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953
The structure and function of complex networks
- SIAM REVIEW
, 2003
"... Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, ..."
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Cited by 2600 (7 self)
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Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
GroupLens: An Open Architecture for Collaborative Filtering of Netnews
, 1994
"... Collaborative filters help people make choices based on the opinions of other people. GroupLens is a system for collaborative filtering of netnews, to help people find articles they will like in the huge stream of available articles. News reader clients display predicted scores and make it easy for ..."
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Cited by 1548 (41 self)
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Collaborative filters help people make choices based on the opinions of other people. GroupLens is a system for collaborative filtering of netnews, to help people find articles they will like in the huge stream of available articles. News reader clients display predicted scores and make it easy for users to rate articles after they read them. Rating servers, called Better Bit Bureaus, gather and disseminate the ratings. The rating servers predict scores based on the heuristic that people who agreed in the past will probably agree again. Users can protect their privacy by entering ratings under pseudonyms, without reducing the effectiveness of the score prediction. The entire architecture is open: alternative software for news clients and Better Bit Bureaus can be developed independently and can interoperate with the components we have developed.
Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions
- IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
, 2005
"... 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 vario ..."
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Cited by 1490 (23 self)
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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 multcriteria ratings, and a provision of more flexible and less intrusive types of recommendations.
Item-based Collaborative Filtering Recommendation Algorithms
- PROC. 10TH INTERNATIONAL CONFERENCE ON THE WORLD WIDE WEB
, 2001
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Social Information Filtering: Algorithms for Automating "Word of Mouth"
, 1995
"... 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 personal ..."
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Cited by 1159 (20 self)
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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.
Evaluating collaborative filtering recommender systems
- ACM TRANSACTIONS ON INFORMATION SYSTEMS
, 2004
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Grouplens: Applying collaborative filtering to usenet news
- COMMUNICATIONS OF THE ACM
, 1997
"... ... a collaborative filtering system for Usenet news—a high-volume, high-turnover discussion list service on the Internet. Usenet newsgroups—the individual discussion lists—may carry hundreds of messages each day. While in theory the newsgroup organization allows readers to select the content that m ..."
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Cited by 803 (18 self)
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... a collaborative filtering system for Usenet news—a high-volume, high-turnover discussion list service on the Internet. Usenet newsgroups—the individual discussion lists—may carry hundreds of messages each day. While in theory the newsgroup organization allows readers to select the content that most interests them, in practice most newsgroups carry a wide enough spread of messages to make most individuals consider Usenet news to be a high noise information resource. Furthermore, each user values a different set of messages. Both taste and prior knowledge are major factors in evaluating news articles. For example, readers of the rec.humor newsgroup, a group designed for jokes and other humorous postings, value articles based on whether they perceive them to be funny. Readers of technical groups, such as comp.lang.c� � value articles based
NewsWeeder: Learning to Filter Netnews
- in Proceedings of the 12th International Machine Learning Conference (ML95
, 1995
"... A significant problem in many information filtering systems is the dependence on the user for the creation and maintenance of a user profile, which describes the user's interests. NewsWeeder is a netnews-filtering system that addresses this problem by letting the user rate his or her interest l ..."
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Cited by 561 (0 self)
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A significant problem in many information filtering systems is the dependence on the user for the creation and maintenance of a user profile, which describes the user's interests. NewsWeeder is a netnews-filtering system that addresses this problem by letting the user rate his or her interest level for each article being read (1-5), and then learning a user profile based on these ratings. This paper describes how NewsWeeder accomplishes this task, and examines the alternative learning methods used. The results show that a learning algorithm based on the Minimum Description Length (MDL) principle was able to raise the percentage of interesting articles to be shown to users from 14% to 52% on average. Further, this performance significantly outperformed (by 21%) one of the most successful techniques in Information Retrieval (IR), termfrequency /inverse-document-frequency (tf-idf) weighting. 1
Factorization meets the neighborhood: a multifaceted collaborative filtering model
- In Proc. of the 14th ACM SIGKDD conference
, 2008
"... Recommender systems provide users with personalized suggestions for products or services. These systems often rely on Collaborating Filtering (CF), where past transactions are analyzed in order to establish connections between users and products. The two more successful approaches to CF are latent f ..."
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Cited by 424 (12 self)
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Recommender systems provide users with personalized suggestions for products or services. These systems often rely on Collaborating Filtering (CF), where past transactions are analyzed in order to establish connections between users and products. The two more successful approaches to CF are latent factor models, which directly profile both users and products, and neighborhood models, which analyze similarities between products or users. In this work we introduce some innovations to both approaches. The factor and neighborhood models can now be smoothly merged, thereby building a more accurate combined model. Further accuracy improvements are achieved by extending the models to exploit both explicit and implicit feedback by the users. The methods are tested on the Netflix data. Results are better than those previously published on that dataset. In addition, we suggest a new evaluation metric, which highlights the differences among methods, based on their performance at a top-K recommendation task.
SimRank: A Measure of Structural-Context Similarity
- In KDD
, 2002
"... The problem of measuring "similarity" of objects arises in many applications, and many domain-specific measures have been developed, e.g., matching text across documents or computing overlap among item-sets. We propose a complementary approach, applicable in any domain with object-to- ..."
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Cited by 387 (3 self)
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The problem of measuring "similarity" of objects arises in many applications, and many domain-specific measures have been developed, e.g., matching text across documents or computing overlap among item-sets. We propose a complementary approach, applicable in any domain with object-to-object relationships, that measures similarity of the structural context in which objects occur, based on their relationships with other objects. Effectively, we compute a measure that says "two objects are similar if they are related to similar objects." This general similarity measure, called SimRank, is based on a simple and intuitive graph-theoretic model. For a given domain, SimRank can be combined with other domain-specific similarity measures. We suggest techniques for efficient computation of SimRank scores, and provide experimental results on two application domains showing the computational feasibility and effectiveness of our approach.
