Automatic personalization based on Web usage mining

By Bamshad Mobasher, Robert Cooley, Jaideep Srivastava. 2000

1,521 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

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In Computer Networks and Communications

In Information Systems

Citations in the corpus

Poisoning Attacks to Graph-Based Recommender Systems (2018, methodology): “Depending on the techniques used to capture the correlations between users’ behavior data, collaborative filtering based recommender systems can further include matrix-factorization-based [17], association-rule-based [6, 22], and graph-based [7] recommender systems.”
Mining interesting knowledge from weblogs: a survey (2005, background): “This problem can be partially solved by the use of cookies [26], by URL rewriting [27], or by requiring the user to log in when entering the Web site [21].”
Effective Attack Models for Shilling Item-Based Collaborative Filtering Systems (2005, methodology): “Users tend to find the answers they are looking for with Google [10], so we decide to use the Google’s rank as the public page score.”
Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization (2004, background): “A general framework for personalization based on aggregate usage profiles is depicted in figure 1 (Mobasher et al., 2000).”
A personalized recommender system based on web usage mining and decision tree induction (2002, methodology): “The pattern discovery tasks involve the discovery of association rules, sequential patterns, usage clusters, page clusters, user classifications or any other pattern discovery method (Mobasher et al., 2000a,b).”
Web Usage Mining as a Tool for Personalization: A Survey (2003, methodology): “The construction of usage clusters was done with the Association Rule Hypergraph Partition (ARHP) technique (Han et al., 1997), which does not require distance computations (Mobasher et al., 1999b).”
Measuring Online Social Bubbles (2015, background): “One important result of this process has been 14 higher personalization (Mobasher et al., 2000) — people see more content tailored specifically to them 15 based on their past behaviors or social networks.”
Automatic Recommendations for E-Learning Personalization Based on Web Usage Mining Techniques and Information Retrieval (2008, methodology): “(Mobasher et al., 2000) used a multivariate K-Mmeans algorithm to obtain transaction clusters and the Association Rule Hypergraph Partitioning (ARHP) technique to obtain usage clusters.”
The impact of customer interface quality, satisfaction and switching costs on e-loyalty: Internet experience as a moderator (2008, background): “For example, Mobasher, Cooley, and Srivastava (2000) find that Internet firms have a growing ability to tailor their customer interfaces and services to specific needs via personalization technologies; it is hoped that these technologies will enhance customer lock in and retention.”
Recommender Systems (2016, neutral): “Sequential pattern-mining methods for predicting accesses in Web logs are discussed in [208, 440, 442, 443, 562].”
Learning Analytics: Definitions, Processes and Potential (2011, background): “It also demonstrates the use of increasingly complex computer-mediated data-tracking, capture and modelling to meet the current needs and predict the future needs of their customers (Cho et al., 2002; Mobasher et al., 2000; Wang & Ren, 2009).”
Measuring Switching Costs and the Determinants of Customer Retention in Internet-Enabled Businesses: A Study of the Online Brokerage Industry (2002, background): “Internet firms are increasingly able to tailor their customer interface and services to specific needs through personalization technologies—it is hoped that these technologies will build greater customer lock in and retention (Crosby and Stephens 1987, Pearson 1998, Mobasher et al. 2000, Cingil et”
PocketLens: Toward a personal recommender system (2004, background): “In addition, some systems try to make sense of shoppers browsing behavior by analyzing the web log and looking at the pattern of pages each user visits [Mobasher et al. 2000].”
Measuring personalization of web search (2013, background): “ry B, the results for B may be in˚uenced by the previous search for A. Prior research on user intent while searching has shown that sequential queries from a user are useful for re˙ning search result [1, 7, 35, 49, 55]. Thus, it is not surprising that some search engines implement query re˙nement us”