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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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A Path-based recommendations Approach for Online Systems via Hyperbolic Network Embedding

A Path-based Recommendations Approach for Online Systems via...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Papadis, Nikolaos Stai, Eleni Karyotis, Vasileios Natl Tech Univ Athens Sch Elect & Comp Engn Athens 15780 Greece Ecole Polytech Fed Lausanne Sch Comp & Commun Sci CH-1015 Lausanne Switzerland

ISBN: (纸本)9781538616291

In this paper we introduce and demonstrate new recommendation algorithms for large-scale online systems, such as e-shops and cloud services. The proposed algorithms are based on the combination of network embedding in hyperbolic space with greedy routing, exploiting properties of hyperbolic metric spaces. Contrary to the existing recommender systems that rank products in order to propose the highest ranked ones to the users, our proposed recommender system creates a progressive path of recommendations towards a final (known or inferred) target product using greedy routing over networks embedded in hyperbolic space. Thus, it prepares the user by intermediate recommendations for maximizing the chances that he/she accepts the recommendation of the target product(s). This casts the problem of locating a suitable recommendation as a path problem, where leveraging on the efficiency of greedy routing in graphs embedded in hyperbolic spaces and exploiting special network structure, if any, pays dividends. Two variants of our recommendation approach are provided, namely Hyperbolic recommendation-Known Destination (HRKD), Hyperbolic recommendation-Unknown Destination (HRUD), when the target product is known or unknown, respectively. We demonstrate how the proposed approach can be used for producing efficient recommendations in online systems, along with studying the impact of the several parameters involved in its performance via proper emulation of user activity over suitably defined graphs.

关键词： recommendation algorithms Online social networks E-commerce Network embedding Greedy routing Hyperbolic spaces Path problems

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Bias Disparity in Graph-Based Collaborative Filtering Recommenders 22

Bias Disparity in Graph-Based Collaborative Filtering Recomm...

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37th Annual ACM Symposium on Applied Computing

作者： Boltsis, Georgios Pitoura, Evaggelia Univ Ioannina Comp Sci & Engn Dept Ioannina Greece

ISBN: (纸本)9781450387132

In collaborative filtering (CF) recommendations, user preferences may be reinforced resulting in the under-representation of specific categories of items in the recommendation lists received by users belonging to specific groups. We call this phenomenon bias disparity. In this paper, we focus on CF recommenders that exploit the bipartite user-item interaction graph and present algorithms that modify the random walks on this graph to address bias disparity. We evaluate experimentally both the conditions under which bias disparity appears and the effectiveness of our algorithms.

关键词： recommendation algorithms bias fairness collaborative filtering

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Analyzing the Interplay between Diversity of News recommendations and Misinformation Spread in Social Media 32

Analyzing the Interplay between Diversity of News Recommenda...

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32nd ACM Conference on User Modeling, Adaptation and Personalization (ACM UMAP)

作者： Pathak, Royal Spezzano, Francesca Boise State Univ Boise ID 83725 USA

ISBN: (纸本)9798400704666

Recommender systems play a crucial role in social media platforms, especially in the context of news, by assisting users in discovering relevant news. However, these systems can inadvertently contribute to increased personalization, and the formation of filter bubbles and echo chambers, thereby aiding in the propagation of fake news or misinformation. This study specifically focuses on examining the tradeoffs between the diversity of news recommendations and the dissemination of misinformation on social media. We evaluated classical recommender algorithms on two Twitter (now X) datasets to assess the diversity of top-10 recommendation lists and simulated the propagation of recommended misinformation within the user network to analyze the impact of diversity on mis-information spread. The research findings indicate that an increase in news recommendation diversity indeed contributes to mitigating the propagation of misinformation. Additionally, collaborative and content-based recommender systems provide more diversity in comparison to popularity and network-based systems, resulting in less misinformation propagation. Our study underscores the crucial role of diversity recommendations in mitigating misinformation propagation, offering valuable insights for designing misinformation-aware recommender systems and diversity-based misinformation intervention.

关键词： diversity misinformation recommendation algorithms echo chambers

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Novelty-Aware Matrix Factorization Based on Items' Popularity 17th

Novelty-Aware Matrix Factorization Based on Items' Popularit...

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17th International Conference of the Italian-Association-for-Artificial-Intelligence (AI*IA)

作者： Coba, Ludovik Symeonidis, Panagiotis Zanker, Markus Free Univ Bozen Bolzano I-39100 Bozen Bolzano Italy

ISBN: (纸本)9783030038403;9783030038397

The search for unfamiliar experiences and novelty is one of the main drivers behind all human activities, equally important with harm avoidance and reward dependence. A recommender system personalizes suggestions to individuals to help them in their exploration tasks. In the ideal case, these recommendations, except of being accurate, should be also novel. However, up to now most platforms fail to provide both novel and accurate recommendations. For example, a well-known recommendation algorithm, such as matrix factorization (MF), tries to optimize only the accuracy criterion, while disregards the novelty of recommended items. In this paper, we propose a new model, denoted as popularity-based NMF, that allows to trade-off the MF performance with respect to the criteria of novelty, while only minimally compromising on accuracy. Our experimental results demonstrate that we attain high accuracy by recommending also novel items.

关键词： recommendation algorithms Evaluation Novelty Collaborative filtering Matrix factorization

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Challenges and Perspectives of Recommender Systems in Vending Machines

Challenges and Perspectives of Recommender Systems in Vendin...

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IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus)

作者： Deryabina, Polina S. Iolshina, Valeriya M. Markova, Angelina, V Shevskaya, Natalya, V Belov, Vladimir St Petersburg Electrotech Univ LETI Dept Software Engn & Comp Applicat St Petersburg Russia St Petersburg Electrotech Univ LETI Dept Qual Management St Petersburg Russia

ISBN: (纸本)9781665404761

This paper describes the features of using recommendation algorithms for vending machines. A vending machine is an automated machine with a significantly limited range of products (snacks, beverages, cigarettes), and it also does not provide any additional information about the customer, which imposes some limitations on the predicting relevant products. In order to increase the demand for vending products and make a profit, this paper proposes to consider how modern recommendation algorithms can be applied in this area. One of the important stages in building recommendation system is preparing data for analysis, so this paper describes an approach for grouping data by sales intensity and also considers the set cover problem.

关键词： recommendation systems recommendation algorithms data analysis preparing data vending machines set cover problem grouping data machine learning

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An Enhanced Content-Based Recommender System for Academic Social Networks 4

An Enhanced Content-Based Recommender System for Academic So...

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IEEE Fourth International Conference on Big Data and Cloud Computing (BdCloud)

作者： Rohani, Vala Ali Kasirun, Zarinah Mohd Ratnavelu, Kuru Univ Malaya Fac Comp Sci & Informat Technol Kuala Lumpur 50603 Malaysia Univ Malaya Fac Sci Inst Math Sci Kuala Lumpur 50603 Malaysia

ISBN: (纸本)9781479967193

the present study utilizes social computing techniques to enhance the content-based recommender systems. Coined as Enhanced Content-based Algorithm using Social Networking (ECSN), this recommender algorithm is applied in academic social networks to suggest the most relevant items to members of these online societies. In addition to considering user's own preferences, ECSN takes advantage of the interest and preferences of user's friends and faculty mates for providing more accurate recommendations. The research experiments were conducted by applying four different algorithms -random, collaborative, content-based, and ECSN, for 14 consecutive weeks. During this period, 1398 academic items were recommended to all 920 members of Malaysian Experts Academic Social Network (MyExpert). ANOVA tests indicate that the proposed algorithm significantly improves the prediction accuracy of algorithms based on well-known measurements of precision, fallout and F1. It is believed that this study can make a significant contribution to the level of user satisfaction in academic social networks.

关键词： recommendation algorithms Prediction Accuracy Academic Social Networks Content-based recommender systems

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Research on E-commerce Content recommendation System Based on Fuzzy ART

Research on E-commerce Content Recommendation System Based o...

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作者： Guo Xin Xiao Min Zhixing College of Hubei University Taishan University

With the popularity of the Internet and the improvement of people's needs, the problem of information overload corresponding to e-commerce is very prominent. In this context, an effective e-commerce recommendation system is developed to provide personalized recommendation services for users, so as to provide service guarantee for users to find the products they need. The wide application of recommendation system provides good technical support for convenient life, but there are many problems and challenges behind the rise of this technology. To improve these problems is to promote the development of recommendation system towards a more humane direction. On this basis, this paper focuses on the research of e-commerce content recommendation system based on Fuzzy ART, which provides basic ideas for the optimization of commodity recommendation services.

关键词： Fuzzy ART E-commerce recommendation System recommendation algorithms

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