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ACM/IEEE 44th International Conference on Software Engineering - Software Engineering in Practice (ICSE-SEIP)

作者： Chen, Qiuyuan Kong, Dezhen Bao, Lingfeng Sun, Chenxing Xia, Xin Li, Shanping Zhejiang Univ Hangzhou Zhejiang Peoples R China Tencent Technol Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781665495905

Code review is essential for assuring system quality in software engineering. Over decades in practice, code review has evolved to be a lightweight tool-based process focusing on code change: the smallest unit of the development cycle, and we refer to it as Modern Code Review (MCR). MCR involves code contributors committing code changes and code reviewers reviewing the assigned code changes. Such a reviewer assigning process is challenged by efficiently finding appropriate reviewers. Recent studies propose automated code reviewer recommendation (CRR) approaches to resolve such challenges. These approaches are often evaluated on open-source projects and obtain promising performance. However, the code reviewer recommendation systems are not widely used on proprietary projects, and most current reviewer selecting practice is still manual or, at best, semi-manual. No previous work systematically evaluated these approaches' effectiveness and compared each other on proprietary projects in practice. In this paper, we performed a quantitative analysis of typical recommendation approaches on proprietary projects in Tencent. The results show an imperfect performance of these approaches on proprietary projects and reveal practical challenges like the "cold start problem". To better understand practical challenges, we interviewed practitioners about the expectations of applying reviewer recommendations to a production environment. The interview involves the current systems' limitations, expected application scenario, and information requirements. Finally, we discuss the implications and the direction of practical code reviewer recommendation tools.

关键词： code review code reviewer recommendation recommendation algorithm

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Application of Intelligent recommendation for Agricultural Information: A Systematic Literature Review

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IEEE ACCESS 2021年 9卷 153616-153632页

作者： Song, Caixia Dong, Haoyu Qingdao Agr Univ Coll Sci & Informat Qingdao 266109 Peoples R China

With the ever-growing volume of online information, recommender system has been an effective strategy to overcome such information overload. Recommender systems are widely used in many web applications, such as e-commerce, news, agriculture and other fields. Agricultural informatization is an important driving force for the development of agricultural modernization. With the further improvement of agricultural informatization infrastructure construction, the use of modern information technology to achieve personalized agricultural information resource recommendation services and provide users with timely and effective information has become an effective solution. This article aims to provide a comprehensive review of recent research efforts on application of agricultural information based on intelligent recommender systems. Firstly, the method of content analysis used in this article to sort out the papers is introduced. Secondly, the background concepts of recommender systems and the key technologies are presented. Thirdly, the applications of recommender systems/technologies for agricultural information are described in detail. Finally, a summary and outlook on the application of recommender systems for agricultural information are provided.

关键词： Recommender systems Collaboration Social networking (online) Clustering algorithms Agricultural products Prediction algorithms Licenses recommendation algorithm recommender system application model agricultural information collaborative filtering hybrid recommendation

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Research on Adaptive QoE Global Bandwidth Allocation algorithm Based on Enhanced Greed

Research on Adaptive QoE Global Bandwidth Allocation Algorit...

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IEEE/CIC International Conference on Communications in China (ICCC)

作者： Li, Shufeng Li, Tianchi Liu, Jianzhang Commun Univ China Sch Informat & Commun Engn Beijing Peoples R China

ISBN: (纸本)9798350378412

With the rapid development of the Internet and the improvement of computing and storage capabilities, ultra-high-definition technology has been rapidly applied in various fields. In the current domain of streaming media broadcasting, providing the necessary network bandwidth for ultra-high-definition cloud performing arts services has become a critical issue. Despite the establishment of Quality of Experience evaluation models, bandwidth allocation algorithms based on QoE still face challenges such as accuracy, user satisfaction, and low bandwidth utilization. In response to these issues, this paper proposes an Adaptive QoE Global Bandwidth Allocation algorithm Based on Enhanced Greed. Building upon existing bandwidth allocation algorithms, this proposed algorithm incorporates greedy algorithms and enhances the profit calculation method and execution strategy of the greedy algorithm to maximize the satisfaction rate of user QoE demands and bandwidth utilization. Simulation results demonstrate the outstanding performance of the proposed algorithm in terms of bandwidth allocation accuracy, user QoE satisfaction rate, and bandwidth utilization.

关键词： UHD cloud performing arts live quality of experience recommendation algorithm bandwidth allocation

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Click-Through Rate Prediction algorithm Based on Modeling of Implicit High-Order Feature Importance

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JOURNAL OF INTERNET TECHNOLOGY 2022年第5期23卷 1077-1086页

作者： Yang, Qing Li, Ning Hu, Shiyan Li, Heyong Zhang, Jingwei Guilin Univ Elect Technol Guangxi Key Lab Automat Detecting Technol & Instru Guilin Guangxi Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Trusted Software Guilin Guangxi Peoples R China

Click-through rate (CTR) prediction plays a central role in online advertising and recommendation systems. In recent years, with the successful application of deep neural networks (DNNs) in many fields, researchers have integrated deep learning into CTR prediction algorithms to model implicit high-order features. However, most of these existing methods unify the weights of implicit higher-order features to predict user behaviors. The importance of such features of different dimensions for predicting user click behaviors are different. Base on this, we propose a prediction method that dynamically learns the importance of implicit high-order features. Specifically, we integrate the output features of deep and shallow components, and adaptively learn the weights of implicit high-order features from among all features through the designed attention network, which effectively capturing the deep interests of users. In addition, this framework has strong versatility and can be combined with shallow models such as Logistic Regression (LR) and Factorization Machines (FMs) to form different models and achieve optimal performance. The extended experiment is conducted on two large-scale datasets, AVAZU and SafeDrive, and the experimental results show that the performance of the proposed model is superior to that of existing baseline models.

关键词： recommendation algorithm Click-through rate Implicit high order feature Attention network

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A collaborative filtering algorithm based on item labels and Hellinger distance for sparse data

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JOURNAL OF INFORMATION SCIENCE 2022年第6期48卷 749-766页

作者： Wang, Yong Zhao, Xuhui Zhang, Zhiqiang Zhang, Leo Yu Chongqing Univ Posts & Telecommun Chongqing 400065 Peoples R China Deakin Univ Geelong Vic Australia

The Neighbourhood-based collaborative filtering (CF) algorithm has been widely used in recommender systems. To enhance the adaptability to the sparse data, a CF with new similarity measure and prediction method is proposed. The new similarity measure is designed based on the Hellinger distance of item labels, which overcomes the problem of depending on common-rated items (co-rated items). In the proposed prediction method, we present a new strategy to solve the problem that the neighbour users do not rate the target item, that is, the most similar item rated by the neighbour user is used to replace the target item. The proposed prediction method can significantly improve the utilisation of neighbours and obviously increase the accuracy of prediction. The experimental results on two benchmark datasets both confirm that the proposed algorithm can effectively alleviate the sparse data problem and improve the recommendation results.

关键词： Collaborative filtering Hellinger distance recommendation algorithm similarity measure sparse data

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Analysis of Xinhua’s Cross-Platform Discourse on WeChat Public Media Under the Background of Big Data

Analysis of Xinhua’s Cross-Platform Discourse on WeChat Pub...

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作者： Sun, Yike Yu, Tianxin Shridhar, V. Advertising Institute Communication University of China Beijing China Academy of Literature Guangxi Minzu University Nanning China Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Karnataka Bengaluru India

With the development of media technology, the audience’s news contact habits have changed. Media integration is an important measure to respond to the shift in digital news dissemination. Cross-platform dissemination of news discourse is an important content of mainstream media communication practice on social media platforms. Previous research on media integration has focused on the integration of media channels and digital technology, with less in-depth exploration of the language characteristics of the two media at the micro-discourse level. Based on the background data of Xinhua News Agency's official account, this paper analyzes the language characteristics of the title of Xinhua News Agency’s WeChat official account, with a view to explaining the relationship between Xinhua News Agency’s discourse characteristics and communication effects from a linguistic perspective, providing a new perspective for theoretical research and theoretical support for business practice. The research is conducted under the control dialectical relationship theory and believes that there are three parties involved in the WeChat news discourse of Xinhua News Agency: national will, network subculture, and commercial interests. Xinhua News Agency still holds the dominant position in promoting online value. Xinhua News Agency uses online discourse technology to express the will of the country. algorithm visibility is the main factor affecting the discourse technology and dissemination path of Xinhua News Agency’s WeChat news. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Critical discourse Dialectical theory of control recommendation algorithm Social media

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Link recommendation Beyond Homophily and an Extension to Recommender Systems

Link Recommendation Beyond Homophily and an Extension to Rec...

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作者： Yin, Kexin University of Delaware

学位级别：Ph.D., Doctor of Philosophy

Online social media has gained soaring popularity and generated unprecedented commercial values because of their penetration into people's daily lives. As one of the essential functions of online social media, recommendation algorithms have attracted considerable attention from academia and industry. A recommendation algorithm with high prediction accuracy benefits both users and business operators significantly. Aiming at improving the prediction accuracy of the state-of-the art recommendation algorithms, this dissertation proposes three novel recommendation problems and develops novel solution methods to these problems. In the first study of this dissertation, we propose a novel link recommendation problem, and develop its solution method, namely the Diversity Preference-Aware Link recommendation (DPA-LR) method. Traditional link recommendation methods overlook the importance of users' diversity preferences. According to social and psychology theories, people's diversity preference is an essential driver in friendship formation and should be utilized to increase link prediction accuracy. To address this research gap, we formulate the new DPA-LR problem and design its novel solution method. We then demonstrate the superiority of the DPA-LR method over benchmarks with two large-scale social network datasets. In the second study, we extend the DPA-LR method to recommender systems. Diversity and novelty are essential objectives in recommender systems to improve stakeholders' benefits by reducing user's discovery efforts and improving business operators' sales and revenue. Existing diversity and novelty-based methods indifferently increase diversity or novelty for every user, which inevitably induces the trade-off dilemma between relevance and accuracy. Moreover, different users have different preferences for recommendation diversity and novelty. Such preference should be considered by a recommendation algorithm, thereby avoiding the trade-off dilemma and increasing th

关键词： Optimization recommendation algorithm Online social networks

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Design of an algorithm for recommending elective courses based on collaborative filtering

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2022年第6期22卷 2173-2184页

作者： Yu, Jian Xiong, Zenggang Bao, Qi Ning, Xiao Hubei Engn Univ Sch Comp & Informat Sci Xiaogan 432000 Hubei Peoples R China Management Comm Hubei Yingcheng Econ Dev Zone Yingcheng Hubei Peoples R China Xiaogan Power Supply Co State Grid Hubei Elect Po Xiaogan Hubei Peoples R China

At present, college students generally choose courses according to their own interests or understanding of the course, which has a certain subjectivity and blindness. In many cases, students know little about the courses before class, and only rely on the course name to guess the course content, so as to decide whether to take this course. However, the existing studies are mainly aiming at online learning resources which are heterogeneous, these methods cannot be effectively applied to the recommendation of university courses. This paper explores improve collaborative filtering for university application environments, provides a knowledge recommendation algorithm for university elective courses. First, we created individual models of the course and the students based on background information. Next, we use context-based recommendation and "Parent Class Filling" method to reduce the impact of Cold Start and Sparsity problem on the initial stage of the system. Then, recommendations are generated based on the course evaluation model and similarity matrix. We select several commonly used algorithms to achieve the recommendation, and the experimental results proved that the proposed algorithm is accurate and effective.

关键词： recommendation algorithm collaborative filtering course selection system

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Privacy-Preserving Mobility-Aware Federated Collaborative Filtering Framework for Caching Prediction in Vehicular Networks 20

Privacy-Preserving Mobility-Aware Federated Collaborative Fi...

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20th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)

作者： Ouyang, Xinzhi Feng, Chenyuan Feng, Daquan Yang, Howard H. State Key Lab Mobile Network & Mobile Multimedia Shenzhen 518055 Peoples R China Shenzhen Univ Shenzhen Key Lab Digital Creat Technol Guangdong Hong Kong Joint Lab Big Data Imaging & Shenzhen 518060 Peoples R China Zhejiang Univ Zhejiang Univ Univ Illinois Urbana Champaign Inst Haining Peoples R China

ISBN: (纸本)9798350300529

recommendation algorithm can effectively reduce the difficulty of proactive edge caching prediction by excavating users' preferences among the massive contents, which has drawn great attentions from both academia and industry. The effectiveness of prediction models depends on big data analysis of user information, however, traditional methods based on centralized learning become more and more impractical due to the growing concern on privacy data protection. Recently, implementing the recommendation algorithm in a federated learning (FL) manner has emerged as a promising approach. In an FL manner, users are allowed to keep their private data local and upload the model parameters learned by local training to the server for collaborative training. In this work, we propose a proactive caching prediction algorithm for mobile vehicle users based on differential privacy and federate learning. Our proposed algorithm not only predicts the popular contents with a strong protection for users' private data, but also applies to large-scale networks with massive mobile users. In addition, we also investigate the impact of user mobility on the caching prediction accuracy, and propose an attention-based model aggregation mechanism, which assigns different aggregation weights to each vehicle user and edge server to mitigate the performance degradation caused by user movement. The results show that our proposed model can obtain high caching prediction accuracy and strong privacy protection level in vehicular networks.

关键词： Caching prediction recommendation algorithm federated learning user mobility differential privacy

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Aggregate Distillation for Top-K Recommender System 32nd

Aggregate Distillation for Top-K Recommender System

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32nd International Conference on Artificial Neural Networks (ICANN)

作者： Shen, Hao Chu, Tengfei Yang, Yulou Yang, Ming Nanjing Normal Univ Nanjing Peoples R China

ISBN: (纸本)9783031441943;9783031441950

As knowledge distillation techniques have gained prominence in recommender systems (RS), they have proven instrumental in maintaining exceptional recommendation performance while simultaneously reducing model size. Nonetheless, conventional single-teacher bidirectional distillation methods present two significant limitations. First, the restricted knowledge from a solitary teacher hinders comprehensive learning for student model. Second, both teacher and student models are susceptible to overfitting due to the traditional distillation loss function. In response to these challenges, we introduce an innovative Aggregated Distillation (AD) framework explicitly designed for RS. We propose an ensemble-based bidirectional knowledge distillation method, utilizing multiple teacher models to furnish a more diverse knowledge base and capitalizing on their synergistic effects to bolster the student model's performance. Moreover, we devise a novel regularization module that integrates a high-performance teacher model (supervisor) into the primary model. This module mitigates overfitting by directing the principal model towards the gradient direction of the incorporated high-performance teacher model, diminishing the risk of optimization towards an overfitting gradient direction. Rigorous experiments on two real-world datasets substantiate that our proposed framework outperforms its counterparts, with each component contributing significantly to the superior performance.

关键词： recommendation algorithm Knowledge Distillation Deep Learning Ensemble Learning

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