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18,798 results on '"RECOMMENDER systems"'

1. Tales of Two Channels: Digital Advertising Performance Between AI Recommendation and User Subscription Channels.

Author

Dong, Beibei, Zhuang, Mengzhou, Fang, Eric, and Huang, Minxue

Subjects
INTERNET advertising, NATIVE advertising, RECOMMENDER systems, SUBSCRIPTION Internet services, IN-stream advertising, ARTIFICIAL intelligence, CLICK through rate
Abstract

Although in-feed advertising is popular on mainstream platforms, academic research on it is limited. Platforms typically deliver organic content through two methods: subscription by users or recommendation by artificial intelligence. However, little is known about the ad performance between these two channels. This research examines how the performance of in-feed ads, in terms of click-through rates and conversion rates, differs between subscription and recommendation channels and whether these effects are mediated by ad intrusiveness and moderated by ad attributes. Two ad attributes are investigated: ad appeal (informational vs. emotional) and ad link (direct vs. indirect). Study 1 finds that the recommendation channel generates higher click-through rates but lower conversion rates than the subscription channel, and these effects are amplified by informational ad appeal and direct ad links. Study 2 explores channel differences, revealing that the recommendation channel yields less source credibility and content control, reducing consumer engagement with organic content. Studies 3 and 4 validate the mediating role of ad intrusiveness and rule out ad recognition as an alternative explanation. Study 5 uses eye-tracking technology to show that the recommendation channel has lower content engagement, lower ad intrusiveness, and greater ad interest. [ABSTRACT FROM AUTHOR]

Published
2024
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2. Delinquent Acts in Academic Libraries: A Case Study of Three Academic Libraries in Northern Ghana.

Author

Appiah, Deborah Kore, Kwaah, Christiana Oduraa, and Plockey, Florence Dzokoto-Dodzie

Subjects
*LIBRARY users, *LIBRARY resources, *LIBRARY personnel, *ACADEMIC libraries, *LIBRARY finance, *DEVIANT behavior, *LIBRARY cooperation, *COPYING, *RECOMMENDER systems
Abstract

This paper reports on a study on delinquent acts in three academic libraries in northern Ghana. A survey method was used for the study and a questionnaire was used to collect data. The study population consisted of staff in the Reader Service Sections of the three libraries. The study found that failure to observe library regulations, keeping overdue materials, eating in the library, littering, reserving seats unjustifiably, and hiding of books for the purpose of monopoly are examples of delinquent acts practiced by library users. According to the participants, the main reasons for delinquent acts are inadequate orientation to users, insufficient copies of books, and indiscipline among library users. The respondents further reported that these actions led to a reduction in library resources and low patronage. To curb deviant behaviors in libraries, the study recommends the adoption of pragmatic approaches including massive orientation for users, imbibing positive library culture that fosters respect, bolstering commitment and cooperation among library users and the staff, increasing library budgets as well as finding more creative ways of acquiring more copies of books, such as using Open Educational Resources (OERs), belonging to consortia and building and/or enhancing existing Institutional Repositories (IR). [ABSTRACT FROM AUTHOR]

Published
2024
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3. The Daily Me Versus the Daily Others: How Do Recommendation Algorithms Change User Interests? Evidence from a Knowledge-Sharing Platform.

Author

Liu, Jia and Cong, Ziwei

Subjects
RECOMMENDER systems, ALGORITHMS, INTEREST (Psychology), CHANGE (Psychology), INFORMATION sharing, ONLINE social networks, USER-generated content
Abstract

Recommender systems on online platforms are often accused of polarizing user attention and consumption. The authors examine this phenomenon using a quasi-experiment conducted by Zhihu, the largest online knowledge-sharing platform (or Q&A community) in China. Zhihu originally used a content-based filtering algorithm, which recommends content to users on the basis of the topics to which each user has subscribed. After more than a year, Zhihu moved to a social filtering algorithm, which recommends content with which users' social connections are already engaged. The authors find that this algorithm change increased the creation of social ties by approximately 15% but decreased question subscriptions by 20% and answer contributions by 23%. The authors show that users' increased social interests mainly involved following popular users, leading to a greater concentration of social interests on the platform. However, users' topical interests became less concentrated, as popular topics received significantly fewer subscribers than unpopular topics. The authors explain these findings by exploring the underlying mechanism. They show that compared with content-based filtering algorithms, social filtering algorithms are more likely to expose general users to content consumed by their followees, who are more interested in niche topics than general users are. [ABSTRACT FROM AUTHOR]

Published
2023
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4. Healthcare analytic for disease prediction using machine learning.

Author

Singh, Sandeep Kumar, Bhatia, Madhulika, Madaan, Rosy, and Wadhwa, Bhawna

Subjects
*BIG data, *MACHINE learning, *INFORMATION technology, *RECOMMENDER systems, *MEDICAL personnel, *K-nearest neighbor classification, *MEDICAL care
Abstract

Globally, the behavior of people seeking information is changing due to the need for health information. Finding health information online on illnesses, diagnoses, and treatments can be difficult for many people. It will save a lot of time if a recommendation system for physicians and medications can be created utilizing review mining. They have difficulty understanding the diverse medical jargon. Actually, the dataset contains forty unique diseases and one hundred thirty-two symptoms. The system used a 92% pre-trained model and a testing portion is about 8% to enhance the accuracy of the K-Nearest Neighbor algorithm. The goal of the recommender system is to adapt to the unique user-related needs of the health domain. To the best of our knowledge, no recent work in the area of remedial big data analytics has focused on all data kinds simultaneously. When compared to various common calculating algorithms, our suggested algorithm's planning accuracy is 93% with a regular pace that is faster than the uni-modal illness risk prediction algorithm and generates a report. In this study, there were 25,074 discharge summaries from NYPH included in the case report. Healthcare information technology is transforming the way healthcare providers interact with patients and driving productivity, quality, and efficiency. To support this transformation, the Methodology for this flow of diagram is "Fig 1". All healthcare professionals have secure access to health information that is relevant to their practice setting. Studies for predicting general disease are taken from general symptoms are shown in Table 2. HIE also needs a system that can recognize sensitive content and provide recommendations so healthcare providers can avoid potentially compromising conversations with their patients. Based on our research, we propose an alternative for healthcare professionals who want tailored recommendation engines for their specific needs. [ABSTRACT FROM AUTHOR]

Published
2024
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5. Book recommendation system using TF-IDF and cosine similarity.

Author

Reswara, Christopher Gavra, Nicolas, Josua, Widyatama, I. Made Danendra, David, David, and Arisaputra, Panji

Subjects
*RECOMMENDER systems, *RESERVATION systems, *BOOK titles, *ELECTRONIC books, *AUTHOR-publisher relations
Abstract

The number of new books that appear on the market makes it difficult for readers to find books that meet their needs and desires. There are many new books every year. Sometimes there are productions with the same or similar titles but with different authors or publishers. The content may be other, even though the title is identical. It may also happen that a similarly titled book is a sequel to an earlier one. Therefore, this paper will focus on a content-based book recommendation system. The book content used in the system is the book title. The system provides book recommendations with book titles that are the same or similar to the book selected by the user. The way the system works is to extract the book title feature with Term Frequency-Inverse Document Frequency (TF-IDF). Then, all book titles are compared with cosine similarity. There are two kinds of results from this book recommendation system. First, 20 books are similar to the book selected by the user, where the 20 books have a similarity level above 50%. Second, 20 books have a similarity level below 50%. Both types of results are offered to users to be selected according to their preferences. [ABSTRACT FROM AUTHOR]

Published
2024
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6. A novel approach for hotel recommendation system based on modified KNN and naive bayes algorithm.

Author

Gopikrishna, B., Sathish, T., and Ashwini, S.

Subjects
*RECOMMENDER systems, *ALGORITHMS, *HOTELS, *RESTAURANTS, *NAIVE Bayes classification
Abstract

For the purpose of assisting customers in selecting the most appropriate hotel within their budgetary constraints, close to convenient amenities such cafes and internet cafes. This study helps in simplifying the process of selecting the most suitable hotel, ensuring a pleasant stay with all conveniences. The hotel datasets in the kaggle dataset were used to acquire a total of 730 samples. The modified KNN method and the naïve bayes algorithm were employed for this purpose, and their performance was evaluated and compared. The value of G-force was stable at 80%. When compared to the results obtained by the naive bayes algorithm (80.1% accuracy, 83.39 percent precision, 84.2 percent sensitivity, and 86.2 percent specificity), the results obtained by the modified collaborative filtering were, respectively, 87.4 percent, 88.2 percent, 89.2 percent, and 93.3 percent. More than 730 samples were used, and the findings were statistically significant at the 0.162 level, with a pretest power of 80%. The results show that the new technique is more accurate than the naïve bayes algorithm. [ABSTRACT FROM AUTHOR]

Published
2024
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7. Measuring and comparing the efficiency of logistic regression, decision tree and SVM with k-means algorithm in college recommendation system.

Author

Kumar, M. Vinod, Lakshmi, S. Vidhya, and Sabitha, R.

Subjects
*DECISION trees, *RECOMMENDER systems, *K-means clustering, *SUPPORT vector machines, *LOGISTIC regression analysis
Abstract

The purpose of this research is to evaluate the performance of four distinct approaches to prediction—namely, Logistic Regression (LR), Decision Tree (DT), K Means Algorithm (KMA), and Support Vector Machine (SVM) Classification—in order to determine which one yields the most accurate results. The Following are the Components and Methods: When testing the Classification technique, a dataset consisting of 778 items is employed. In the subject of education, the Logistic Regression (LR), K-Means, Decision Tree, and Support Vector Machine (SVM) are all viable frameworks that have been suggested and developed for the College Recommendation system. K-Means was the first of these frameworks to be built. Through the use of G power, we were able to establish that we need 55 people for each of the conditions. Both the precision and accuracy of the classifiers have been analysed and recognised. Through the use of G Power, we were able to establish that each of our groups required 55 individuals. The College dataset had a sample size of 303 students, 76 attributes, and a few blanks in addition to those numbers. A clinical study was conducted to establish the appropriate size of the sample, and the following were the findings: 80 percent predictive power on the pretest, 0.05 alpha, and a 1. When applied to the College Recommendation System data set, the results demonstrate that the K-Means classifier produces the same group that it predicts (50 percent). This is also the case for Logistic regression (86 percent), Decision Tree (88 percent), K-Means (50 percent), and Support Vector Machine (50 percent) (85 percent). The value assigned to significance is zero. Consequently, DT performs better than LR, SVM, and K-Means. The findings indicate that Decision Tree offers greater performance in terms of precision and accuracy when compared to SVM, LR, DT, and K-Means. This is shown by the fact that Decision Tree. [ABSTRACT FROM AUTHOR]

Published
2024
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8. Comparison of hybrid novel pearson correlation coefficient with collaborative filtering model to improve accuracy for movie recommendation system.

Author

Shoaib, Syed Mohammed and Jaisharma

Subjects
*RECOMMENDER systems, *PEARSON correlation (Statistics), *STATISTICAL hypothesis testing, *CONFIDENCE intervals
Abstract

Hybrid Collaborative Filtering (CF) and HNPCC Recommendation Models Improve Movie Recommendation Accuracy. A hybrid movie recommendation system was created using the HNPCC and current CF system. Each group will include 30 individuals, giving pre-test power of 0.80. Our analysis used 23 attributes and 2725 samples from the movielens dataset. This study found that HNPCC is slightly more accurate than CF at 94.3 percent. Independent t-tests demonstrated a statistically significant difference between the two sets of data. The test significance level was 0.001 (p0.05), and the confidence interval was 95%. (CI). The comparison shows that the HNPCC is more accurate than the CF. [ABSTRACT FROM AUTHOR]

Published
2024
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9. Machine learning approaches for hotel recommendation.

Author

Parikh, Shivanshi N., Shah, Jaimeel, Sutaria, Kamal, and Vala, Brijesh

Subjects
*HOTEL reservation systems, *MACHINE learning, *RECOMMENDER systems, *HOTEL ratings & rankings, *INFORMATION filtering, *HOTEL rooms
Abstract

The usage of recommendation systems has increased significantly over the last several years across numerous businesses, including the travel and entertainment sectors. They may serve as information filters and provide clients with ideas that are appropriate for their requirements by combining various types of data from various networks. Based on published reviews, ratings, and particular areas of interest, many visitors and travelers choose hotels in cities all over the globe. Online hotel reservation systems often rank their hotels depending on what their existing customers have to say about them to attract additional visitors. A hotel recommendation system attempts to predict which of the hotels it suggests a user would choose. Because of this, it's crucial to create a system like this one that aids users in selecting the finest hotel out of all the available choices. Here, the opinions of our customers will be valuable. Therefore, the purpose of this study is to examine several algorithms, like SVM, Random Forest, and others. By analyzing recent research and discussing the difficulties ahead for online recommendations of tourist resources based on crowdsourced data, this article will demonstrate how the area might be improved. To create a model that would assist customers in selecting hotels that meet their requirements, exploratory research was conducted. [ABSTRACT FROM AUTHOR]

Published
2024
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10. Recommender systems and ITS applications on popular online platforms.

Author

Kumar, Ashish and Singh, Yudhvir

Subjects
*RECOMMENDER systems, *INTERNET, *ELECTRONIC commerce, *ENTRANCES & exits, *CONSUMERS
Abstract

With the increasing use of the internet, there is an increase in demand for recommender systems (RSs). Now internet and RSs are growing parallelly. Getting appropriate information from the ocean of information in less time with minimum effort is the main role player in the development of the RSs. The use of RSs is not only restricted to e-commerce platforms but it is expanded a lot outside of e-commerce also. On the web, if any portal is providing personalized services, then it is not possible without using the RSs. In this paper, we have explained RSs of various types, their properties, and popular platforms like Amazon.com, LinkedIn, YouTube, Netflix, Spotify, and Google news; which are using it for their customers to provide them with personalized services. [ABSTRACT FROM AUTHOR]

Published
2024
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11. Decision support system for determining best job vacancies with Edas algorithm.

Author

Hardi, Sri Melvani, Kartono, Tommy, and Selvida, Desilia

Subjects
*DECISION support systems, *JOB vacancies, *INDONESIANS, *COGNITIVE ability, *RECOMMENDER systems
Abstract

Indonesia Central Bureau of Statistics February 2020's data showed us that there were 6.88 million Indonesian citizens who were still unemployed. One of the factors that cause unemployment is the difficulty to find job vacancies that are suitable for job seekers' skills. Generally, job seekers will find job vacancies that offer a good salary and many facilities. But, often they forget to consider the required qualifications and decide the best job vacancies for themselves based on intuition only. This is quite natural considering the limited human cognitive ability. But, decision-making that relies on only intuition alone can cause fatal consequences. Especially considering number of job vacancies is quite limited. Therefore, we can develop a decision support system to help job seekers to find job vacancies that offer the best facilities and still consider the required qualifications. Decision support system is expected to provides the best job vacancies recommendations based on criteria weight that was obtained from a survey of 100 job seekers. Criteria used to evaluate job vacancies data are salary, number of employees, number of the offered facility, number of mandatory skills, and minimum work experience. EDAS (Evaluation based on Distance from Average Solution) method is chosen to be implemented in this decision support system. EDAS method uses two measuring distances to evaluate every job vacancies data, specifically Positive Distance from Average (PDA) and Negative Distance from Average (NDA). After conducting several tests on the system by using the manual calculation method and black-box testing method, it can be concluded that the developed decision support system can give job vacancies recommendations for users. [ABSTRACT FROM AUTHOR]

Published
2024
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12. Recommendation system for determining learning strategies using collaborative filtering techniques.

Author

Saleh, Amir, Azmi, Fadhillah, Dharshinni, N. P., and Perangin-Angin, Despaleri

Subjects
*RECOMMENDER systems, *LEARNING strategies, *INSTRUCTIONAL systems, *LEARNING, *BLOOM'S taxonomy
Abstract

The recommendation system has advantages, especially in saving time in obtaining decisions on the problems at hand. This system has been widely used for various purposes, such as in the field of education. One system that can be developed in the field of education is recommendations for determining learning strategies. Common problems encountered in learning, such as the implementation of learning, are carried out with the same technique and do not evaluate the learning outcomes obtained in accordance with the main objectives of learning. Basically, a teacher's teaching technique can be categorized as successful if all students achieve good performance in accordance with the learning objectives that have been set. Thus, a recommendation system is needed for a teacher to plan the learning to be implemented. In addition, the system provides opportunities for students to choose the desired learning style, and the teacher can apply it in the learning process. The recommendation system proposed in this study uses collaborative filtering techniques to determine learning strategies that are tailored to the wishes of students and teachers through questionnaires. The algorithm used in generating recommendations in this study uses the cosine similarity distance equation and the k-nearest neighbor (kNN) algorithm to measure the similarity of the data generated by the respondents. Based on the recommendation system testing results obtained, the accuracy results reached a value of 95.45% using the proposed method. [ABSTRACT FROM AUTHOR]

Published
2024
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14. Exploring Comprehensive Privacy Solutions for Enhancing Recommender System Security and Utility

Author

Gupta, Esmita, Shinde, Shilpa, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Sharma, Harish, editor, Shrivastava, Vivek, editor, Tripathi, Ashish Kumar, editor, and Wang, Lipo, editor

Published
2024
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18. Psychologically Informed Design of Energy Recommender Systems: Are Nudges Still Effective in Tailored Choice Environments?

Author

Starke, Alain D., Willemsen, Martijn C., Vanderdonckt, Jean, Editor-in-Chief, Liao, Q. Vera, Editor-in-Chief, Barbosa, Simone, Editorial Board Member, Bernhaupt, Regina, Editorial Board Member, Blagojevic, Rachel, Editorial Board Member, Bunt, Andrea, Editorial Board Member, Cao, Xiang, Editorial Board Member, Carroll, John M., Editorial Board Member, Cherubini, Mauro, Editorial Board Member, de Choudhury, Munmun, Editorial Board Member, Cockton, Gilbert, Editorial Board Member, Dragicevic, Pierre, Editorial Board Member, Duh, Henry Been-Lirn, Editorial Board Member, Feiner, Steven, Editorial Board Member, Fussell, Susan, Editorial Board Member, González-Calleros, Juan, Editorial Board Member, Jacob, Robert, Editorial Board Member, Jorge, Joaquim, Editorial Board Member, Kuflik, Tsvika, Editorial Board Member, Kumar, Ranjitha, Editorial Board Member, Lazar, Jonathan, Editorial Board Member, Lim, Youn-kyung, Editorial Board Member, Markopoulos, Panos, Editorial Board Member, Myers, Brad A., Editorial Board Member, Palanque, Philippe, Editorial Board Member, Schmidt, Albrecht, Editorial Board Member, Schnädelbach, Holger, Editorial Board Member, Seffah, Ahmed, Editorial Board Member, Vatavu, Radu-Daniel, Editorial Board Member, Vetere, Frank, Editorial Board Member, Zhao, Shengdong, Editorial Board Member, Ferwerda, Bruce, editor, Graus, Mark, editor, Germanakos, Panagiotis, editor, and Tkalčič, Marko, editor

Published
2024
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19. Transparent Music Preference Modeling and Recommendation with a Model of Human Memory Theory

Author

Kowald, Dominik, Reiter-Haas, Markus, Kopeinik, Simone, Schedl, Markus, Lex, Elisabeth, Vanderdonckt, Jean, Editor-in-Chief, Liao, Q. Vera, Editor-in-Chief, Barbosa, Simone, Editorial Board Member, Bernhaupt, Regina, Editorial Board Member, Blagojevic, Rachel, Editorial Board Member, Bunt, Andrea, Editorial Board Member, Cao, Xiang, Editorial Board Member, Carroll, John M., Editorial Board Member, Cherubini, Mauro, Editorial Board Member, de Choudhury, Munmun, Editorial Board Member, Cockton, Gilbert, Editorial Board Member, Dragicevic, Pierre, Editorial Board Member, Duh, Henry Been-Lirn, Editorial Board Member, Feiner, Steven, Editorial Board Member, Fussell, Susan, Editorial Board Member, González-Calleros, Juan, Editorial Board Member, Jacob, Robert, Editorial Board Member, Jorge, Joaquim, Editorial Board Member, Kuflik, Tsvika, Editorial Board Member, Kumar, Ranjitha, Editorial Board Member, Lazar, Jonathan, Editorial Board Member, Lim, Youn-kyung, Editorial Board Member, Markopoulos, Panos, Editorial Board Member, Myers, Brad A., Editorial Board Member, Palanque, Philippe, Editorial Board Member, Schmidt, Albrecht, Editorial Board Member, Schnädelbach, Holger, Editorial Board Member, Seffah, Ahmed, Editorial Board Member, Vatavu, Radu-Daniel, Editorial Board Member, Vetere, Frank, Editorial Board Member, Zhao, Shengdong, Editorial Board Member, Ferwerda, Bruce, editor, Graus, Mark, editor, Germanakos, Panagiotis, editor, and Tkalčič, Marko, editor

Published
2024
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21. Multi-sourced Integrated Ranking with Exposure Fairness

Author

Liu, Yifan, Liu, Weiwen, Xia, Wei, Zhu, Jieming, Zhang, Weinan, Dong, Zhenhua, Wang, Yang, Tang, Ruiming, Zhang, Rui, Yu, Yong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yang, De-Nian, editor, Xie, Xing, editor, Tseng, Vincent S., editor, Pei, Jian, editor, Huang, Jen-Wei, editor, and Lin, Jerry Chun-Wei, editor

Published
2024
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22. Towards Multi-subsession Conversational Recommendation

Author

Ji, Yu, Shen, Qi, Zhu, Shixuan, Yu, Hang, Zhang, Yiming, Cui, Chuan, Wei, Zhihua, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yang, De-Nian, editor, Xie, Xing, editor, Tseng, Vincent S., editor, Pei, Jian, editor, Huang, Jen-Wei, editor, and Lin, Jerry Chun-Wei, editor

Published
2024
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23. Recommendation System over Multi-layer Complex Networks: A Wide and Deep Graph Convolutional Neural Network Approach

Author

Hamidi Rad, Radin, ZareMehrjerdi, Hossein, Mirmohammad, Yasaman, Shahsavari, Mohammad Navid, Haeri, Maryam Amir, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, and Arai, Kohei, editor

Published
2024
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25. Recommender Systems

Author

Igual, Laura, Seguí, Santi, Mackie, Ian, Series Editor, Abramsky, Samson, Advisory Editor, Hankin, Chris, Advisory Editor, Hinchey, Mike, Advisory Editor, Kozen, Dexter C., Advisory Editor, Riis Nielson, Hanne, Advisory Editor, Skiena, Steven S., Advisory Editor, Stewart, Iain, Advisory Editor, Kizza, Joseph Migga, Advisory Editor, Crole, Roy, Advisory Editor, Scott, Elizabeth, Advisory Editor, Igual, Laura, Seguí, Santi, Vitrià, Jordi, With Contrib. by, Puertas, Eloi, With Contrib. by, Radeva, Petia, With Contrib. by, Pujol, Oriol, With Contrib. by, Escalera, Sergio, With Contrib. by, and Dantí, Francesc, With Contrib. by

Published
2024
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27. Quantum Computing for Information Retrieval and Recommender Systems

Author

Ferrari Dacrema, Maurizio, Pasin, Andrea, Cremonesi, Paolo, Ferro, Nicola, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Goharian, Nazli, editor, Tonellotto, Nicola, editor, He, Yulan, editor, Lipani, Aldo, editor, McDonald, Graham, editor, Macdonald, Craig, editor, and Ounis, Iadh, editor

Published
2024
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28. Countering Mainstream Bias via End-to-End Adaptive Local Learning

Author

Pan, Jinhao, Zhu, Ziwei, Wang, Jianling, Lin, Allen, Caverlee, James, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Goharian, Nazli, editor, Tonellotto, Nicola, editor, He, Yulan, editor, Lipani, Aldo, editor, McDonald, Graham, editor, Macdonald, Craig, editor, and Ounis, Iadh, editor

Published
2024
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29. GenRec: Large Language Model for Generative Recommendation

Author

Ji, Jianchao, Li, Zelong, Xu, Shuyuan, Hua, Wenyue, Ge, Yingqiang, Tan, Juntao, Zhang, Yongfeng, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Goharian, Nazli, editor, Tonellotto, Nicola, editor, He, Yulan, editor, Lipani, Aldo, editor, McDonald, Graham, editor, Macdonald, Craig, editor, and Ounis, Iadh, editor

Published
2024
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30. Learning-to-Rank with Nested Feedback

Author

Sagtani, Hitesh, Jeunen, Olivier, Ustimenko, Aleksei, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Goharian, Nazli, editor, Tonellotto, Nicola, editor, He, Yulan, editor, Lipani, Aldo, editor, McDonald, Graham, editor, Macdonald, Craig, editor, and Ounis, Iadh, editor

Published
2024
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31. Revealing the Hidden Impact of Top-N Metrics on Optimization in Recommender Systems

Author

Wegmeth, Lukas, Vente, Tobias, Purucker, Lennart, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Goharian, Nazli, editor, Tonellotto, Nicola, editor, He, Yulan, editor, Lipani, Aldo, editor, McDonald, Graham, editor, Macdonald, Craig, editor, and Ounis, Iadh, editor

Published
2024
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32. Learning Action Embeddings for Off-Policy Evaluation

Author

Cief, Matej, Golebiowski, Jacek, Schmidt, Philipp, Abedjan, Ziawasch, Bekasov, Artur, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Goharian, Nazli, editor, Tonellotto, Nicola, editor, He, Yulan, editor, Lipani, Aldo, editor, McDonald, Graham, editor, Macdonald, Craig, editor, and Ounis, Iadh, editor

Published
2024
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33. A Comparative Analysis of Memory-Based and Model-Based Collaborative Filtering on Recommender System Implementation

Author

Seridi, Karim, El Rharras, Abdessamad, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Ben Ahmed, Mohamed, editor, Boudhir, Anouar Abdelhakim, editor, El Meouche, Rani, editor, and Karaș, İsmail Rakıp, editor

Published
2024
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34. Prompt-Based Generative News Recommendation (PGNR): Accuracy and Controllability

Author

Li, Xinyi, Zhang, Yongfeng, Malthouse, Edward C., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Goharian, Nazli, editor, Tonellotto, Nicola, editor, He, Yulan, editor, Lipani, Aldo, editor, McDonald, Graham, editor, Macdonald, Craig, editor, and Ounis, Iadh, editor

Published
2024
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35. Investigating the Robustness of Sequential Recommender Systems Against Training Data Perturbations

Author

Betello, Filippo, Siciliano, Federico, Mishra, Pushkar, Silvestri, Fabrizio, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Goharian, Nazli, editor, Tonellotto, Nicola, editor, He, Yulan, editor, Lipani, Aldo, editor, McDonald, Graham, editor, Macdonald, Craig, editor, and Ounis, Iadh, editor

Published
2024
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36. The Impact of Differential Privacy on Recommendation Accuracy and Popularity Bias

Author

Müllner, Peter, Lex, Elisabeth, Schedl, Markus, Kowald, Dominik, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Goharian, Nazli, editor, Tonellotto, Nicola, editor, He, Yulan, editor, Lipani, Aldo, editor, McDonald, Graham, editor, Macdonald, Craig, editor, and Ounis, Iadh, editor

Published
2024
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37. Performance Comparison of Session-Based Recommendation Algorithms Based on GNNs

Author

Shehzad, Faisal, Jannach, Dietmar, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Goharian, Nazli, editor, Tonellotto, Nicola, editor, He, Yulan, editor, Lipani, Aldo, editor, McDonald, Graham, editor, Macdonald, Craig, editor, and Ounis, Iadh, editor

Published
2024
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38. Curiosity Enhanced Bayesian Personalized Ranking for Recommender Systems

Author

Deng, Yaoming, Ding, Qiqi, Wu, Xin, Cai, Yi, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Luo, Biao, editor, Cheng, Long, editor, Wu, Zheng-Guang, editor, Li, Hongyi, editor, and Li, Chaojie, editor

Published
2024
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39. Which Data Quality Model for Recommender Systems?

Author

Hassani Saissi, Meriem, Zellou, Ahmed, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Farhaoui, Yousef, editor, Hussain, Amir, editor, Saba, Tanzila, editor, Taherdoost, Hamed, editor, and Verma, Anshul, editor

Published
2024
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42. A Comparative Review of Convolutional Neural Networks, Long Short-Term Memory, and Recurrent Neural Networks in Recommendation Systems

Author

Tyagi, Geetanjali, Ray, Susmita, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Sharma, Harish, editor, Chakravorty, Antorweep, editor, Hussain, Shahid, editor, and Kumari, Rajani, editor

Published
2024
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46. Artificial Intelligence in Smart Tourism

Author

Kontogianni, Aristea, Alepis, Efthimios, Virvou, Maria, Patsakis, Constantinos, Kacprzyk, Janusz, Series Editor, Jain, Lakhmi C., Series Editor, Kontogianni, Aristea, Alepis, Efthimios, Virvou, Maria, and Patsakis, Constantinos

Published
2024
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49. Graph Representation Learning for Recommendation Systems: A Short Review

Author

Ammar, Khouloud, Inoubli, Wissem, Zghal, Sami, Mephu Nguifo, Engelbert, van der Aalst, Wil, Series Editor, Ram, Sudha, Series Editor, Rosemann, Michael, Series Editor, Szyperski, Clemens, Series Editor, Guizzardi, Giancarlo, Series Editor, Saad, Inès, editor, Rosenthal-Sabroux, Camille, editor, Gargouri, Faiez, editor, Chakhar, Salem, editor, Williams, Nigel, editor, and Haig, Ella, editor

Published
2024
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