A personalized recommender system based on web usage mining and decision tree induction

By Yoon Ho Cho, Jae Kyeong Kim, Soung Hie Kim. 2002

498 citations recorded by the source corpus.

Subfields: Information Systems, Marketing

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Citations in the corpus

Explainable Recommendation: A Survey and New Perspectives (2018, background): “[2001] leveraged association rule mining for efficient web page recommendation at large-scale; Cho et al. [2002] combined decision trees and association rule mining for a webbased shop recommender system; Smyth et al.”
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).”
Who is talking? An ontology-based opinion leader identification framework for word-of-mouth marketing in online social blogs (2011, methodology): “One of the most successful recommendation methods in online platforms is the completion of recommendations by the collaborative filtering of the data of a large number of participants [9,50].”
Data Mining Methods for Recommender Systems (2015, methodology): “[18], for instance, present a RS for online purchases that combines the use of Association Rules (see Section 2.”
Recurrent neural network based language model personalization by social network crowdsourcing (2013, background): “Personalization, which is an indispensable aspect in several real world applications nowadays, has been studied in a wide range of research ﬁelds, such as personalized web search [1, 2] and recommendation systems [3, 4, 5, 6].”
Learning analytics: envisioning a research discipline and a domain of practice (2012, background): “To advance as a field, LA researchers and practitioners need to address the following: (a) development of new tools, techniques, and people; (b) data: openness, ethics, and scope; (c) target of analytics activity; and (d) connections to related fields and practitioners.”
An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion (2022, background): “Personalized models are typically found in the realms of recommendation systems (Ben-hamdi et al., 2017; Amat et al., 2018; Martinez et al., 2009; Cho et al., 2002) or in federated learning (Mansour et al., 2020; Jiang et al., 2019; Fallah et al., 2020; Shamsian et al., 2021).”