Bio: Yinan Feng is an academic researcher from Huazhong University of Science and Technology. The author has contributed to research in topics: Computer science & Physics. The author has an hindex of 3, co-authored 3 publications receiving 32 citations.
TL;DR: This paper forms the personalized video big data retrieval problem as an interaction between the user and the system via a stochastic process, not just a similarity matching, accuracy (feedback) model of the retrieval; introduces users’ real-time context into the retrieval system; and proposes a general framework.
Abstract: Online video sharing (e.g., via YouTube or YouKu) has emerged as one of the most important services in the current Internet, where billions of videos on the cloud are awaiting exploration. Hence, a personalized video retrieval system is needed to help users find interesting videos from big data content. Two of the main challenges are to process the increasing amount of video big data and resolve the accompanying “cold start” issue efficiently. Another challenge is to satisfy the users’ need for personalized retrieval results, of which the accuracy is unknown. In this paper, we formulate the personalized video big data retrieval problem as an interaction between the user and the system via a stochastic process, not just a similarity matching, accuracy (feedback) model of the retrieval; introduce users’ real-time context into the retrieval system; and propose a general framework for this problem. By using a novel contextual multiarmed bandit-based algorithm to balance the accuracy and efficiency, we propose a context-based online big-data-oriented personalized video retrieval system. This system can support datasets that are dynamically increasing in size and has the property of cross-modal retrieval. Our approach provides accurate retrieval results with sublinear regret and linear storage complexity and significantly improves the learning speed. Furthermore, by learning for a cluster of similar contexts simultaneously, we can realize sublinear storage complexity with the same regret but slightly poorer performance on the “cold start” issue compared to the previous approach. We validate our theoretical results experimentally on a tremendously large dataset; the results demonstrate that the proposed algorithms outperform existing bandit-based online learning methods in terms of accuracy and efficiency and the adaptation from the bandit framework offers additional benefits.
01 Mar 2018
TL;DR: This paper model this issue as a novel contextual multiarmed bandit based Monte Carlo tree search problem and proposes a big data support online learning algorithm to meet the demand of content push with low cost and theoretically proves that it achieves sublinear regret bound and sublinear storage, which are very efficient in the big data context and do not increase the network burden.
Abstract: With the rapid growth of the social network, information overload becomes a critical issue. Service providers push a lot of redundant contents and advertisements to users every day. Thus, users’ interests and the probability of reading them have dropped considerably and the network load is wasted. To address this issue, accurate content push is needed, where the main challenges are proving precise descriptions of users and supporting the big data nature of users and contents. Content-centric networking (CCN) has emerged as a new network architecture to meet today's requirement for content access and delivery. By using the named content, CCN makes it possible to track users’ real-time interests and motivates us studying a novel content accurate push (or called content recommendation) system. In this paper, we model this issue as a novel contextual multiarmed bandit based Monte Carlo tree search problem and propose a big data support online learning algorithm to meet the demand of content push with low cost. To avoid destroying CCN's energy efficient feature, the energy consumption is considered into our module. Then, we theoretically prove that our online learning algorithm achieves sublinear regret bound and sublinear storage, which is very efficient in the big data context and do not increase the network burden. Experiments in an offline collected dataset show that our approach significantly increases the accuracy and convergence speed against other state-of-the-art bandit algorithms and can overcome the cold start problem as well.
••01 Nov 2017
TL;DR: This paper proposes an online learning system for IoT service recommendation based on a contextual multi-armed bandit algorithm that significantly improves recommendation accuracy compared to other IoT recommendation algorithms and bandit approaches.
Abstract: Due to the advances of wireless sensor networks, radiofrequency identification (RFID) and Web-based services, large volume of devices have been interconnected to the Internet of Things (IoT). In addition, the tremendous number of IoT services provided by service providers arises an urgent need to propose effective recommendation methods to discover suitable services to users. In this paper, we propose an online learning system for IoT service recommendation based on a contextual multi-armed bandit algorithm. Our system learns online probably useful services for users which are not known by them based on context (e.g. spatiotemporal information, users type, device settings, etc.). We cluster services and contexts online as tree structures for computational efficiency. This approach significantly improves recommendation accuracy compared to other IoT recommendation algorithms and bandit approaches. Fusing the user-centered context with service-centered context, the system addresses the cold start problem and performs well even when users and services are in large scale. Furthermore, experiments based on massive dataset prove that our system achieves sublinear regret in the long-run and reduces the storage complexity to sublinear level, which means our algorithm provides online big data support. The experiments perfectly validate our theoretical analysis.
TL;DR: Based on the continuous satellite data, Wang et al. as mentioned in this paper revealed the spatial mismatch between water resources supply and grain growth pattern in China, which caused the depletion of grain production potential in the water-limited regions, while the southeastern regions with higher potential still have more capacity for agricultural production.
Abstract: China has achieved sustained growth in grain production and significant changes in grain patterns since the early 21st century. Meanwhile, the contradiction between the shortage of water resources and the development of agriculture is becoming more and more severe. This study introduced Gravity Recovery and Climate Experiment (GRACE) gravity satellite Total Water Storage (TWS) Product to indicate total water storage and calculated the Cumulated Normalized Difference Vegetation Index (CNDVI) of cropland as an indicator for grain growth. Based on the continuous satellite data, this paper revealed the spatial mismatch between water resources supply and grain growth pattern in China. The center of gravity of the CNDVI tends to move northwest, while the GRACE TWS data’s center of gravity is in the opposite direction. There were different relationships between GRACE-TWS and CNDVI changes in different zones. We calculated the pixel-wise spatial Pearson Correlation coefficients of TWS and CNDVI. The TWS data and CNDVI data show negative correlation trends in the water-limited areas such as the northern arid-semiarid region and northern China plain, while they show a positive correlation in relatively sufficient water resources in southeast China. According to the results, the changing pattern of grain production in China is likely to cause the depletion of grain production potential in the water-limited regions, while the southeastern regions with higher potential still have more capacity for agricultural production.
28 Mar 2018
TL;DR: This paper proposes to use the linked users across social networking sites and e-commerce websites as a bridge to map users’ social networking features to another feature representation for product recommendation, and develops a feature-based matrix factorization approach which can leverage the learnt user embeddings for cold-start product recommendation.
Abstract: In recent years, the boundaries between ecommerce and social networking have become increasingly blurred. Many e-commerce websites support the mechanism of social login where users can sign on the websites using their social network identities such as their Facebook or Twitter accounts. Users can also post their newly purchased products on microblogs with links to the e-commerce product web pages. In this paper we propose a novel solution for cross-site coldstart product recommendation which aims to recommend products from e-commerce websites to users at social networking sites in “cold-start” situations, a problem which has rarely been explored before. A major challenge is how to leverage knowledge extracted from social networking sites for cross-site cold-start product We propose to use the linked users across social networking sites and e-commerce websites (users who have social networking accounts and have made purchases on e-commerce websites) as a bridge to map users’ social networking features to another feature representation for product recommendation. In specific, we propose learning both users’ and products’ feature representations (called user embeddings and product embeddings, respectively) from data collected from ecommerce websites using recurrent neural networks and then apply a modified gradient boosting trees method to transform users’ social networking features into user embeddings. We then develop a feature-based matrix factorization approach which can leverage the learnt user embeddings for cold-start product recommendation. Experimental results on a large dataset constructed from the largest Chinese microblogging service SINA WEIBO and the largest Chinese B2C e-commerce website JINGDONG have shown the effectiveness of our proposed framework.
TL;DR: It is demonstrated how big data analytics meets SNA, and a comprehensive review is provided on big data analytic approaches in social networks to search published studies between 2013 and August 2020, with 74 identified papers.
Abstract: Social Networking Services (SNSs) connect people worldwide, where they communicate through sharing contents, photos, videos, posting their first-hand opinions, comments, and following their friends. Social networks are characterized by velocity, volume, value, variety, and veracity, the 5 V’s of big data. Hence, big data analytic techniques and frameworks are commonly exploited in Social Network Analysis (SNA). By the ever-increasing growth of social networks, the analysis of social data, to describe and find communication patterns among users and understand their behaviors, has attracted much attention. In this paper, we demonstrate how big data analytics meets social media, and a comprehensive review is provided on big data analytic approaches in social networks to search published studies between 2013 and August 2020, with 74 identified papers. The findings of this paper are presented in terms of main journals/conferences, yearly distributions, and the distribution of studies among publishers. Furthermore, the big data analytic approaches are classified into two main categories: Content-oriented approaches and network-oriented approaches. The main ideas, evaluation parameters, tools, evaluation methods, advantages, and disadvantages are also discussed in detail. Finally, the open challenges and future directions that are worth further investigating are discussed.
TL;DR: A taxonomy of smart city features, dimensions, actions and goals, and, according to these variables, the existing literature on recommender systems is surveyed, to show current opportunities and challenges where personalized recommendations could be exploited as solutions for citizens, firms and public administrations.
Abstract: Among other conceptualizations, smart cities have been defined as functional urban areas articulated by the use of Information and Communication Technologies (ICT) and modern infrastructures to face city problems in efficient and sustainable ways. Within ICT, recommender systems are strong tools that filter relevant information, upgrading the relations between stakeholders in the polity and civil society, and assisting in decision making tasks through technological platforms. There are scientific articles covering recommendation approaches in smart city applications, and there are recommendation solutions implemented in real world smart city initiatives. However, to the best of our knowledge, there is not a comprehensive review of the state of the art on recommender systems for smart cities. For this reason, in this paper we present a taxonomy of smart city features, dimensions, actions and goals, and, according to these variables, we survey the existing literature on recommender systems. As a result of our survey, we do not only identify and analyze main research trends, but also show current opportunities and challenges where personalized recommendations could be exploited as solutions for citizens, firms and public administrations.
TL;DR: It was found that strategies for cut-based segmentation, color-based indexing, k-means based dimensionality reduction and data clustering have been the most frequent choices in recent papers.
Abstract: Content-based video retrieval and indexing have been associated with intelligent methods in many applications such as education, medicine and agriculture. However, an extensive and replicable review of the recent literature is missing. Moreover, relevant topics that can support video retrieval, such as dimensionality reduction, have not been surveyed. This work designs and conducts a systematic review to find papers able to answer the following research question: “what segmentation, feature extraction, dimensionality reduction and machine learning approaches have been applied for content-based video indexing and retrieval?”. By applying a research protocol proposed by us, 153 papers published from 2011 to 2018 were selected. As a result, it was found that strategies for cut-based segmentation, color-based indexing, k-means based dimensionality reduction and data clustering have been the most frequent choices in recent papers. All the information extracted from these papers can be found in a publicly available spreadsheet. This work also indicates additional findings and future research directions.
TL;DR: This paper has provided thought for the application research of intelligent logistics system based on blockchain, and has positive reference value and guiding significance to the development of blockchain application research.
Abstract: Aiming at the current problems of security threats and privacy leak risks in the operation process of related data of intelligent logistics system, and the operation of system lacking of supervision and traceability, this paper proposes to use blockchain technology to resolve these problems. A scheme on applying blockchain in intelligent logistics system is proposed, including operation principle, consensus authentication mechanism, and data storage and access mechanism. By introducing and analyzing the related big data of intelligent logistics system, improving the scientific, rationality, and intelligence of the decision. The basic characteristic of blockchain is traceability. By constructing algorithm models, proposing the realization principles of consensus authentication mechanism. For different events, different “multi-authentication centers”, intelligent contracts, and blockchain systems are constructed, improving the efficiency and supervision of the operation of intelligent logistics system. By constructing the correlations between the fundamental data that corresponding to different blockchain, and the correlations between the fundamental data that corresponding to the same blockchain, making the related data easier to collect and analyze. By constructing the storage and access mechanism, ensuring the security and confidentiality of the operation data of intelligent logistics system. This paper has provided thought for the application research of intelligent logistics system based on blockchain, and has positive reference value and guiding significance to the development of blockchain application research.