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Tamim Mahmud Al-Hasan, Aya Nabil Sayed, Faycal Bensaali, Yassine Himeur, Iraklis Varlamis and George Dimitrakopoulos
Recommender systems are a key technology for many applications, such as e-commerce, streaming media, and social media. Traditional recommender systems rely on collaborative filtering or content-based filtering to make recommendations. However, these appr...
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Suleiman Ali Alsaif, Minyar Sassi Hidri, Imen Ferjani, Hassan Ahmed Eleraky and Adel Hidri
For more than ten years, online job boards have provided their services to both job seekers and employers who want to hire potential candidates. The provided services are generally based on traditional information retrieval techniques, which may not be a...
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Olga Malyeyeva, Vadym Yesipov, Roman Artiukh, Viktor Kosenko
Pág. 59 - 68
The subject of research in the article is the methods of finding close objects and technologies of forming recommendations. The aim of the article is to develop a recommendation system based on a hybrid method of searching for objects, taking into accoun...
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Ezekiel Mensah Martey, Hang Lei, Xiaoyu Li and Obed Appiah
Image representation plays a vital role in the realisation of Content-Based Image Retrieval (CBIR) system. The representation is performed because pixel-by-pixel matching for image retrieval is impracticable as a result of the rigid nature of such an app...
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Márcio Guia, Rodrigo Rocha Silva and Jorge Bernardino
The growth of the Internet has increased the amount of data and information available to any person at any time. Recommendation Systems help users find the items that meet their preferences, among the large number of items available. Techniques such as c...
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Paul Sheridan, Mikael Onsjö, Claudia Becerra, Sergio Jimenez and George Dueñas
Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start p...
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Elahe Khazaei and Abbas Alimohammadi
Location-based social networking services have attracted great interest with the growth of smart mobile devices. Recommending locations for users based on their preferences is an important task for location-based social networks (LBSNs). Since human bein...
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Vasyl Lytvyn,Victoria Vysotska,Viktor Shatskykh,Ihor Kohut,Oksana Petruchenko,Lyudmyla Dzyubyk,Vitaliy Bobrivetc,Valentyna Panasyuk,Svitlana Sachenko,Myroslav Komar
Pág. 6 - 28
The paper reports a study into recommendation algorithms and determination of their advantages and disadvantages. The method for developing recommendations based on collaborative filtering such as Content-Based Filtering (CBF), Collaborative Filtering (C...
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Mingxuan Sun, Fei Li and Jian Zhang
Collaborative filtering (CF) approaches, which provide recommendations based on ratings or purchase history, perform well for users and items with sufficient interactions. However, CF approaches suffer from the cold-start problem for users and items with...
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Qingyao Ai, Vahid Azizi, Xu Chen and Yongfeng Zhang
Providing model-generated explanations in recommender systems is important to user experience. State-of-the-art recommendation algorithms?especially the collaborative filtering (CF)- based approaches with shallow or deep models?usually work with various ...
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