Efficient Adaptive-Support Association Rule Mining for Recommender Systems

By Weiyang Lin, Sergio A. Alvarez, Carolina Ruiz. 2004

469 citations recorded by the source corpus.

Subfields: Information Systems, Marketing

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Citations in the corpus

Recommender Systems (2016, unknown): “The first two of these methods leverage user associations rather than item associations [358, 359].”
Mining interesting knowledge from weblogs: a survey (2005, methodology): “[96] Personalization Association rules Web Server [57] Personalization Association rules, Clustering Web Server [50] Personalization Clustering Web Server [97] Personalization Clustering, Sequential patterns Web Server”
Sequential Event Prediction with Association Rules (2011, unknown): “Lin et al. (2002) also construct a recommender system using rules, having a minimum confidence threshold and then an adjustable minimum support threshold.”
Recommender systems in e-commerce (2014, background): “This means that whenever products from X exist in a transaction T, there is high likelihood that products from Y will also exist in the same transaction [11] [14].”
Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering (2004, methodology): “Cluster models, Bayesian Network models, and specialized association-rule algorithms, among other techniques, have been used for this identi.cation purpose [Breese et al. 1998; Lin et al. 2002].”
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].”
Data Mining Methods for Recommender Systems (2015, methodology): “[49] present a new association mining algorithm that adjusts the minimum support of the rules during mining in order to obtain an appropriate number of significant rule therefore addressing some of the shortcomings of previous algorithms such as the a priori.”
Learning Hierarchical Representation Model for NextBasket Recommendation (2015, methodology): “• F1-score: F1-score is the harmonic mean of precision and recall, which is a widely used measure in recommendation [9, 15, 23]:”
A Distributed, Architecture-Centric Approach to Computing Accurate Recommendations from Very Large and Sparse Datasets (2011, methodology): “The association rules approach applies rule discovery algorithms to detect rating patterns and associations among items [10,90,93,142].”
Learning theory analysis for association rules and sequential event prediction (2013, methodology): “In practice, the choice of K can be determined in several ways: K can be manually determined (for instance by the customer), it can be set using side information as considered by McCormick et al. (2012), or it can be set via cross-validation on an extra hold-out set.”