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24th International Conference on Neural Information Processing (ICONIP)

作者： Zhao, Zhi-Lin Huang, Ling Wang, Chang-Dong Lai, Jian-Huang Yu, Philip S. Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Peoples R China Univ Illinois Dept Comp Sci Chicago IL USA Tsinghua Univ Inst Data Sci Beijing Peoples R China

ISBN: (纸本)9783319701394;9783319701387

Recently, recommendation algorithms have been widely used to improve the benefit of businesses and the satisfaction of users in many online platforms. However, most of the existing algorithms generate intermediate output when predicting ratings and the error of intermediate output will be propagated to the final results. Besides, since most algorithms predict all the unrated items, some predicted ratings may be unreliable and useless which will lower the efficiency and effectiveness of recommendation. To this end, we propose a Low-rank and Sparse Matrix Completion (LSMC) method which recovers rating matrix directly to improve the quality of rating prediction. Following the common methodology, we assume the structure of the predicted rating matrix is low-rank since rating is just connected with some factors of user and item. However, different from the existing methods, we assume the matrix is sparse so some unreliable predictions will be removed and important results will be retained. Besides, a slack variable will be used to prevent overfitting and weaken the influence of noisy data. Extensive experiments on four real-world datasets have been conducted to verify that the proposed method outperforms the state-of-the-art recommendation algorithms.

关键词： recommendation algorithms Low-rank Sparse

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SOCIAL TAGGING recommendation SYSTEM FOR SMART CITY ENVIRONMENTS

SOCIAL TAGGING RECOMMENDATION SYSTEM FOR SMART CITY ENVIRONM...

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IEEE International Conference on Multimedia & Expo (ICME)

作者： Alhamid, Mohammed F. King Saud Univ Software Engn Dept CCIS Riyadh Saudi Arabia

ISBN: (纸本)9781509015528

The increasing use of social network resources has increased the users demands through these networks. Analyzing social media enables us to benefit from the rich source of multimedia in a personalized, adaptive and systematic way for building smart cities environments. This paper proposes an algorithm to fill the gap between the social media resources, user personalized preferences and the collaborative opinions. The algorithm utilizes social tagging to contribute in increasing the quality for searching for places, services, and the discovering communities of the same interest. By exploiting social tagging, we propose a model which analyzes comments and feedback of posts published by users online. We demonstrate how the proposed algorithm can better utilize social information to improve the discovering of relevant items for a given user. The experiment results show feasibility of filtering and matching items of a similar interest for individual users and a social community.

关键词： Smart city social media collaborative analysis recommendation algorithms

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Visual Analysis of recommendation Performance 17

Visual Analysis of Recommendation Performance

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11th ACM Conference on Recommender Systems (RecSys)

作者： Coba, Ludovik Symeonidis, Panagiotis Zanker, Markus Free Univ Bozen Fac Comp Sci Piazza Domenicani 3 I-39100 Bolzano Italy

ISBN: (纸本)9781450346528

rrecsys is a novel library in R for developing and assessing recommendation algorithms. In this demo, we extend rrecsys with functions for visual analytics of recommendation performance, that is one of the strong capabilities of the R environment. In particular, we show how the library can be used to depict dataset characteristics, train and test recommendation algorithms and to visually assess, for instance, their capability to exploit long-tail items for making correct predictions.

关键词： recommendation algorithms R evaluation Visualization

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Auric: Using Data-driven recommendation to Automatically Generate Cellular Configuration 21

Auric: Using Data-driven Recommendation to Automatically Gen...

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ACM SIGCOMM Conference (SIGCOMM)

作者： Mahimkar, Ajay Sivakumar, Ashiwan Ge, Zihui Pathak, Shomik Biswas, Karunasish AT&T Dallas TX 75201 USA

ISBN: (纸本)9781450383837

Cellular service providers add carriers in the network in order to support the increasing demand in voice and data traffic and provide good quality of service to the users. Addition of new carriers requires the network operators to accurately configure their parameters for the desired behaviors. This is a challenging problem because of the large number of parameters related to various functions like user mobility, interference management and load balancing. Furthermore, the same parameters can have varying values across different locations to manage user and traffic behaviors as planned and respond appropriately to different signal propagation patterns and interference. Manual configuration is time-consuming, tedious and error-prone, which could result in poor quality of service. In this paper, we propose a new data-driven recommendation approach Auric to automatically and accurately generate configuration parameters for new carriers added in cellular networks. Our approach incorporates new algorithms based on collaborative filtering and geographical proximity to automatically determine similarity across existing carriers. We conduct a thorough evaluation using real-world LTE network data and observe a high accuracy (96%) across a large number of carriers and configuration parameters. We also share experiences from our deployment and use of Auric in production environments.

关键词： Cellular network configuration carrier addition collaborative filtering recommendation algorithms

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Coupling Semantic Association Graphs with Contrastive Learning in recommendation 27

Coupling Semantic Association Graphs with Contrastive Learni...

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27th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)

作者： Ji, Yifan Zhou, Jing Commun Univ China Sch Comp & Cyber Sci Beijing Peoples R China

ISBN: (纸本)9798350391961;9798350391954

Knowledge graphs (KGs) are typically used to enhance the performance of recommender systems by leveraging their characteristic to supplement the sparse user-item interaction data in the latter. This is because KGs feature abundant semantic information about entities (e.g. users or items in recommender systems) and inter-entity relationships. Deep learning has been seen gain wide use in boosting the performance of recommender systems. Thanks to focusing merely on nodes in a KG, or entities, such an approach, however, suffers from well capturing the semantics hidden in the node connectivity when learning node embeddings, leading to undesirable discovery of potential semantic associations between nodes. Against this background, we propose for recommender systems a multi-level cross-view contrastive learning mechanism that learns high-quality feature representations from unlabeled data. By incorporating item-item correlations into item embeddings, we developed an inter-item semantic association graph (SAG), which was intended to model more comprehensive and finer-grained inter-item semantic associations. A series of experiments have been carried out and the findings verify that the proposed mechanism outperforms other baseline models on all metrics, confirming the effectiveness of coupling SAGs with contrastive learning in enhancing the performance of recommender systems.

关键词： graph neural network semantic similarity contrastive learning knowledge graph recommendation algorithms

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Tuning in to Personalized Music: A Spotify API-Based Hybrid recommendation System Integrating Content-Based and Popularity-Based Approaches 2

Tuning in to Personalized Music: A Spotify API-Based Hybrid ...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Bachal, Tarush Kumar, Ashwin Bachal, Anish Gutte, Vitthal S. Mit World Peace University Department of Computer Engineering & Technology Maharashtra Pune India Mit World Peace University Department of Electronics and Communication Engineering Maharashtra Pune India Dr. Vishwanath Karad Mit World Peace University Department of Computer Engineering and Technology Maharashtra Pune India

ISBN: (纸本)9798350308846

This research paper offers a comprehensive approach for constructing a successful music recommendation system that integrates popularity- and content-based approaches. The suggested method intends to improve music discovery for users in an era of expansive music libraries and personalized user experiences. It makes use of the Spotify API to compile thorough track data and employs data preparation methods to guarantee data quality. The recommendation engine is the center of the research. Song recommendations are personalized by a content-based algorithm that bases them on aural features. In addition, a hybrid model balances user preferences and trending music by weighting track popularity by release date. A user-friendly GUI is included to make it easier for users to interact, allowing users to input their favorite music and get recommendations right away. Although the code serves as the basis for a strong recommendation system, later versions can include performance criteria for evaluation, such as accuracy, precision, and recall. This study contributes to the constantly developing field of music technology and user pleasure by providing a foundation for the improvement of music recommendation systems. © 2023 IEEE.

关键词： Content-Based Data Analysis Data Preprocessing Hybrid recommendation Personalization Popularity-Based recommendation algorithms Spotify API User Interface

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A recommendation system to support design patterns selection

A recommendation system to support design patterns selection

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2010 IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC 2010

作者： Navarro, Ignacio Díaz, Paloma Malizia, Alessio Computer Science Universidad Carlos III DEI Lab Leganés Madrid Spain

ISBN: (纸本)9780769542065

Pattern languages might be useful tools for design as far as designers can identify the right patterns. For this users need to understand the relationships between patterns and how to combine them. In this process, knowing how patterns have been used by other designers might help in identifying the best combination of solutions. In this paper we propose a module that recommends patterns based on the selection already made by them, with a view to supporting designers in making the correct design decisions. © 2010 IEEE.

关键词： component design decisions design patterns design patterns selection information filtering object-oriented methods pattern languages recommendation algorithms recommendation system recommender systems

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Thesis Reviewer recommendation Based on Multi-Graph Neural Networks

Thesis Reviewer Recommendation Based on Multi-Graph Neural N...

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2024 International Conference on Machine Learning and Intelligent Computing, MLIC 2024

作者： Wang, Tao Fu, Peng Zhang, Jing Chen, Kaixuan Nanjing University of Science and Technology Nanjing China Jiangsu Guidgine Educational Evaluation Inc. Nanjing China

Thesis review is a crucial step in ensuring the quality of academic theses, and accurately recommending reviewers for theses is currently a problem that needs to be addressed. When reviewer information is incomplete, it is difficult to achieve good recommendation results. This paper proposes a Multi-Graph Neural Network algorithm for review reviewer recommendation in thesis review. Based on the keyword-reviewer bipartite graph, a graph neural network model is constructed. By utilizing graph neural networks, high-order relationships between reviews and keywords can be explored, enabling the discovery of reviews' implicit research interests and expanding their research interests to some extent. Additionally, incorporating keyword-keyword interaction graphs and review-review interaction graphs allows for information exchange operations in the two graphs separately, enhancing the representation of keywords and reviews. We conducted experiments on real thesis reviews and compared the proposed algorithm with other recommendation algorithms. The results show that the proposed algorithm achieves favorable results across various evaluation metrics, demonstrating the effectiveness of the algorithm presented in this paper. © 2024 T. Wang, P. Fu, J. Zhang & K. Chen.

关键词： Bipartite Graph Graph Neural Networks recommendation algorithms Thesis Reviewer recommendation

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Composite recommendations： from items to packages

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Frontiers of Computer Science 2012年第3期6卷 264-277页

作者： Min XIE Laks V. S. LAKSHMANAN Peter T. WOOD Department of Computer Science University of British Columbia Vancouver V6T 1Z4 Canada Department of Computer Science and Information Systems Birkbeck University of London London WCIE 7HX UK

Classical recommender systems provide users with a list of recommendations where each recommendation consists of a single item, e.g., a book or DVD. However, sev- eral applications can benefit from a system capable of recom- mending packages of items, in the form of sets. Sample appli- cations include travel planning with a limited budget （price or time） and twitter users wanting to select worthwhile tweeters to follow, given that they can deal with only a bounded num- ber of tweets. In these contexts, there is a need for a system that can recommend the top-k packages for the user to choose from. Motivated by these applications, we consider composite recommendations, where each recommendation comprises a set of items. Each item has both a value （rating） and a cost associated with it, and the user specifies a maximum total cost （budget） for any recommended set of items. Our composite recommender system has access to one or more component recommender systems focusing on different do- mains, as well as to information sources which can provide the cost associated with each item. Because the problem of deciding whether there is a recommendation （package） whose cost is under a given budget and whose value exceeds some threshold is NP-complete, we devise several approximation algorithms for generating the top-k packages as recommen- dations. We analyze the efficiency as well as approximation quality of these algorithms. Finally, using two real and two synthetic datasets, we subject our algorithms to thorough ex- perimentation and empirical analysis. Our findings attest tothe efficiency and quality of our approximation algorithms for the top-k packages compared to exact algorithms.

关键词： recommendation algorithms optimization,top-k query processing

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Understanding Risk Preference and Risk Perception When Adopting High-Risk and Low-Risk AI Technologies

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International Journal of Human-Computer Interaction 2025年

作者： Wei, Mengyi Zhou, Kyrie Zhixuan Chen, Dongsheng Sanfilippo, Madelyn Rose Zhang, Puzhen Chen, Chuan Feng, Yu Meng, Liqiu Chair of Cartography and Visual Analytics Technical University of Munich Munich Germany School of Information Sciences University of Illinois at Urbana-Champaign Champaign IL United States Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio TX United States

Recent advances in AI have significantly changed people’s lives, yet sometimes their inherent risks deter adoption. Risk preference and perception in AI remain understudied. We surveyed 406 participants to explore how risk preferences, risk perceptions, and socioeconomic variables influence AI adoption in high-risk (autonomous vehicles) and low-risk (recommendation algorithms) contexts. Socioeconomic groups overall show different levels of risk aversion and seeking across scenarios. For high-risk autonomous driving, the risk aspects tend to be centralized. In contrast, the risk aspects of recommendation algorithms are more dispersed. These findings indicate a prevailing inclination among individuals toward caution regarding risks, highlighting the need for government policies that distinguish high- and low-risk AI. Regulations for autonomous vehicles should be strengthened to ensure safety and clarify liability, while those for recommendation algorithms should be expanded to enhance public risk awareness. This study aims to support policymakers toward more targeted AI risk management. © 2025 The Author(s). Published with license by Taylor & Francis Group, LLC.

关键词： AI adoption autonomous vehicles recommendation algorithms risk perception Risk preference

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