Matrix Factorization Techniques for Recommender Systems

本文对国际科学计量学杂志《Ｓｃｉｅｎｔｏｍｅｔｒｉｃｓ》１９７９－１９９１年的研究论文内容、栏目、作者及国别和编委及国别作了计量分析，揭示出科学计量学研究的重点、活动的中心及发展趋势，说明了学科带头人在发展科学计量学这门新兴学科中的作用。

国际刊物《Scientometrics》文献计量研究

Models and Methods in Social Network Analysis presents the most important developments in quantitative models and methods for analyzing social network data that have appeared during the 1990s. Intended as a complement to Wasserman and Faust’s Social Network Analysis: Methods and Applications, it is a collection of original articles by leading methodologists reviewing recent advances in their particular areas of network methods. Reviewed are advances in network measurement, network sampling, the analysis of centrality, positional analysis or blockmodeling, the analysis of diffusion through networks, the analysis of affiliation or “two-mode” networks, the theory of random graphs, dependence graphs, exponential families of random graphs, the analysis of longitudinal network data, graphic techniques for exploring network data, and software for the analysis of social networks.

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Models and methods in social network analysis

All one needs to know about fog computing and related edge computing paradigms: A complete survey

In the last 16 years, more than 200 research articles were published about research-paper recommender systems. We reviewed these articles and present some descriptive statistics in this paper, as well as a discussion about the major advancements and shortcomings and an overview of the most common recommendation concepts and approaches. We found that more than half of the recommendation approaches applied content-based filtering (55 %). Collaborative filtering was applied by only 18 % of the reviewed approaches, and graph-based recommendations by 16 %. Other recommendation concepts included stereotyping, item-centric recommendations, and hybrid recommendations. The content-based filtering approaches mainly utilized papers that the users had authored, tagged, browsed, or downloaded. TF-IDF was the most frequently applied weighting scheme. In addition to simple terms, n-grams, topics, and citations were utilized to model users' information needs. Our review revealed some shortcomings of the current research. First, it remains unclear which recommendation concepts and approaches are the most promising. For instance, researchers reported different results on the performance of content-based and collaborative filtering. Sometimes content-based filtering performed better than collaborative filtering and sometimes it performed worse. We identified three potential reasons for the ambiguity of the results. (A) Several evaluations had limitations. They were based on strongly pruned datasets, few participants in user studies, or did not use appropriate baselines. (B) Some authors provided little information about their algorithms, which makes it difficult to re-implement the approaches. Consequently, researchers use different implementations of the same recommendations approaches, which might lead to variations in the results. (C) We speculated that minor variations in datasets, algorithms, or user populations inevitably lead to strong variations in the performance of the approaches. Hence, finding the most promising approaches is a challenge. As a second limitation, we noted that many authors neglected to take into account factors other than accuracy, for example overall user satisfaction. In addition, most approaches (81 %) neglected the user-modeling process and did not infer information automatically but let users provide keywords, text snippets, or a single paper as input. Information on runtime was provided for 10 % of the approaches. Finally, few research papers had an impact on research-paper recommender systems in practice. We also identified a lack of authority and long-term research interest in the field: 73 % of the authors published no more than one paper on research-paper recommender systems, and there was little cooperation among different co-author groups. We concluded that several actions could improve the research landscape: developing a common evaluation framework, agreement on the information to include in research papers, a stronger focus on non-accuracy aspects and user modeling, a platform for researchers to exchange information, and an open-source framework that bundles the available recommendation approaches.

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Research-paper recommender systems: a literature survey

Cyber foraging is an important method to enable resource-constrained mobile devices to perform applications in different mobile cloud computing environments to improve performance and to save energy consumption. This paper focuses on the decision problem about how to offload computation-intensive applications in mobile ad hoc network-based cloud computing environments. A set of online and batch scheduling heuristics were proposed to offload dynamically arriving independent tasks among mobile nodes. The heuristics were validated in a simulation environment, and their performances with respect to both user-centric and system-centric metrics such as the average makespan, the average waiting time, the average slowdown and the average utilization, were investigated with comprehensive experiments. Experimental results show that it is not appropriate to map tasks only based on the expected bandwidth, execution time or the overall offloading time, On the contrary, the expected completion time must be taken into account. Furthermore, the MCTComm heuristic seems to be the best choice from the standpoint of the tradeoff between the complexity and the performance.

Heuristics to allocate high-performance cloudlets for computation offloading in mobile ad hoc clouds

Social media systems provide ever-growing huge volumes of information for dissemination and communication among communities of users, while recommender systems aim to mitigate information overload by filtering and providing users the most attractive and relevant items from information-sea. This paper aims at providing compound recommendation engine for social media systems, and focuses on exploiting multi-sourced information (e.g. social networks, item contents and user feedbacks) to predict the ratings of users to items and make recommendations. For this, we suppose the users' decisions on adopting item are affected both by their tastes and the favors of trusted friends, and extend Collaborative Topic Regression to jointly incorporates social trust ensemble, topic modeling and probabilistic matrix factorization. We propose corresponding approaches to learning the latent factors both of users and items, as well as additional parameters to be estimated. Empirical experiments on Lastfm and Delicious datasets show that our model is better and more robust than the state-of-the-art methods on making recommendations in term of accuracy. Experiments results also reveal some useful findings to enlighten the development of recommender systems in social media.

Collaborative Topic Regression with social trust ensemble for recommendation in social media systems

The name ambiguity problem presents many challenges for scholar finding, citation analysis and other related research fields. To attack this issue, various disambiguation methods combined with separate disambiguation features have been put forward. In this paper, we offer an unsupervised Dempster---Shafer theory (DST) based hierarchical agglomerative clustering algorithm for author disambiguation tasks. Distinct from existing methods, we exploit the DST in combination with Shannon's entropy to fuse various disambiguation features and come up with a more reliable candidate pair of clusters for amalgamation in each iteration of clustering. Also, some solutions to determine the convergence condition of the clustering process are proposed. Depending on experiments, our method outperforms three unsupervised models, and achieves comparable performances to a supervised model, while does not prescribe any hand-labelled training data.

Unsupervised author disambiguation using Dempster---Shafer theory

Corner points are formed from two or more edges and edges usually define the boundary between two different objects or parts of the same objects. In this novel we discuss the theory of the Harris corner detection and indicate its disadvantage. Then it proposes an improved algorithm of Harris detection algorithm based on the neighboring point eliminating method. It reduces the time of the detection, and makes the corners distributing more homogenous so that avoids too many corners stay together. Experimental results show that the algorithm can detect corner more equality distributing, and can be used in some fact applications such as image registration well. Key words-Corner point; Harris corner; neighboring

An Improved Algorithm for Harris Corner Detection

This paper proposes an improved discrete wavelet framework based image fusion algorithm, after studying the principles and characteristics of the discrete wavelet framework. The improvement is the careful consideration of the high frequency subband image region characteristic. The algorithms can efficiently synthesis the useful information of the each source image retrieved from the multi sensor. The multi focus image fusion experiment and medical image fusion experiment can verify that our proposed algorithm has the effectiveness in the image fusion. On the other side, this paper studies the quality assessment of the image fusion, and summarize and quantitatively analysis the performance of algorithms proposed in the paper.

Yijian Pei

Papers

Heuristics to allocate high-performance cloudlets for computation offloading in mobile ad hoc clouds

Collaborative Topic Regression with social trust ensemble for recommendation in social media systems

Unsupervised author disambiguation using Dempster---Shafer theory

An Improved Algorithm for Harris Corner Detection

The improved wavelet transform based image fusion algorithm and the quality assessment