Effective personalization based on association rule discovery from web usage data

By Bamshad Mobasher, Honghua (Kathy) Dai, Tao Luo, Miki Nakagawa. 2001

543 citations recorded by the source corpus.

Subfields: Information Systems, Signal Processing

How researchers see this paper

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

In Information Systems

In Signal Processing

Citations in the corpus

A Survey on Session-based Recommender Systems (2019, background): “Finally, if the conditional probability 𝑃 ( ˆ 𝑣 | ˆ 𝑠 ) is greater than a predefined confidence threshold, then ˆ 𝑣 is added into the recommendation list [77, 124].”
Goodbye Pareto Principle, Hello Long Tail: The Effect of Search Costs on the Concentration of Product Sales (2011, background): “If a consumer clicks on one of the products being recommended, the recommendation system enables the consumer to explore and discover products that she otherwise may not have found and, hence, provides new information (Mobasher et al. 2001).”
Automatic Recommendations for E-Learning Personalization Based on Web Usage Mining Techniques and Information Retrieval (2008, background): “Precision measure represents the proportion of relevant recommendations to the total number of obtained recommendations, whereas Recall measure or coverage represents the proportion of relevant recommendations to all learning objects that should be recommended (Mobasher et al., 2001).”
Data Mining for Web Personalization (2007, methodology): “Although not as widely used as clustering for Web personalization, the results of association rule mining on the user proﬁle and items space can result in models that, in conjunction with the activity or proﬁle of a target user, can be used for recommendation generation [39, 71, 83, 115].”
Empirical Analysis of the Impact of Recommender Systems on Sales (2010, background): “, [30]) have focused on the ability to recommend relatively unknown items that would otherwise be missed by users.”
Introduction to the special topic section on mining Web resources for enhancing information retrieval (2007, methodology): “Association rule mining is a technique used to capture the relationships among items on the basis of their cooccurrence patterns across transactions ( Mobasher, Dai, Luo, & Nakagawa, 2001 ).”
Data Mining Methods for Recommender Systems (2015, background): “[53] present a system for web personalization based on association rules mining.”
Evaluating collaborative filtering recommender systems (2004, background): “A range of research and systems have looked at measures including the degree to which the recommendations cover the entire set of items [Mobasher et al. 2001], the degree to which recommendations made are nonobvious [McNee et al.”
Recommender Systems (2016, unknown): “Association rule-based systems have found significant uses in Web-based personalization and recommender systems [441, 552].”