A novel approach to hybrid recommendation systems based on association rules mining for content recommendation in asynchronous discussion groups

Kardan, Ahmad A.; Ebrahimi, Mahnaz

doi:10.1016/j.ins.2012.07.011

Cited by 111 publications

(62 citation statements)

References 41 publications

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“…Firstly, association rules mining technique is considered as similarity measures to determine similarity between users or between items in recommendation system. Examples of this are as follows: association rules mining technique employed in a hybrid recommendation system to compute similarity among/between users from implicit data collected in a discussion group [15]; association rules mining used for identifying similar listening history among users on the same set of songs and predicting users' unknown preferences [18]; association rules mining technique employed to find out the similarities between readers, between articles, and combine them together to generate recommendation results [29]. Secondly, association rules mining technique is considered as data mining technique to process in recommendation system.…”

Section: Related Workmentioning

confidence: 99%

Collaborative Recommenderation Based on Statistical Implication Rules

Phan

Dang

Huynh³

2018

JCC

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Abstract. In recent researches, many approaches based on association rules have been proposed to improve the accuracy of recommender systems. These approaches are primarily based on Apriori data mining algorithm in order to generate the association rules and apply them to improving the recommendation results. However, these approaches also reveal some disadvantages of the system, such as taking a longer time for generating association rules; applying the Apriori algorithm on rating sparse matrix resulting in irrelevant information and causing poor recommendation results to target users and association rules generated primarily relying on given threshold of Support and Confidence measures leading to the focus on the majority of rules and ignoring the astonishment of rules to affect the recommendation results. In this study, we propose a new model for collaborative filtering recommender systems: The collaborative recommendation is based on statistical implication rules (IIR); Differently from collaborative recommendation based on association rules (AR), the IIR predicts the items for users based on statistical implication rules generated from rating matrix and Implication intensity measures measuring the surprisingness of rules. To evaluate the effectiveness of the model, in the experimental section, we implement the model on three real datasets and compare the results with some different effective models. The results show that the IIR has higher precision on the experimental datasets.

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Section: Related Workmentioning

confidence: 99%

Collaborative Recommenderation Based on Statistical Implication Rules

Phan

Dang

Huynh³

2018

JCC

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“…In order to overcome the problems of the above recommendation algorithms and exploit their respective advantages, many scholars studied hybrid recommendation algorithms that fuse multiple recommendation techniques [14,15]. The hybrid recommendation algorithms can be divided into the following categories, according to the fusion degree: (1) the recommendation results based on CBF and CF are implemented respectively.…”

Section: The Recommendation System and Hybrid Algorithmmentioning

confidence: 99%

An Interactive Personalized Recommendation System Using the Hybrid Algorithm Model

Guo

Wang

2017

Symmetry

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Abstract:With the rapid development of e-commerce, the contradiction between the disorder of business information and customer demand is increasingly prominent. This study aims to make e-commerce shopping more convenient, and avoid information overload, by an interactive personalized recommendation system using the hybrid algorithm model. The proposed model first uses various recommendation algorithms to get a list of original recommendation results. Combined with the customer's feedback in an interactive manner, it then establishes the weights of corresponding recommendation algorithms. Finally, the synthetic formula of evidence theory is used to fuse the original results to obtain the final recommendation products. The recommendation performance of the proposed method is compared with that of traditional methods. The results of the experimental study through a Taobao online dress shop clearly show that the proposed method increases the efficiency of data mining in the consumer coverage, the consumer discovery accuracy and the recommendation recall. The hybrid recommendation algorithm complements the advantages of the existing recommendation algorithms in data mining. The interactive assigned-weight method meets consumer demand better and solves the problem of information overload. Meanwhile, our study offers important implications for e-commerce platform providers regarding the design of product recommendation systems.

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“…Association rule mining is finding applications in behavior analysis [16], such as plagiarism detection [17], students' enrollment [18], and recommendation [19].…”

Section: Association Rule Miningmentioning

confidence: 99%

Smartphone App Usage Log Mining

Tseng¹,

Hsu²

2014

IJCEE

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Abstract-The rise of smartphones can be connected to the large number of applications, or apps, which can be installed and run by users on smartphones. It becomes important for researchers, smartphone designers, and application developers to know how users use apps on their smartphones. The aim of this paper is to present our work where data mining is applied to smartphone app usage log data with a focus on data preparation. The methods include association rule mining and sequential pattern mining. The results are the discovered rules and patterns that reflect users' real-life app-using behaviors; they can lead to improved user interfaces, and they can also be used to reconstruct the context in which a user used his or her smartphone. We demonstrate an application of data mining that can have an impact on the smartphone industry, and we also demonstrate a data-driven approach that can help us know more about how users use apps on their smartphones.Index Terms-Association rule mining, sequential pattern mining, smartphone app usage log.

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A novel approach to hybrid recommendation systems based on association rules mining for content recommendation in asynchronous discussion groups

Cited by 111 publications

References 41 publications

Collaborative Recommenderation Based on Statistical Implication Rules

Collaborative Recommenderation Based on Statistical Implication Rules

An Interactive Personalized Recommendation System Using the Hybrid Algorithm Model

Smartphone App Usage Log Mining

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