Recommender system based on pairwise association rules

An exorbitant source of data is easily available but the actual task lies in using this data efficiently. In this article, the aim is to analyse the significant information embedded in the customer purchase behaviour to recommend new products to them. Our proposed scheme is a two-fold approach. First, the authors retrieve various product correlations from the vast library of user transactions. Based on these product correlations, utility based association rules are learned which depict the customer purchase behaviour. These rules are then applied in a recommender system for novel product suggestions to the customers. With improved utility based mining the paper tries to incorporate the usefulness of an item set like cost, profit or any other factor along with their frequency. In this paper the authors have deployed the rules discovered from both the conventional Frequent Item Set Mining and Improved Utility Based Mining on an e-commerce platform to compare the accuracy of the algorithms. The obtained results establish the efficacy of the proposed algorithm.

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A recommender system based on car pairwise comparisons on a mobile application using association rules

2016 IEEE International Conference on Industrial Technology (ICIT) ◽

10.1109/icit.2016.7474952 ◽

2016 ◽

Cited By ~ 1

Author(s):

Jei-Zheng Wu ◽

Hsiu-Wen Liu ◽

Fang-Lin Wu

Keyword(s):

Association Rules ◽

Recommender System ◽

Mobile Application ◽

Pairwise Comparisons

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Evolving temporal association rules in recommender system

Neural Computing and Applications ◽

10.1007/s00521-017-3217-z ◽

2017 ◽

Vol 31 (7) ◽

pp. 2605-2619 ◽

Cited By ~ 2

Author(s):

Huiyu Zhou ◽

Kotaro Hirasawa

Keyword(s):

Association Rules ◽

Recommender System ◽

Temporal Association

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Association Rules-Based Gray Relational Approach for E-Commerce Recommender System

Advances in Business Information Systems and Analytics - Machine Learning Techniques for Improved Business Analytics ◽

10.4018/978-1-5225-3534-8.ch004 ◽

2019 ◽

pp. 64-93

Author(s):

Hüseyin Fidan

Keyword(s):

Recommender Systems ◽

Association Rules ◽

Recommender System ◽

Gray Relational Analysis ◽

Insufficient Data ◽

Apriori Algorithm ◽

Insufficient Information ◽

Relational Approach ◽

Relational Analysis

Recommender systems cannot provide healthy results in case of similar products that cannot be identified in e-commerce sites. Insufficient information about users or items is one of the most crucial problems, especially with adding new users or products. The inability to perform relational analysis in the system is due to insufficient data. In this case, the system cannot recommend or bring the non-related items to the users. This chapter suggests the gray relational approach to identify more healthy recommendation lists when there are few relational items. The data was obtained from an e-commerce company and apriori algorithm was applied to the dataset that a randomly chosen user purchased. Gray relational analysis was applied for the most suitable recommendation by using support, confidence, number of likes, adding favorite, deleting from basket, and return information of the products in the dataset. In addition, the most appropriate product sequencing of the recommendation list was realized by gray relational degrees.

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Improved Personalized Recommendation based on Causal Association Rule and Collaborative Filtering

International Journal of Distance Education Technologies ◽

10.4018/ijdet.2016070102 ◽

2016 ◽

Vol 14 (3) ◽

pp. 21-33 ◽

Cited By ~ 6

Author(s):

Wu Lei ◽

Fang Qing ◽

Jin Zhou

Keyword(s):

Data Mining ◽

Association Rules ◽

Recommender System ◽

Association Rule ◽

Nearest Neighbor ◽

Personalized Recommendation ◽

User Evaluation ◽

Causal Association ◽

Rating Prediction ◽

Preference Matrix

There are usually limited user evaluation of resources on a recommender system, which caused an extremely sparse user rating matrix, and this greatly reduce the accuracy of personalized recommendation, especially for new users or new items. This paper presents a recommendation method based on rating prediction using causal association rules. First, users and items are mapped into two feature vectors, which would be minded later to get the causal association rules from the perspective of data mining; then based on the casual association rules, the authors create a preference matrix which would predict the rating of the items that users have not rated; finally a nearest neighbor similarity measure method is designed for personalized recommendation. Experiment shows that the algorithm efficiently improves the recommendation comparing to traditional methods.

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