Improved collaborative filtering recommendations using quantitative implication rules mining in implication field

Nguyen, Hoang; Huynh, Hung Huu; Phan, Lan Phuong; Huynh, Hiep Xuan

doi:10.1145/3310986.3310996

Cited by 3 publications

(4 citation statements)

References 21 publications

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“…Nguyen et al [27] used collaborative filtering for quantitative association rules to build a recommendation system. A solution has been presented to discover association rules on binary data and to support quantitative data.…”

Section: Related Workmentioning

confidence: 99%

Improving Association Rules Accuracy in Noisy Domains Using Instance Reduction Techniques

Al-Akhras¹,

Darwish²,

Atawneh³

et al. 2022

Computers, Materials &Amp; Continua

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Association rules' learning is a machine learning method used in finding underlying associations in large datasets. Whether intentionally or unintentionally present, noise in training instances causes overfitting while building the classifier and negatively impacts classification accuracy. This paper uses instance reduction techniques for the datasets before mining the association rules and building the classifier. Instance reduction techniques were originally developed to reduce memory requirements in instance-based learning. This paper utilizes them to remove noise from the dataset before training the association rules classifier. Extensive experiments were conducted to assess the accuracy of association rules with different instance reduction techniques, namely: Decremental Reduction Optimization Procedure (DROP) 3, DROP5, ALL K-Nearest Neighbors (ALLKNN), Edited Nearest Neighbor (ENN), and Repeated Edited Nearest Neighbor (RENN) in different noise ratios. Experiments show that instance reduction techniques substantially improved the average classification accuracy on three different noise levels: 0%, 5%, and 10%. The RENN algorithm achieved the highest levels of accuracy with a significant improvement on seven out of eight used datasets from the University of California Irvine (UCI) machine learning repository. The improvements were more apparent in the 5% and the 10% noise cases. When RENN was applied, the average classification accuracy for the eight datasets in the zero-noise test enhanced from 70.47% to 76.65% compared to the original test. The average accuracy was improved from 66.08% to 77.47% for the 5%-noise case and from 59.89% to 77.59% in the 10%-noise case. Higher confidence was also reported in building the association rules when RENN was used. The above results indicate that RENN is a good solution in removing noise and avoiding overfitting during the construction of the association rules classifier, especially in noisy domains.

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Section: Related Workmentioning

confidence: 99%

Improving Association Rules Accuracy in Noisy Domains Using Instance Reduction Techniques

Al-Akhras¹,

Darwish²,

Atawneh³

et al. 2022

Computers, Materials &Amp; Continua

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“…AR-Based RecSys: in this category, under the conditions of data sparsity, cold-start, scalability, and low accuracy problems, a wide range of recommender systems are built by employing only the association rules to overcome all these issues [6][7][8][9][10][11][12] where no rating system is needed. Additionally, other naive RecSys problems are also able to be in this category, like the "overfitting" where e.g.…”

Section: Solutions Taxonomy For the Naive Recsys Issuesmentioning

confidence: 99%

“…This prediction is done by various techniques such as collaborative filtering, clustering, and other data mining techniques 1,2 that interpret customer's shopping behaviors and generating personalized recommendations 3,4 . However, most of the mentioned techniques rely on the use of online product purchases and ratings made by the user, and although such methods generally show good performance, in high dimensional data sets they suffer from major problems such as the data sparsity and scalability [2][3][4][5][6][7][8][9][10][11][12][13] . In such cases, researchers used sampling or dimensionality reduction meth-ods to overcome these drawbacks.…”

Section: Introductionmentioning

confidence: 99%

“…similarities (items are similar because they are in the same category, or preferred by similar customers), deduction (selling items together, makes other items most likely to be sold), or other relations, it is possible to produce a list of items that are relevant, and be suggested to the customer who shows any interesting behaviors in those relevant items. Figure1 illustrates an example formulated from the knowledge gained during the process of accomplishment of this paper, where recommendations are generated by using associated items of rules discovered in the transactions database 6,10 . Therefore, this paper extracts new knowledge by surveying published literature in the fields of RecSys, AR, and the intersection area between the two fields.…”

Section: Introductionmentioning

confidence: 99%

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Association rules and offline-data-based recommender systems

Sweidan

2020

Developments of Artificial Intelligence Technologies in Computation and Robotics

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Association rules are rules that define relationships between items in sales databases. They have been used primarily to organize relevant products in stores in a way to makes them more visible to consumers, which may increase sales and profits. On the other hand, it has been rarely used in recommender systems where algorithms provide instant recommendations by processing consumers' interests that are gathered when browsing online. However, the vast amount of information collected from transaction data saved on backup servers is poorly taken advantage of, because it is not connected to the Internet, although interesting and personalized recommendations can be created after finding the collections of most frequent items, or most interesting rules in such databases. In this paper, we do a critique of the existing research on both recommender systems along with showing their drawbacks, and the association rules with detailed explanations on their advantages. Finally, draw up with several solutions for producing high quality as well as accurate recommendations by applying novel combinations of techniques observed in this research area including the association-rules-based recommender systems.

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Recommendation with Subjective Tendency Based on Statistical Implicative Analysis

Huynh

Phan

Tran

et al. 2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Improved collaborative filtering recommendations using quantitative implication rules mining in implication field

Cited by 3 publications

References 21 publications

Improving Association Rules Accuracy in Noisy Domains Using Instance Reduction Techniques

Improving Association Rules Accuracy in Noisy Domains Using Instance Reduction Techniques

Association rules and offline-data-based recommender systems

Recommendation with Subjective Tendency Based on Statistical Implicative Analysis

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