A hybrid recommender system for an online store using a fuzzy expert system

Walek, Bogdan; Fajmon, Petr

doi:10.1016/j.eswa.2022.118565

Cited by 50 publications

(18 citation statements)

References 24 publications

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“…A recommender system is an information system of user decision-making that can recommend information and services based on the preferences of each individual user [13,61]. The principles of recommender systems address issues of information overload and complex decision-making by using information filtering to support data prediction and to recommend user alternatives in numerous interest areas [62]: for instance, agriculture [36,63], logistics [64], product [13,65], and society [66,67].…”

Section: Recommender System In Educationmentioning

confidence: 99%

“…By combining the digital number of 32 criteria bits from the example in Figure 4, the binary number 01000000000101110000000010011100 represents the integer value 107,524,930,81010. Conversion from binary to base-10 is: 010000000001011100000000100111002 = (0 × 2 31 ) + (1 × 2 30 ) + (0 × 2 29 ) + (0 × 2 28 ) + (0 × 2 27 ) + (0 × 2 26 ) + (0 × 2 25 ) + (0 × 2 24 ) + (0 × 2 23 ) + (0 × 2 22 ) + (0 × 2 21 ) + (1 × 2 20 ) + (0 × 2 19 ) + (1 × 2 18 ) + (1 × 2 17 ) + (1 × 2 16 ) + (0 × 2 15 ) + (0 × 2 14 ) + (0 × 2 13 ) + (0 × 2 12 ) + (0 × 2 11 ) + (0 × 2 10 ) + (0 × 2 9 ) + (0 × 2 8 ) + (1 × 2 7 ) + (0 × 2 6 ) + (0 × 2 5 ) + (1 × 2 4 ) + (1 × 2 3 ) + (1 × 2 2 ) + (0 × 2 1 ) + (0 × 2 0 ) = 107524930810.…”

Section: Samplesmentioning

confidence: 99%

“…A recommender system contributes to the development of an original application and offers users subsequent actions based on a variety of factors and choices, such as user interests or preferences and user history [11,12]. The recommender system has become a valuable tool for resolving the problem of information overload, enabling users to make difficult decisions and meeting their needs in a variety of solutions, including digital commerce [13], finance [14], entertainment (film [15], music [16], and television shows [17]), travel [18], healthcare [19], energy [20], and education [5,21].…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Multi-Criteria Decision Making of Graduate Admission Recommender System: AHP and Fuzzy AHP Approaches

2023

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The optimal management of personal resources impacts everyone’s quality of life. An investment in graduate education is a sustainable opportunity for improved outcomes in human life, including cognition, behavior, life opportunities, salary, and career. Advanced technology dramatically reduces the risk of personal resources in graduate program admission recommendations that depend on multiple individual needs and preferences. In the digital age, a dynamic recommender system enhances the suitably effective solution for students’ university selections. This study focused on designing, developing, and testing a recommender system for graduate admission using a dynamic multi-criteria AHP and fuzzy AHP approach. The explicit multi-criteria recommender system was a platform as a service (PaaS) web application created to aid in graduate admissions management and decision-making. The design proposed that the bit representation store a dynamic explicit multi-criteria data structure. The recommendations adopting dynamic multi-criteria were validated by comparing them to the programs to which the students were actually admitted and enrolled. They individually ranked the evaluation outcomes of dynamic explicit multi-criteria and alternative preferences to provide graduate admission recommendations. Eighty graduate students in information technology evaluated the recommender system. Using top-1, top-2, and F1-score accuracy, the effective system accuracy performance on the dynamic multi-criteria recommender system was evaluated using AHP and fuzzy AHP approaches. The fuzzy AHP demonstrated marginally greater practical accuracy than the AHP method.

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Section: Recommender System In Educationmentioning

confidence: 99%

Section: Samplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamic Multi-Criteria Decision Making of Graduate Admission Recommender System: AHP and Fuzzy AHP Approaches

2023

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“…Zihayat et al [ 11 ] proposed a news recommendation system combining utility model and probabilistic topic model, which solved the cold start problem of items. Xiao et al [ 12 ] combined association rule-based recommendation, content-based recommendation, and collaborative filtering-based recommendation to propose a hybrid personalized recommendation system [ 13 ]. Fernández-García et al [ 14 ] then recommend a mix of music in multiple dimensions of music genre, theme, and voice tone.…”

Section: Related Workmentioning

confidence: 99%

Knowledge distillation for multi-depth-model-fusion recommendation algorithm

Yang

Li²,

Zhou³

et al. 2022

PLoS ONE

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Recommendation algorithms save a lot of valuable time for people to get the information they are interested in. However, the feature calculation and extraction process of each machine learning or deep learning recommendation algorithm are different, so how to obtain various features with different dimensions, i.e., how to integrate the advantages of each model and improve the model inference efficiency, becomes the focus of this paper. In this paper, a better deep learning model is obtained by integrating several cutting-edge deep learning models. Meanwhile, to make the integrated learning model converge better and faster, the parameters of the integrated module are initialized, constraints are imposed, and a new activation function is designed for better integration of the sub-models. Finally, the integrated large model is distilled for knowledge distillation, which greatly reduces the number of model parameters and improves the model inference efficiency.

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“…It is getting more difficult for users to quickly find the product or item they need among the sea of irrelevant items [1,4]. To address this issue, recommender systems were invented, which now are playing an important role in various applications such as e-commerce, music radio, advertising and movies [3,[5][6][7][8][9][10][11]. With recommender systems, personalized item lists according to users can be generated based on their interests and preferences in order to boost sales, promote bundling and improve customer satisfaction [3,4,8].…”

Section: Introductionmentioning

confidence: 99%

Improve the Quality of Recommender Systems based on Collaborative Filtering with Missing Data Imputation

Hikmawati

Nugroho

Surendro

2023

Preprint

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Recommender systems which are becoming increasingly popular and have been proven to boost cross-selling and customer satisfaction still need further refinement to improve their relevancy and correspond to the user’s need. One widely used method of recommender systems is Collaborative Filtering (CF). The CF method bases predictions for a user on feedback data from other users, so that user preferences can be formulated based on the feedback it provides. One of the prevalent obstacles to the CF method is that the feedback data is often incomplete because there is no compulsion for the user to provide feedback on the items they have consumed. Incomplete data will affect the quality of recommendation results. One way to address this is to use missing data imputation. In this study, a collaborative filtering method is proposed where missing data imputation is carried out first before commencing data training. From the experiment results using Amazon Customer Review dataset, it is shown that the CF method with Imputation Mean produces lower RMSE and MAE values.

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A hybrid recommender system for an online store using a fuzzy expert system

Cited by 50 publications

References 24 publications

Dynamic Multi-Criteria Decision Making of Graduate Admission Recommender System: AHP and Fuzzy AHP Approaches

Dynamic Multi-Criteria Decision Making of Graduate Admission Recommender System: AHP and Fuzzy AHP Approaches

Knowledge distillation for multi-depth-model-fusion recommendation algorithm

Improve the Quality of Recommender Systems based on Collaborative Filtering with Missing Data Imputation

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