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Information filtering system

About: Information filtering system is a(n) research topic. Over the lifetime, 4657 publication(s) have been published within this topic receiving 114536 citation(s).

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Papers
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Journal ArticleDOI: 10.1109/MIC.2003.1167344
Greg Linden1, Brent R. Smith1, J. York1Institutions (1)
Abstract: Recommendation algorithms are best known for their use on e-commerce Web sites, where they use input about a customer's interests to generate a list of recommended items. Many applications use only the items that customers purchase and explicitly rate to represent their interests, but they can also use other attributes, including items viewed, demographic data, subject interests, and favorite artists. At Amazon.com, we use recommendation algorithms to personalize the online store for each customer. The store radically changes based on customer interests, showing programming titles to a software engineer and baby toys to a new mother. There are three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods. Here, we compare these methods with our algorithm, which we call item-to-item collaborative filtering. Unlike traditional collaborative filtering, our algorithm's online computation scales independently of the number of customers and number of items in the product catalog. Our algorithm produces recommendations in real-time, scales to massive data sets, and generates high quality recommendations.

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Topics: Slope One (63%), Collaborative filtering (63%), Recommender system (62%) ...read more

4,085 Citations


Journal ArticleDOI: 10.1023/A:1021240730564
Robin Burke1Institutions (1)
Abstract: Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques To improve performance, these methods have sometimes been combined in hybrid recommenders This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants Further, we show that semantic ratings obtained from the knowledge-based part of the system enhance the effectiveness of collaborative filtering

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Topics: Recommender system (71%), Collaborative filtering (67%), Information filtering system (64%) ...read more

3,578 Citations


Open accessProceedings ArticleDOI: 10.1145/223904.223931
Upendra Shardanand1, Pattie Maes1Institutions (1)
01 May 1995-
Abstract: This paper describes a technique for making personalized recommendations from any type of database to a user based on similarities between the interest profile of that user and those of other users. In particular, we discuss the implementation of a networked system called Ringo, which makes personalized recommendations for music albums and artists. Ringo's database of users and artists grows dynamically as more people use the system and enter more information. Four different algorithms for making recommendations by using social information filtering were tested and compared. We present quantitative and qualitative results obtained from the use of Ringo by more than 2000 people.

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3,191 Citations


Journal ArticleDOI: 10.1023/A:1006544522159
Michael J. Pazzani1Institutions (1)
Abstract: We discuss learning a profile of user interests for recommending information sources such as Web pages or news articles. We describe the types of information available to determine whether to recommend a particular page to a particular user. This information includes the content of the page, the ratings of the user on other pages and the contents of these pages, the ratings given to that page by other users and the ratings of these other users on other pages and demographic information about users. We describe how each type of information may be used individually and then discuss an approach to combining recommendations from multiple sources. We illustrate each approach and the combined approach in the context of recommending restaurants.

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Topics: Information filtering system (55%), Web page (55%), Recommender system (54%) ...read more

1,450 Citations


Open accessProceedings ArticleDOI: 10.1145/336597.336662
Raymond J. Mooney1, Loriene Roy1Institutions (1)
01 Jun 2000-
Abstract: Recommender systems improve access to relevant products and information by making personalized suggestions based on previous examples of a user's likes and dislikes. Most existing recommender systems use collaborative filtering methods that base recommendations on other users' preferences. By contrast,content-based methods use information about an item itself to make suggestions.This approach has the advantage of being able to recommend previously unrated items to users with unique interests and to provide explanations for its recommendations. We describe a content-based book recommending system that utilizes information extraction and a machine-learning algorithm for text categorization. Initial experimental results demonstrate that this approach can produce accurate recommendations.

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Topics: Recommender system (61%), Collaborative filtering (59%), Information filtering system (55%) ...read more

1,246 Citations


Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20223
202114
202033
201934
201841
2017104

Top Attributes

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Topic's top 5 most impactful authors

Yuefeng Li

15 papers, 331 citations

Yue Xu

13 papers, 327 citations

Hidekazu Yanagimoto

10 papers, 9 citations

Ryotaro Kamimura

8 papers, 112 citations

Raymond Y. K. Lau

7 papers, 124 citations

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