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Article
Engineering, Civil
Zhijian Lin et al.
Summary: In this paper, the importance of edge caching in reducing content retrieval latency and relieving the burden on backhaul links in 6G mmWave intelligent vehicular networks is investigated. The authors propose a group caching scheme and a hard-cored based caching algorithm, as well as study a joint caching and scheduling strategy. Simulation results demonstrate that the proposed scheme achieves higher performance in terms of request hit probability and the number of concurrent transmissions.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Le Wu et al.
Summary: Influenced by the success of deep learning, research in recommendation has shifted to developing new recommender models based on neural networks. This survey paper systematically reviews neural recommender models from the perspective of recommendation modeling with the accuracy goal, aiming to summarize the field and facilitate researchers and practitioners. It categorizes the work into collaborative filtering, content enriched recommendation, and temporal/sequential recommendation based on the data usage, and discusses promising directions in the field.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Information Systems
Yaru Fu et al.
Summary: In this paper, the interplay between personalized bundle recommendation and cache decision on the performance of wireless edge caching networks is explored from a revenue maximization perspective. The quantitative impact of bundle recommendation on different users' content request probability is examined, and the dependence of system revenue on bundle recommendation and caching policies is specified. A joint bundling, caching, and recommendation decision problem is formulated to maximize the achievable system revenue, considering the constraints of user-distinguished recommendation quality, recommendation amount, and cache capacity budget. A divide-then-conquer methodology is adopted to solve this non-tractable optimization problem, and detailed properties analysis for the proposed bundling and joint optimization algorithms is provided. Comprehensive numerical simulations validate the performance enhancement of the designed solutions compared to extensive conventional single-item recommendation oriented benchmarks.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Yong Liu et al.
Summary: This paper proposes a recommendation framework called CGAT, which explicitly utilizes both local and non-local graph context information of entities in a knowledge graph. CGAT captures local context information using a user-specific graph attention mechanism, and extracts non-local context using biased random walk sampling process and models the dependency using an RNN. It also incorporates an item-specific attention mechanism to capture the user's personalized preferences. Experimental results on real datasets demonstrate the effectiveness of CGAT compared to state-of-the-art KG-based recommendation methods.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Information Systems
Dimitra Tsigkari et al.
Summary: In this paper, a metric of streaming experience (MoSE) is defined to capture the tradeoff between streaming quality and recommendation quality. The joint optimization of caching and recommendations is proposed to maximize this metric, with the algorithm achieving significant performance gains over baseline schemes and existing algorithms.
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
(2022)
Article
Computer Science, Information Systems
Komal S. Khan et al.
Summary: This article introduces edge caching and incentive mechanisms for D2D communication. By clustering users with similar interests and optimizing cache hit probability, the performance of D2D communication can be improved. Additionally, a monetary incentive-based mechanism is proposed to increase user participation.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Artificial Intelligence
Wenhui Yu et al.
Summary: This paper proposes a dense and data-driven propagation mechanism for Graph Neural Networks (GNNs) to address the deficiencies of existing propagation strategies in recommendation tasks. By completing sparse graphs and using predicted graphs as propagation tools, the model learns a propagation matrix and uses a Self-propagation Graph Neural Network (SGNN) to propagate embeddings. Comprehensive experiments demonstrate the effectiveness and efficiency of the proposed model in recommendation tasks.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Heng-Shiou Sheu et al.
Summary: Personalized news recommendation aims to recommend news articles based on user preferences and short-term reading interests, and session-based news recommendation has recently attracted attention for recommending news articles within an active session. The CAGE approach improves semantic representations of news articles using external knowledge graphs, graph neural networks, and attention neural networks to model user preferences, outperforming competitive baselines in multiple news recommendation benchmark datasets.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Engineering, Multidisciplinary
Huan Zhou et al.
Summary: This paper proposes a reverse auction-based incentive mechanism, called RAIM, for providing data offloading services through OMNs. It also introduces a heuristic method and payment rule to address the problem, and validates their effectiveness through simulation results under different scenarios.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Information Systems
Priya Bhaskar Pandharbale et al.
Summary: This paper introduces a novel clustering-based approach called dynamic clustering (DCLUS) to improve the performance of service-oriented computing systems. By utilizing dynamic clustering technique, the clustering performance is optimized and the prediction accuracy of services is enhanced.
INTERNATIONAL JOURNAL OF INFORMATION SYSTEM MODELING AND DESIGN
(2022)
Article
Computer Science, Information Systems
Savvas Kastanakis et al.
Summary: The joint caching and recommendation paradigm has been proposed as a new way to increase the efficiency of mobile edge caching. This paper introduces a methodology for evaluating the performance of these schemes in real content services, with YouTube as a case study. Results show significant performance gains can be achieved in practice through cache-aware recommendations.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Priya Bhaskar Pandharbale et al.
Summary: This project proposes an improved clustering-based method for recommending web services, aiming to generate diverse recommendation results. The method combines functional interest, QoS preference, and diversity features to produce a unique recommendation list of web services.
INTERNATIONAL JOURNAL OF SYSTEM DYNAMICS APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Lina Yao et al.
Summary: Mashups have become a popular approach for data-centric applications, particularly mobile apps, in recent years. However, the difficulty lies in selecting the right services due to the lack of specific QoS information and formal semantic specifications in API recommendations. Existing methods need to improve diversity in recommendations and consider both explicit and implicit correlations among different APIs to enhance accuracy.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2021)
Article
Engineering, Electrical & Electronic
Xiaofei Wang et al.
Summary: This study proposes a D2D-assisted heterogeneous collaborative edge caching framework that optimizes node selection and cache replacement in mobile networks through flexible trilateral cooperation, using deep Q-learning network and attention-weighted federated deep reinforcement learning model. The effectiveness in reducing delay, improving hit rate, and offloading traffic is demonstrated, with proven convergence of the algorithm.
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
(2021)
Article
Engineering, Electrical & Electronic
Tao Fang et al.
Summary: This paper investigates content delivery in edge caching networks through D2D communications, aiming to maximize the number of requesters obtaining all desired contents by optimizing the selection of content sources. By applying hypergraph theory and formulating the problem as a hypergraph based cooperation cache game, the proposed log-linear based cooperation caching algorithm (LBCC) demonstrates superiority in finding Nash equilibrium points.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Engineering, Electrical & Electronic
Yaru Fu et al.
Summary: Edge side caching assisted D2D communication is a promising technique to reduce network latency and backhaul transmission burden. However, the effectiveness of caching strategies at the network edge depends on individual user's content preference distribution. Recommendation plays a crucial role in reshaping content request probabilities, impacting cache decisions significantly. Our research investigates how recommendation can enhance caching efficiency in D2D enabled wireless content caching networks, optimizing cache hit ratio and demonstrating the potential for sub-optimal algorithms.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
(2021)
Article
Engineering, Electrical & Electronic
Yaru Fu et al.
Summary: This paper presents a full-stack perspective involving content coding, caching, and recommendation for wireless edge caching networks to enhance network reliability and edge caching efficiency. By designing coding patterns and offering personalized recommendations, it can effectively recover data, improve successful content retrieval, and promote cache hit ratios.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
(2021)
Article
Computer Science, Information Systems
Changyan Yi et al.
Summary: This paper examines joint resource management for device-to-device (D2D) communication assisted multi-tier fog computing, presenting a complex resource optimization problem and its solution.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2021)
Article
Engineering, Electrical & Electronic
Huan Zhou et al.
Summary: An Incentive-driven and Deep Q Network (DQN) based Method, named IDQNM, utilizes a reverse auction as an incentive mechanism to motivate nodes to participate in D2D offloading and content caching in order to maximize the CSP's saving cost.
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
(2021)
Proceedings Paper
Engineering, Electrical & Electronic
S. Krishnendu et al.
Summary: This paper proposes an algorithm for joint optimization of caching and recommendation, simulating the influence of recommendations on popularity through a probability transition matrix to maximize cache hit rate and providing theoretical guarantees on algorithm performance. Simulation results demonstrate that the proposed algorithm significantly outperforms existing algorithms in terms of average cache hit rate.
2021 IEEE 93RD VEHICULAR TECHNOLOGY CONFERENCE (VTC2021-SPRING)
(2021)
Article
Computer Science, Information Systems
Junmei Hao et al.
Summary: This paper proposes an annular-graph attention based sequential recommendation model that explores user's long-term and short-term preferences for personalized recommendations. Short-term preferences are captured by building an annular-graph and applying graph attention for local and global features, while long-term preferences are handled through the introduction of latent factor models. Experimental results show that the model outperforms current state-of-the-art methods on two public datasets.
IEEE TRANSACTIONS ON MULTIMEDIA
(2021)
Article
Computer Science, Interdisciplinary Applications
Mamata Garanayak et al.
Summary: Agriculture plays a crucial role in India, but the impact of temperature variations has led to poor crop growth. Accurate forecasting is essential for crop management in order to support the provision of agricultural occupation.
INTERNATIONAL JOURNAL OF AGRICULTURAL AND ENVIRONMENTAL INFORMATION SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Huan Zhou et al.
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
(2020)
Article
Engineering, Electrical & Electronic
Roy Karasik et al.
IEEE TRANSACTIONS ON COMMUNICATIONS
(2020)
Article
Automation & Control Systems
Jie Tang et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2020)
Article
Computer Science, Information Systems
Lianxin Yang et al.
IEEE TRANSACTIONS ON MULTIMEDIA
(2020)
Article
Computer Science, Information Systems
Jiaqi Liu et al.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2020)
Article
Engineering, Electrical & Electronic
Ran Zhang et al.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
(2020)
Article
Computer Science, Information Systems
Livia Elena Chatzieleftheriou et al.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2019)
Article
Engineering, Electrical & Electronic
Wen Wu et al.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2019)
Article
Computer Science, Hardware & Architecture
Yin Zhang et al.
IEEE WIRELESS COMMUNICATIONS
(2019)
Article
Engineering, Electrical & Electronic
Tiankui Zhang et al.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2019)
Proceedings Paper
Computer Science, Software Engineering
Venkatraman Balasubramanian et al.
2019 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME)
(2019)
Article
Engineering, Electrical & Electronic
Pavios Sermpezis et al.
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
(2018)
Article
Computer Science, Information Systems
Changyan Yi et al.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2018)
Article
Computer Science, Hardware & Architecture
Zongqing Lu et al.
IEEE-ACM TRANSACTIONS ON NETWORKING
(2017)
Article
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Bo Han et al.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2012)
Article
Geochemistry & Geophysics
IV Zaliapin et al.
PURE AND APPLIED GEOPHYSICS
(2005)
