citations


Open accessPosted Content
Abstract: Collaborative filtering or recommender systems use a database about user preferences to predict additional topics or products a new user might like. In this paper we describe several algorithms designed for this task, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods. We compare the predictive accuracy of the various methods in a set of representative problem domains. We use two basic classes of evaluation metrics. The first characterizes accuracy over a set of individual predictions in terms of average absolute deviation. The second estimates the utility of a ranked list of suggested items. This metric uses an estimate of the probability that a user will see a recommendation in an ordered list. Experiments were run for datasets associated with 3 application areas, 4 experimental protocols, and the 2 evaluation metrics for the various algorithms. Results indicate that for a wide range of conditions, Bayesian networks with decision trees at each node and correlation methods outperform Bayesian-clustering and vector-similarity methods. Between correlation and Bayesian networks, the preferred method depends on the nature of the dataset, nature of the application (ranked versus one-by-one presentation), and the availability of votes with which to make predictions. Other considerations include the size of database, speed of predictions, and learning time.

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Topics: Recommender system (59%), Collaborative filtering (59%), Bayesian network (55%) ...read more

4,883 Citations


Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202263
20212,482
20202,675
20192,659
20182,375
20172,316

Top Attributes

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Topic's top 5 most impactful authors

Francesco Ricci

155 papers, 10.4K citations

Robin Burke

116 papers, 9.2K citations

Dietmar Jannach

115 papers, 5.9K citations

Barry Smyth

109 papers, 4.8K citations

Bamshad Mobasher

101 papers, 4.9K citations

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