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Aileen C. Benedict and Zbigniew W. Ras
The paper concerns the problem of action-rule extraction when datasets are large. Such rules can be used to construct a knowledge base in a recommendation system. One of the popular approaches to construct action rules in such cases is to partition the d...
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Mohd Anuaruddin Bin Ahmadon, Shingo Yamaguchi, Abd Kadir Mahamad and Sharifah Saon
Online services, ambient services, and recommendation systems take user preferences into data processing so that the services can be tailored to the customer?s preferences. Associative rules have been used to capture combinations of frequently preferred ...
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Rand Jawad Kadhim Almahmood and Adem Tekerek
In recent years, especially with the (COVID-19) pandemic, shopping has been a challenging task. Increased online shopping has increased information available via the World Wide Web. Finding new products or identifying the most suitable products according...
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Markos Konstantakis, Georgios Alexandridis and George Caridakis
Recent developments in digital technologies regarding the cultural heritage domain have driven technological trends in comfortable and convenient traveling, by offering interactive and personalized user experiences. The emergence of big data analytics, r...
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Laith T. Khrais
The advent and incorporation of technology in businesses have reformed operations across industries. Notably, major technical shifts in e-commerce aim to influence customer behavior in favor of some products and brands. Artificial intelligence (AI) comes...
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Cristina Gena, Pierluigi Grillo, Antonio Lieto, Claudio Mattutino and Fabiana Vernero
Aiming at granting wide access to their contents, online information providers often choose not to have registered users, and therefore must give up personalization. In this paper, we focus on the case of non-personalized news recommender systems, and ex...
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Emelia Opoku Aboagye and Rajesh Kumar
We approach scalability and cold start problems of collaborative recommendation in this paper. An intelligent hybrid filtering framework that maximizes feature engineering and solves cold start problem for personalized recommendation based on deep learni...
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Eirini Eleni Tsiropoulou, George Kousis, Athina Thanou, Ioanna Lykourentzou and Symeon Papavassiliou
This paper addresses the problem of museum visitors? Quality of Experience (QoE) optimization by viewing and treating the museum environment as a cyber-physical social system. To achieve this goal, we harness visitors? internal ability to intelligently s...
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Andreas Kanavos, Stavros Anastasios Iakovou, Spyros Sioutas and Vassilis Tampakas
In this manuscript, we present a prediction model based on the behaviour of each customer using data mining techniques. The proposed model utilizes a supermarket database and an additional database from Amazon, both containing information about customers...
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Hao Tian and Peifeng Liang
In order to alleviate the pressure of information overload and enhance consumer satisfaction, personalization recommendation has become increasingly popular in recent years. As a result, various approaches for recommendation have been proposed in the pas...
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