Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization

By Bamshad Mobasher, Honghua (Kathy) Dai, Tao Luo, Miki Nakagawa. Data mining and knowledge discovery, 2004

482 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/a70ef8a891712b4c30e7716c0828ae0396f6f67d

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In Computer Networks and Communications

In Information Systems

Citations in the corpus

Data Mining for Web Personalization (2007, methodology): “One suchmetric is the The F1 measure which is computed as the harmonic mean of precision and recall [9, 85].”
A Comprehensive Survey of Neighborhood-based Recommendation Methods (2011, background): “recommendation of books and CDs [47, 53], music [45, 70], movies [31, 51, 5], news [6, 41, 76], jokes [23], and web pages [7, 52].”
Mining interesting knowledge from weblogs: a survey (2005, methodology): “[57] combines association rule mining and clustering into a method called association rule hypergraph partitioning.”
Recommender Systems (2016, unknown): “Sequential pattern-mining methods for predicting accesses in Web logs are discussed in [208, 440, 442, 443, 562].”
Hybrid Web Recommender Systems (2007, background): “These longer multiple-visit profiles are somewhat less reliable as user profiles because they are collated using IP address alone [31].”
Privacy via the Johnson-Lindenstrauss Transform (2012, background): “There is convincing evidence that segmentation of users into clusters is effective in some contexts [5, 35, 37, 44].”
Real-time prediction of online shoppers’ purchasing intention using multilayer perceptron and LSTM recurrent neural networks (2018, methodology): “[8] set up two different clustering models based on user transactions and pageviews to derive useful aggregate”
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions (2005, methodology): “…recommender systems, advanced profiling techniques that are based on data mining have been used mainly in the context of Web usage analysis [59], [68], [110], i.e., to discover the navigational Web usage patterns (i.e., page view sequences) of users in order to provide better Web site…”