Evaluating similarity measures: a large-scale study in the orkut social network

By Ellen Spertus, Mehran Sahami, Orkut Buyukkokten. Knowledge Discovery and Data Mining, 2005

358 citations recorded by the source corpus.

Subfields: Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/1e404c21c7a30a3a7a61e64a0ba2302c62f00386

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In Information Systems

In Statistical and Nonlinear Physics

Citations in the corpus

The YouTube video recommendation system (2010, background): “See [6] for an overview of possible choices.”
Efficient similarity joins for near duplicate detection (2008, background): “include document clustering [6], finding replicated Web collections [9], detecting plagiarism [20], community mining in a social network site [25], collaborative filtering [3] and discovering large dense graphs [15].”
Social Structure of Facebook Networks (2011, neutral): “, 2006], the recommendations of online groups [Spertus et al., 2005], online messages and friendship formation [Golder et al.”
Data Mining Methods for Recommender Systems (2015, background): “[69] did a large-scale study to evaluate six different similarity measures in the context of the Orkut social network.”
Make new friends, but keep the old: recommending people on social networking sites (2009, background): “[19] made recommendations of online communities to users based on their current community membership, and compared several different similarity measures in a large study on the social networking site Orkut.”
WHATSUP: A Decentralized Instant News Recommender (2013, background): “An evaluation of the performance of several metrics on the Orkut social network concluded that cosine similarity shows the best empirical results [24].”
EventAction (2019, background): “[70] presented an empirical evaluation of similarity measures for recommending online communities to social network users, where”
Mining user similarity based on location history (2008, background): “[17] present an extensive empirical comparison of six distinct measures of similarity for recommending online communities to members of the Orkut social network.”
Efficient parallel set-similarity joins using MapReduce (2010, methodology): “tecting near duplicate web-pages in web crawling [14], document clustering[5], plagiarism detection [15], master data management, making recommendations to users based on their similarity to other users in query refinement [22], mining in social networking sites [25], and identifying coalitions of c”
Personal recommendation using deep recurrent neural networks in NetEase (2016, neutral): “social network [11][22].”
Social Network Sites: Definition, History, and Scholarship (2007, neutral): “Based on Orkut data, Spertus, Sahami, and Buyukkokten (2005) identified a topology of users through their membership in certain communities; they suggest that sites can use this to recommend additional communities of interest to users.”