Using collaborative filtering to weave an information tapestry

By David Goldberg, D. Nichols, B. Oki, D. Terry. 1992

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

On the Effectiveness of Linear Models for One-Class Collaborative Filtering (2016, background): “Collaborative filtering (CF) is the de facto approach to algorithmic recommendation, based on extracted information from a database of preferences for a collection of users (Goldberg et al. 1992).”
Exploring social influence for recommendation: a generative model approach (2012, background): “Collaborative filtering [4, 9] recommends items”
The Structure and Function of Complex Networks (2003, background): “Networks of this kind form the basis for collaborative filtering algorithms and recommender systems, which are techniques for predicting new likes or dislikes based on comparison of individuals’ preferences with those of others [176, 352, 367].”
Privacy-preserving collaborative filtering using randomized perturbation techniques (2003, methodology): “The term “collaborative filtering” was first coined by the designers of Tapestry [7], a mail filtering software developed in the early nineties for the intranet at the Xerox Palo Alto Research Center.”
Challenges and Solutions of Ubiquitous User Modeling (2012, background): “Agnar and Plaza [3] described the general case-based approach as utilizing the specific knowledge of previously experienced, concrete problem situations (cases) where these has been “captured” in a way that enables the system to recognize similar cases later.”
A Survey of Collaborative Filtering Techniques (2009, background): “The developers of one of the first recommender systems, Tapestry [1] (other earlier recommendation systems include rule-based recommenders and user-customization), coined the phrase “collaborative filtering (CF),” which has been widely adopted regardless of the facts that recommenders may not explic”
Synthesis Lectures on Human Language Technologies (2009, unknown): “Collaborative filtering is commonly used in recommender systems (Goldberg et al., 1992), where the preference information of a user group is tracked by comparing the statistical similarity of the selected items of one user with the items selected by another user.”
Social media recommendation based on people and tags (2010, background): “There are two prevalent approaches for building recommender systems: content-based (CB) [26] and collaborative filtering (CF) [13].”
Collaborative Filtering beyond the User-Item Matrix (2014, methodology): “2 The most successful and widely used recommendation technique is collaborative filtering (CF), which is based on the core assumption that users who have expressed similar interests in the past will share common interests in the future [Goldberg et al. 1992].”
An alternating direction algorithm for matrix completion with nonnegative factors (2011, methodology): “…applications including collaborative ﬁltering , which is used by Netﬂix to infer individual preference from an incomplete set of user preferences [13], global positioning , which discovers the positions of nodes in a network from incomplete pair-wise distances [3], system identiﬁcation and order…”
SimRank: a measure of structural-context similarity (2002, background): “The process of making recommendations to a user based on preference or purchase data from other users is known as collaborative filtering [7, 11, 20].”
Shilling recommender systems for fun and profit (2004, background): “1.1 Related Work 1.1.1 Recommender Systems One of the earliest instances of a collaborative .ltering based recommender system is Tapestry [6].”
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions (2005, methodology): “Later on, the Tapestry system relied on each user to identify like-minded users manually [38].”
Recommender systems (1997, background): “The developers of the first recommender system, Tapestry [1], coined the phrase “collaborative filtering” and several others have adopted it.”
CityTransfer (2018, methodology): “Among the numerous recommendation methods, collaborating filtering (CF) [6,13,22,35] has shown high performance in many recommendation systems.”
Finding interesting associations without support pruning (2000, background): “Not only are the support-free associations a natural class of patterns for data mining in their own right, they also arise in a variety of applications such as: copy detection| identifying identical or similar documents and web pages [4, 13]; clustering | identifying similar vectors in high-dimensio”