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Dimitris Spiliotopoulos, Dionisis Margaris and Costas Vassilakis
One of the typical goals of collaborative filtering algorithms is to produce rating predictions with values very close to what real users would give to an item. Afterward, the items having the largest rating prediction values will be recommended to the u...
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Bingkun Wang, Bing Chen, Li Ma and Gaiyun Zhou
With the explosive growth of product reviews, review rating prediction has become an important research topic which has a wide range of applications. The existing review rating prediction methods use a unified model to perform rating prediction on review...
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Zewen Zhu, Kuai Ye, Xinhua Yu, Zefang Lin, Gangzong Xu, Zhenyou Guo, Shoushan Lu, Biao Nie and Huapeng Chen
The technical condition of bridges has become a crucial issue for organizing the maintenance and repairs in bridge management systems. It is of great practical engineering significance to construct an effective model for predicting the technical conditio...
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Dionisis Margaris, Costas Vassilakis, Dimitris Spiliotopoulos and Stefanos Ougiaroglou
Collaborative filtering has proved to be one of the most popular and successful rating prediction techniques over the last few years. In collaborative filtering, each rating prediction, concerning a product or a service, is based on the rating values tha...
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Jianfei Li, Yongbin Wang and Zhulin Tao
In recent years, graph neural networks (GNNS) have been demonstrated to be a powerful way to learn graph data. The existing recommender systems based on the implicit factor models mainly use the interactive information between users and items for trainin...
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Jaideep Singh and Matloob Khushi
Efficient Market Hypothesis states that stock prices are a reflection of all the information present in the world and generating excess returns is not possible by merely analysing trade data which is already available to all public. Yet to further the re...
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Tae-Gyu Hwang and Sung Kwon Kim
A recommender system (RS) refers to an agent that recommends items that are suitable for users, and it is implemented through collaborative filtering (CF). CF has a limitation in improving the accuracy of recommendations based on matrix factorization (MF...
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Woo-Joo Lee, Hyo-Jin Jhang, Seung Hoe Choi
Pág. 169 - 178
This study aims to find variables that affect the winning rate of the football team before a match. Qualitative variables such as venue, match importance, performance, and atmosphere of both teams are suggested to predict the outcome. Regression analysis...
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Woo-Joo Lee, Hyo-Jin Jhang, Seung Hoe Choi
Pág. 169 - 178
This study aims to find variables that affect the winning rate of the football team before a match. Qualitative variables such as venue, match importance, performance, and atmosphere of both teams are suggested to predict the outcome. Regression analysis...
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Dionisis Margaris, Dimitris Spiliotopoulos, Gregory Karagiorgos and Costas Vassilakis
Collaborative filtering algorithms formulate personalized recommendations for a user, first by analysing already entered ratings to identify other users with similar tastes to the user (termed as near neighbours), and then using the opinions of the near ...
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Mahamudul Hasan and Falguni Roy
Item-based collaborative filtering is one of the most popular techniques in the recommender system to retrieve useful items for the users by finding the correlation among the items. Traditional item-based collaborative filtering works well when there exi...
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Zhi-Peng Zhang, Yasuo Kudo, Tetsuya Murai and Yong-Gong Ren
Recommender systems (RS) analyze user rating information and recommend items that may interest users. Item-based collaborative filtering (IBCF) is widely used in RSs. However, traditional IBCF often cannot provide recommendations with good predictive and...
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Depeng Zhang, Hongchen Wu and Feng Yang
The popularity of intelligent terminals and a variety of applications have led to the explosive growth of information on the Internet. Some of the information is real, some is not real, and may mislead people?s behaviors. Misleading information refers to...
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Dionisis Margaris,Costas Vassilakis
Pág. 22 - 42
Users that enter ratings for items follow different rating practices, in the sense that, when rating items, some users are more lenient, while others are stricter. This aspect is taken into account by the most widely used similarity metric in user-user c...
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Fahad Alshehri and Mark Ross
This hydrological study investigated a combined rating methodology tested on a 14,090 km2 area in Southwest Florida. The approach applied the Hydrological Simulation Program-Fortran (HSPF) over a 23-year period and was validated by 28 stream gauging stat...
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Ali M. Ahmed Al Sabaawi,Hacer Karacan,Yusuf Erkan Yenice
Pág. pp. 70 - 87
Recently, Recommender Systems (RSs) have attracted many researchers whose goal is to improve the performance of the prediction accuracy of recommendation systems by alleviating RSs drawbacks. The most common limitations are sparsity and the cold-start pr...
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Waqas Ul Hussan, Muhammad Khurram Shahzad, Frank Seidel and Franz Nestmann
The accurate estimate of sediment load is important for management of the river ecosystem, designing of water infrastructures, and planning of reservoir operations. The direct measurement of sediment is the most credible method to estimate the sediments....
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Jehan Al-Safi and Cihan Kaleli
A technique employed by recommendation systems is collaborative filtering, which predicts the item ratings and recommends the items that may be interesting to the user. Naturally, users have diverse opinions, and only trusting user ratings of products ma...
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Lukasz Nowotny and Jacek Nurzynski
Lightweight floors are in line with a sustainable construction concept and have become increasingly popular in residential buildings. The acoustic performance of such floors plays a pivotal role in the overall building quality rating. There is, however, ...
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Dionisis Margaris and Costas Vassilakis
One of the major problems that social networks face is the continuous production of successful, user-targeted information in the form of recommendations, which are produced exploiting technology from the field of recommender systems. Recommender systems ...
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