A Recommender System for Recommending Suitable Products in E-shop Using Explanations

Walek, Bogdan; Fajmon, Petr

doi:10.1109/airc56195.2022.9836983

Cited by 8 publications

(3 citation statements)

References 14 publications

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“…It is expected that ERS will attain even larger focus by the scientific society because of the rise in users' concerns regarding the monitoring and utilization of their personalized data and behavior. Walek and Fajmon [154] proposed another, much simpler approach, where a hybrid RS justifies the recommendations by stating whether they were based on their similarity with viewed items (produced by a CB module), on the user's ratings (produced by a CF module), or on the customer's purchase history.…”

Section: Explainable Recommendation Systemsmentioning

confidence: 99%

Recommendation Systems for e-Shopping: Review of Techniques for Retail and Sustainable Marketing

Stalidis,

Karaveli,

Diamantaras

et al. 2023

Sustainability

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In recent years, the interest in recommendation systems (RSs) has dramatically increased, as they have become main components of all online stores. The aims of an RS can be multifaceted, related not only to the increase in sales or the convenience of the customer, but may include the promotion of alternative environmentally friendly products or to strengthen policies and campaigns. In addition to accurate suggestions, important aspects of contemporary RSs are therefore to align with the particular marketing goals of the e-shop and with the stances of the targeted audience, ensuring user acceptance, satisfaction, high impact, and achieving sustained usage by customers. The current review focuses on RS related to retail shopping, highlighting recent research efforts towards enhanced e-shops and more efficient sustainable digital marketing and personalized promotion. The reported research was categorized by main approach, key methods, and specialized e-commerce problems addressed, while technological aspects were linked with marketing aspects. The increasing number of papers in the field showed that it has become particularly popular, following the explosive growth in e-commerce and mobile shopping. The problems addressed have expanded beyond the performance of the core algorithms to the business aspects of recommendation, considering user acceptance and impact maximization techniques. Technologies have also shifted from the improvement of classic filtering techniques to complex deep learning architectures, in order to deal with issues such as contextualization, sequence-based methods, and automatic feature extraction from unstructured data. The upcoming goals seem to be even more intelligent recommendations that more precisely adapt not only to users’ explicit needs and hidden desires but also to their personality and sensitivity for more sustainable choices.

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Section: Explainable Recommendation Systemsmentioning

confidence: 99%

Recommendation Systems for e-Shopping: Review of Techniques for Retail and Sustainable Marketing

Stalidis,

Karaveli,

Diamantaras

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…У випадку, коли традиційні обчислювальні методи вже не можуть проаналізувати всю необхідну інформацію, алгоритми ML працюють гнучкіше, легко масштабуються та автоматизують громіздкі обчислення. Для аналізу даних, розташованих на різних пристроях, доцільно використовувати розподілені алгоритми машинного навчання [6][7][8].…”

Section: особливості обробки даних у розподілених системахunclassified

Investigation of Distributed Matrix Factorisation Efficiency in the Industrial Systems

Hordiichuk-Bublivska

2023

ICTEE

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The processing of big data is an exceedingly urgent challenge in the functioning of modern information systems. The latest information technologies must be employed to collect, store, and analyze vast amounts of information. Intelligent data processing systems were implemented in numerous fields, particularly in the industry. Smart industrial systems also utilize data from various devices, enabling automated management processes and network component analysis. A prime example of an intelligent industrial system is the smart grid, which efficiently distributes electricity to users by considering demand, network parameters, load, etc. Processing large amounts of information necessitates the use of machine learning methods and mathematical data analysis. Matrix factorization serves as an exemplary technique for transforming information into a more convenient form for further processing, establishing relationships between elements, and optimizing outcomes. In particular, the SVD (Singular Value Decomposition) and Funk-SVD algorithms are employed to address big data processing challenges, and they were discussed in this work. The key features of processing large data volumes in industrial smart grid systems were analyzed in the paper. The advantages of distributed computing for more efficient information analysis were identified. The recommendation algorithms that enable faster and more accurate processing of extensive data were explored in the study. Specifically, the SVD and Funk-SVD algorithms, used in recommendation systems for large data processing, were examined. A method of distributed matrix factorization to provide recommendations to smart grid system users was proposed in the paper. This approach involves the exchange of public data between devices and the local processing of private data. The advantages of this distributed model include flexibility in adjusting parameters, improved calculation accuracy through result exchange between nodes, high data processing speed, and scalability were identified. The conclusion that the proposed method can be effectively used in recommendation systems within the smart grid context, enhancing automated management processes and resource distribution was exclaimed.

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“…One of the most prevalent examples of personalization in software is its use in recommender systems. Recommender systems [9][10][11][12][13][14] are programs that offer users customized suggestions based on their previous interests and activity. Recommender systems can assist users in discovering new services, goods, or content that they are likely to consider interesting or relevant by employing personalization strategies, while additionally enhancing the overall user experience.…”

Section: Introductionmentioning

confidence: 99%

Harnessing the Power of User-Centric Artificial Intelligence: Customized Recommendations and Personalization in Hybrid Recommender Systems

et al. 2023

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Recommender systems are widely used in various fields, such as e-commerce, entertainment, and education, to provide personalized recommendations to users based on their preferences and/or behavior. Τhis paper presents a novel approach to providing customized recommendations with the use of user-centric artificial intelligence. In greater detail, we introduce an enhanced collaborative filtering (CF) approach in order to develop hybrid recommender systems that personalize search results for users. The proposed CF enhancement incorporates user actions beyond explicit ratings to collect data and alleviate the issue of sparse data, resulting in high-quality recommendations. As a testbed for our research, a web-based digital library, incorporating the proposed algorithm, has been developed. Examples of operation of the use of the system are presented using cognitive walkthrough inspection, which demonstrates the effectiveness of the approach in producing personalized recommendations and improving user experience. Thus, the hybrid recommender system, which is incorporated in the digital library, has been evaluated, yielding promising results.

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A Recommender System for Recommending Suitable Products in E-shop Using Explanations

Cited by 8 publications

References 14 publications

Recommendation Systems for e-Shopping: Review of Techniques for Retail and Sustainable Marketing

Recommendation Systems for e-Shopping: Review of Techniques for Retail and Sustainable Marketing

Investigation of Distributed Matrix Factorisation Efficiency in the Industrial Systems

Harnessing the Power of User-Centric Artificial Intelligence: Customized Recommendations and Personalization in Hybrid Recommender Systems

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