Abstracts:Overlapping community detection, dynamic community identification and community evolution analysis are the three important problems for social network analysis. It is a challenging task to simultaneously address all these three problems with one single method, thus most traditional studies focus on only one or two of them. This paper proposes a novel Dynamic Overlapping Community Evolution Tracking (DOCET) method to solve the three problems simultaneously with one single model, i.e. topology potential field. Specifically, the proposed DOCET method first detects the initial overlapping community structure based on node location analysis in the peak-valley structure of the topology potential field; then it incrementally updates the dynamic community structure based on influence scope analysis in the topology potential field; finally it tracks community evolution events based on the variation of core nodes in the topology potential field. Experiment results on both synthetic and real-world networks show that our proposed method achieves remarkable performance over the existing state-of-the-art methods. It can both accurately partition dynamic overlapping social networks and efficiently track all kinds of community evolution events.

Abstracts:With the rapid prevalence of smart mobile devices and the dramatic proliferation of mobile applications (Apps), App recommendation becomes an emergent task that will benefit different stockholders of mobile App ecosystems. However, the extreme sparsity of user-App matrix and many newly emerging Apps create severe challenges, causing CF-based methods to degrade significantly in their recommendation performance. Besides, unlike traditional items, Apps have rights to access users’ personal resources (e.g., location, message and contact) which may lead to security risk or privacy leak. Thus, users’ choosing of Apps are influenced by not only their personal interests but also their privacy preferences. Moreover, user privacy preferences vary with App categories. In light of the above challenges, we propose a mobile sparse additive generative model (Mobi-SAGE) to recommend Apps by considering both user interests and category-aware user privacy preferences in this paper. To overcome the challenges from data sparsity and cold start, Mobi-SAGE exploits both textual and visual content associated with Apps to learn multi-view topics for user interest modeling. We collected a large-scale and real-world dataset from 360 App store - the biggest Android App platform in China, and conducted extensive experiments on it. The experimental results demonstrate that our Mobi-SAGE consistently and significantly outperforms the other existing state-of-the-art methods, which implies the importance of exploiting category-aware user privacy preferences and the multi-modal App content data on personalized App recommendation.

Abstracts:Although construction supply chain management has attracted significant research attention, this field remains somewhat fragmented. This paper examines an integrated production-distribution-construction system consisting of the construction department and material suppliers under a fuzzy random environment with the aim of optimizing the global equilibrium. A novel bi-level multistage programming method with multiple objective optimization is developed to examine the inherent conflicts and complex interactions among decision makers in order to obtain the Stackelberg–Nash equilibrium solution, in which the construction department, as the leader, decides on the material allocations to construction sites, while the material supplier, as the follower, produces and transports the corresponding materials. For dealing with uncertainties, a hybrid crisp approach with an expected value operator is proposed to convert the fuzzy random parameters into definitive parameters. A hybrid algorithm combining an evolved genetic algorithm and particle swarm optimization is developed to solve this novel Stackelberg game model. The results from a practical example demonstrate the practicality and efficiency of the proposed optimization method, highlight the significance of quantitative analysis for the construction supply chain, and provide objective guidelines for its real-world application.

Abstracts:This paper presents an efficient algorithm for detecting changes (drifts) in the utility distributions of patterns, named High Utility Drift Detection in Transactional Data Stream (HUDD-TDS). The algorithm is specifically suitable for quantitative data streams, where each item has a unit profit, and non-binary purchase quantities are allowed. We propose a method that enables the HUDD-TDS algorithm to be used in an online setting to detect drifts. An important property of HUDD-TDS is that it can quickly adapt to changes in streams, while considering older transactions to be less important than new ones. Furthermore, the proposed method applies statistical testing based on Hoeffding bound with Bonferroni correction in order to ensure that only significant changes are reported to the user. This test allows identifying a change (drift) if the difference between current and the previous time window is significant in terms of utility distribution. In this work, we focus on both local and global utility drifts. A local utility drift is a drift in the utility distribution of a single pattern, whereas a global utility drift is a change in the utilities of all high utility itemsets. In order to be able to compute the similarity of different high utility itemsets to detect drifts, we propose a new distance measure function. The results of our experiments on both real world and synthetic datasets show the feasibility and efficiency of the proposed HUDD-TDS algorithm.

Abstracts:Large-scale group decision making (LSGDM) or complex group decision making (GDM) problems are very commonly encountered in actual life, especially in the era of data. At present, double hierarchy hesitant fuzzy linguistic term set is a reasonable linguistic expression when describing some complex linguistic preference information. In this paper, we develop a consensus reaching process for LSGDM with double hierarchy hesitant fuzzy linguistic preference relations. To ensure the implementation of consensus reaching process, we also propose the similarity degree-based clustering method, the double hierarchy information entropy-based weights-determining method and the consensus measures. Finally, we apply our model to deal with a practical problem that is to evaluate Sichuan water resource management and make some comparisons with the existing approaches.

Abstracts:Finding relevant and interesting items according to the preferences of each user has become an important challenge in the era of Big Data. Recommender systems have emerged in response to this problem. Collaborative Filtering (CF) is one of the most successful recommender systems used by several big online shopping companies. However, CF is computationally demanding, especially in Big Data context, where the number of users and items are too big to be effectively processed by traditional approaches.

Abstracts:As computer science and information technology are making broad and deep impacts on our daily lives, more and more papers are being submitted to computer science journals and conferences. To help authors decide where they should submit their manuscripts, we present the Content-based Journals & Conferences Recommender System on computer science, as well as its web service at http://www.keaml.cn/prs/. This system recommends suitable journals or conferences with a priority order based on the abstract of a manuscript. To follow the fast development of computer science and technology, a web crawler is employed to continuously update the training set and the learning model. To achieve interactive online response, we propose an efficient hybrid model based on chi-square feature selection and softmax regression. Our test results show that, the system can achieve an accuracy of 61.37% and suggest the best journals or conferences in about 5  s on average.