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56th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

作者： Yoon, Jisang Lee, Kangwook Suh, Changho Kakaocorp R&D Ctr Sungnam South Korea Korea Adv Inst Sci & Technol Sch EE Daejeon South Korea

ISBN: (纸本)9781538665961

We study the problem of recovering both K communities and their features from a labeled graph observation. We assume that the edges of an observed graph are generated as per the symmetric Stochastic Block Model (SBM), and that the label of each node is a noisy and partially-observed version of the corresponding community feature. We characterize the information-theoretic limit of this problem, and then propose a computationally efficient algorithm that achieves the information-theoretic limit.

关键词： Community recovery Stochastic Block Model Matrix completion recommendation algorithms Signal processing

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Opportunistic Multi-aspect Fairness through Personalized Re-ranking 20

Opportunistic Multi-aspect Fairness through Personalized Re-...

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Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization (UMAP)

作者： Sonboli, Nasim Eskandanian, Farzad Burke, Robin Liu, Weiwen Mobasher, Bamshad Univ Colorado Boulder CO 80309 USA DePaul Univ Chicago IL USA Chinese Univ Hong Kong Shatin Hong Kong Peoples R China

ISBN: (纸本)9781450368612

As recommender systems have become more widespread and moved into areas with greater social impact, such as employment and housing, researchers have begun to seek ways to ensure fairness in the results that such systems produce. This work has primarily focused on developing recommendation approaches in which fairness metrics are jointly optimized along with recommendation accuracy. However, the previous work had largely ignored how individual preferences may limit the ability of an algorithm to produce fair recommendations. Furthermore, with few exceptions, researchers have only considered scenarios in which fairness is measured relative to a single sensitive feature or attribute (such as race or gender). In this paper, we present a re-ranking approach to fairness-aware recommendation that learns individual preferences across multiple fairness dimensions and uses them to enhance provider fairness in recommendation results. Specifically, we show that our opportunistic and metric-agnostic approach achieves a better trade-off between accuracy and fairness than prior re-ranking approaches and does so across multiple fairness dimensions.

关键词： recommendation algorithms personalized reranking personalized diversity post processing fairness-aware reranking fairness opportunistic fairness reranking provider-side fairness fairness-aware recommendation algorithms

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The development of media quality in the Digital Age

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List Forum fur Wirtschafts- und Finanzpolitik 2024年第3期50卷 183-203页

作者： Barhoumi, Sahra Düsseldorf Institute for Competition Economics Universität Düsseldorf Düsseldorf Germany

The Digital Age has revolutionized the media landscape, reshaping how consumers access and interact with content. Consumers now have access to a wide range of media content, from physical copies to digital downloads and streaming. This has led to content creators detaching from traditional publishing and self-publishing their content on platforms like Wattpad, SoundCloud, and YouTube. The Digital Age has also added more remedies to signal quality to consumers, such as online reviews and personalized recommendations. This paper aims to analyze the impact of the Digital Age on the quality of media content in three different media fields (books, music, and films and series) by conducting an online survey among 245 German-speaking internet users in May 2022. Findings reveal a significant increase in access to content creators and content availability for consumers in the Digital Age. Moreover, the Digital Age introduces mechanisms that assist consumers in evaluating content quality, mitigating the adverse selection problem. The study concludes that media content quality in the Digital Age surpasses that of the Pre-Digital Age. For an enhanced consumer experience in the field of books, media providers should reconsider their algorithms, prioritizing content over purchasing patterns. Caution is advised in reinforcing intellectual property rights, emphasizing the need for judicious application to preserve content creation incentives. © The Author(s) 2024.

关键词： Content consumption Content creation Online platforms recommendation algorithms Self-publishing Survey

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Improving Social Collaborations in Virtual Learning Environments

Improving Social Collaborations in Virtual Learning Environm...

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OnTheMove (OTM) Federated Conference

作者： Gonzalez, Daniel Motz, Regina Tansini, Libertad Univ Republica Fac Ingn Montevideo Uruguay

ISBN: (纸本)9783319261386;9783319261379

This paper shows how to use social collaborations in virtual learning environments to motivate students in their learning process through recommendations of learning objects between peers. A hybrid recommender system is proposed and implemented in order to provide personalized recommendations for university students. An important contribution of this work is to show how to incorporate different recommendation approaches and techniques in order to produce useful recommendations in a real life scenario. Preliminary results indicate that information about groups of students as well as demographic information and previous evaluations of learning objects, processed with a combination of recommendation algorithms, show to be useful for the generation of personalized recommendations of learning objects.

关键词： Learning objects Hybrid recommender systems recommendation algorithms Virtual learning environments

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To Become Fashionable: A Brief Review of Outfit Compatibility

To Become Fashionable: A Brief Review of Outfit Compatibilit...

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IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)

作者： Feng, Ruining Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Peoples R China

ISBN: (纸本)9781728181172

How to dress fashionably has become a major concern for people nowadays. In order to match a fashionable outfit, we need to make our outfit as compatible as possible. Thus, we need to exploit an effective outfit matching scheme and apply it to help people match their own clothes. Outfit compatibility is an emergent computer vision field which tackles this problem. It refers to whether multiple fashion items look good if worn together. Recently, researchers have proposed different compatibility algorithms and recommendation algorithms to make an outfit as compatible as possible and fulfill the users' personal need. In this paper, we present a detailed overview of major compatibility algorithms, such as neural network, and major recommendation algorithms, such as collaborative filtering in outfit compatibility. This paper evaluates the models based on different tasks and datasets and concludes current applications, challenges and possible solutions in outfit compatibility.

关键词： Outfit compatibility Compatibility algorithms recommendation algorithms Neural Networks Graph Deep Learning Collaborative filtering Compatibility prediction

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A Health Management Platform based on CGM Device 3

A Health Management Platform based on CGM Device

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IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

作者： Zhang, Liangjie Zhong, Shigen Fujian Agr & Forestry Univ Dongfang Coll Dept Informat Engn Fuzhou Fujian Peoples R China Xiamen Nanyang Coll Sch Informat Engn Xiamen Fujian Peoples R China

ISBN: (纸本)9781538662434

People are more and more concerned about the health of modern life. Diabetes is the enemy of health. Forecast to 2030, diabetes will become the seventh largest cause of death. Diabetes is an important public health problem;it is one of the top four non communicable diseases in the world. So, how to detect blood sugar effectively is a question, how to prevent and treat diabetes is particularly important. In this paper, we used Internet of Things, big data, CGM, recommendation algorithms and other technologies to build a diet-based, drugs supplemented diabetes health management platform. Patients wear CGM device to detect blood sugar and other data, upload to the mobile app. Then mobile app uploads combined data to the diabetes health management platform. The platform through big data analysis to provide recommended diet and early warning, and relatives of the patients can understand the state of their parents anytime and anywhere.

关键词： diabetes CGM health management platform diet recommendation recommendation algorithms

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Vector Space Model Embedding for Recomender System Neural Networks 4

Vector Space Model Embedding for Recomender System Neural Ne...

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4th International Conference on Systems and Informatics (ICSAI)

作者： Wang, Yahui Craig, Paul Xian Jiaotong Liverpool Univ Dept Comp Sci & Software Engn Suzhou Peoples R China

ISBN: (纸本)9781538611074

Research shows that recommendation algorithms such as Collaborative Filtering (CF) can be enhanced using neural network (NN) to make more accurate recommendations. This project proposes an adaptive recommendation model based on NN to quickly access different items. A vector space embedding method is used to vectorize users and items before a Deep Neural Network (DNN) rating prediction network model is used to predict users' rating behavior. Multi-domain datasets are utilized in experiments to evaluate results and compared our method with traditional recommendation algorithms. Results show that the improved NN based recommendation model is more effective and achieves a higher score in both similarity calculation and predication.

关键词： recommendation algorithms Versatility Neural Network Embedding Method Rating Prediction Network

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Iterative Use of Weighted Voronoi Diagrams to Improve Scalability in Recommender Systems 19th

Iterative Use of Weighted Voronoi Diagrams to Improve Scalab...

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19th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Das, Joydeep Majumder, Subhashis Dutta, Debarshi Gupta, Prosenjit Heritage Acad Kolkata WB India Heritage Inst Technol Dept Comp Sci & Engn Kolkata WB India

ISBN: (纸本)9783319180380;9783319180373

Collaborative Filtering (CF) technique is used by most of the Recommender Systems (RS) for formulating suggestions of item relevant to users' interest. It typically associates a user with a community of like minded users, and then recommend items to the user liked by others in the community. However, with the rapid growth of the Web in terms of users and items, majority of the RS using CF technique suffers from the scalability problem. In order to address this scalability issue, we propose a decomposition based recommendation Algorithm using Multiplicatively Weighted Voronoi Diagrams. We divide the entire users' space into smaller regions based on the location, and then apply the recommendation Algorithm separately to these regions. This helps us to avoid computations over the entire data. We measure Spatial Autocorrelation indices in the regions or cells formed by the Voronoi decomposition. One of the main objectives of our work is to reduce the running time without compromising the recommendation quality much. This ensures scalability, allowing us to tackle bigger datasets using the same resources. We have tested our algorithms on the MovieLens and Book-Crossing datasets. Our proposed decomposition scheme is oblivious of the underlying recommendation algorithm.

关键词： Collaborative Filtering Spatial autocorrelation Weighted Voronoi Diagrams recommendation algorithms Scalability

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Research on personalized recommendation algorithm for micromechanical sensors based on cloud model

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2023年第1期9卷

作者： Geng, Baojun Sichuan Univ Pittsburgh Inst Chengdu 610200 Sichuan Peoples R China

In order to improve the ability of micromechanical sensors to update data-matching nodes in real-time and speed up the establishment of a neighbor relationship between nodes, this paper proposes a personalized recommendation algorithm for micromechanical sensors based on the cloud model to improve the stability and real-time performance of micromechanical sensors. The algorithm uses the service attribute values of the cloud computing model and cloud clustering method to set feature term labels and establish a cloud service similarity matrix so as to meet the user-matched manufacturing service requirements. The intra-class clustering technique is applied to measure the clustering effect, and the evaluation function of each clustering number is calculated on the basis of considering the time sequence to determine the best clustering result to complete the personalized recommendation path for micromechanical sensors. To verify the application effect of the personalized recommendation algorithm for micromechanical sensors based on the cloud model, experiments are conducted. The results show that the recommendation algorithm in this paper can always control the node energy consumption below 2.5x103, and the average discovery delay is stable between 41-43 seconds. And the sensor response time is 12.1 seconds, and the average absolute deviation value is 0.23, which is nearly 1.3 times smaller than 0.53 and 0.52 of the collaborative recommendation algorithm and hybrid recommendation algorithm. It can be seen that the recommendation algorithm in this paper solves the problem of excessive neighbor discovery delay in the communication process of micromechanical sensors and effectively improves the personalized recommendation performance of micromechanical sensors.

关键词： Micromechanical sensors Cloud clustering methods recommendation algorithms Cloud computing models Intra-class clustering techniques

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Fusion Privacy Protection of Graph Neural Network Points of Interest recommendation

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第4期14卷 548-556页

作者： Gan, Yong Hu, ZhenYu Zhengzhou Univ Light Ind Sch Comp & Commun Engn Zhengzhou Peoples R China

For the rapidly developing location-based web recommendation services, traditional point-of-interest(POI) recommendation methods not only fail to utilize user information efficiently, but also face the problem of privacy leakage. Therefore, this paper proposes a privacy-preserving interest point recommendation system that fuses location and user interaction information. The geolocation-based recommendation system uses convolutional neural networks (CNN) to extract the correlation between user and POI interactions and fuse text features, and then combine the location check-in probability to recommend POIs to users. To address the geolocation leakage problem, this paper proposes an algorithm that integrates k-anonymization techniques with homogenized coordinates (KMG) to generalize the real location of users. Finally, this paper integrates location-preserving algorithms and recommendation algorithms to build a privacy-preserving recommendation system. The system is analyzed by information entropy theory and has a high privacy-preserving effect. The experimental results show that the proposed recommendation system has better recommendation performance on the basis of privacy protection compared with other recommendation algorithms.

关键词： recommendation algorithms location protection graph convolutional neural networks k-anonymity

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