This paper proposes mining query subtopics from questions in community question answering (CQA). The subtopics are represented as a number of clusters of questions with keywords summarizing the clusters. The task is unique in that the subtopics from questions can not only facilitate user browsing in CQA search, but also describe aspects of queries from a question-answering perspective. The challenges of the task include how to group semantically similar questions and how to find keywords capable of summarizing the clusters. We formulate the subtopic mining task as a non-negative matrix factorization (NMF) problem and further extend the model of NMF to incorporate question similarity estimated from metadata of CQA into learning. Compared with existing methods, our method can jointly optimize question clustering and keyword extraction and encourage the former task to enhance the latter. Experimental results on large scale real world CQA datasets show that the proposed method significantly outperforms the existing methods in terms of keyword extraction, while achieving a comparable performance to the state-of-the-art methods for question clustering.

Multi-document summarization is of great value to many real world applications since it can help people get the main ideas within a short time.In this paper, we tackle the problem of extracting summary sentences from multi-document sets by applying sparse coding techniques and present a novel framework to this challenging problem. Based on the data reconstruction and sentence denoising assumption, we present a two-level sparse representation model to depict the process of multi-document summarization. Three requisite properties is proposed to form an ideal reconstructable summary: Coverage, Sparsity and Diversity. We then formalize the task of multi-document summarization as an optimization problem according to the above properties, and use simulated annealing algorithm to solve it.Extensive experiments on summarization benchmark data sets DUC2006 and DUC2007 show that our proposed model is effective and outperforms the state-of-the-art algorithms.

Hippocampal place cells and entorhinal grid cells have been hypothesized to be able to form map-like spatial representation of the environment, namely cognitive map. In most prior approaches, either neural network methods or only hippocampal models are used for building cognitive maps, lacking biological fidelity to the entorhinal-hippocampal system. This paper presents a novel computational model to build cognitive maps of real environments using both place cells and grid cells. The proposed model includes two major components: (1) A competitive Hebbian learning algorithm is used to select velocity-coupled grid cell population activities, which path-integrate self-motion signals to determine computation of place cell population activities; (2) Visual cues of environments are used to correct the accumulative errors intrinsically associated with the path integration process. Experiments performed on a mobile robot show that cognitive maps of the real environment can be efficiently built. The proposed model would provide an alternative neuro-inspired approach for robotic mapping, navigation and localization.

Commenting is a popular facility provided by news sites. Analyzing such user-generated content has recently attracted research interest. However, in multilingual societies such as India, analyzing such user-generated content is hard due to several reasons: (1) There are more than 20 official languages but linguistic resources are available mainly for Hindi. It is observed that people frequently use romanized text as it is easy and quick using an English keyboard, resulting in multi-glyphic comments, where the texts are in the same language but in different scripts. Such romanized texts are almost unexplored in machine learning so far. (2) In many cases, comments are made on a specific part of the article rather than the topic of the entire article. Off-the-shelf methods such as correspondence LDA are insufficient to model such relationships between articles and comments. In this paper, we extend the notion of correspondence to model multi-lingual, multi-script, and inter-lingual topics in a unified probabilistic model called the Multi-glyphic Correspondence Topic Model (MCTM). Using several metrics, we verify our approach and show that it improves over the state-of-the-art.

Collaborative filtering suffers from the problems of data sparsity and cold start, which dramatically degrade recommendation performance. To help resolve these issues, we propose TrustSVD, a trust-based matrix factorization technique. By analyzing the social trust data from four real-world data sets, we conclude that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. Hence, we build on top of a state-of-the-art recommendation algorithm SVD++ which inherently involves the explicit and implicit influence of rated items, by further incorporating both the explicit and implicit influence of trusted users on the prediction of items for an active user. To our knowledge, the work reported is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that our approach TrustSVD achieves better accuracy than other ten counterparts, and can better handle the concerned issues.

We present a data mining approach for reducing the search space of local search algorithms in large-scale set partitioning problems (SPPs). We construct a k-nearest neighbor graph by extracting variable associations from the instance to be solved, in order to identify promising pairs of flipping variables in the large neighborhood search. We incorporate the search space reduction technique into a 2-flip neighborhood local search algorithm with an efficient incremental evaluation of solutions and an adaptive control of penalty weights. We also develop a 4-flip neighborhood local search algorithm that flips four variables alternately along 4-paths or 4-cycles in the k-nearest neighbor graph. According to computational comparison with the latest solvers, our algorithm performs effectively for large-scale SPP instances with up to 2.57 million variables.

Intelligent item recommendation is a key issue in AI research which enables recommender systems to be more “human-minded” when generating recommendations. However, one of the major features of human — forgetting, has barely been discussed as regards recommender systems. In this paper, we considered people’s forgetting of interest when performing personalized recommendations, and brought forward a personalized framework to integrate interest-forgetting property with Markov model. Multiple implementations of the framework were investigated and compared. The experimental evaluation showed that our methods could significantly improve the accuracy of item recommendation, which verified the importance of considering interest-forgetting in recommendations.

Model-based approaches provide a semantically well justified way to revise ontologies. However, in general, model-based revision operators are limited due to lack of efficient algorithms and inexpressibility of the revision results. In this paper, we make both theoretical and practical contribution to efficient computation of model-based revisions in DL-Lite. Specifically, we show that maximal approximations of two well-known model-based revisions for DL-Lite_R can be computed using a syntactic algorithm. However, such a coincidence of model-based and syntactic approaches does not hold when role functionality axioms are allowed. As a result, we identify conditions that guarantee such a coincidence for DL-Lite_FR. Our result shows that both model-based and syntactic revisions can co-exist seamlessly and the advantages of both approaches can be taken in one revision operator. Based on our theoretical results, we develop a graph-based algorithm for the revision operat

A* search is a fundamental topic in artificial intelligence. Recently, the general purpose computation on graphics processing units (GPGPU) has been widely used to accelerate numerous computational tasks. In this paper, we propose the first parallel variant of the A* search algorithm such that the search process of an agent can be accelerated by a single GPU processor in a massively parallel fashion. Our experiments have demonstrated that the GPU-accelerated A* search is efficient in solving multiple real-world search tasks, including combinatorial optimization problems, pathfinding and game solving. Compared to the traditional sequential CPU-based A* implementation, our GPU-based A* algorithm can achieve a significant speedup by up to 45x on large-scale search problems.

The frequency and intensity of natural disasters has significantly increased over the past decades and this trend is predicted to continue. Facing these possible and unexpected disasters, understanding and simulating of human emergency mobility following disasters will becomethe critical issue for planning effective humanitarian relief, disaster management, and long-term societal reconstruction. However, due to the uniquenessof various disasters and the unavailability of reliable and large scale human mobility data, such kind of research is very difficult to be performed. Hence, in this paper,we collect big and heterogeneous data (e.g. 1.6 million users' GPS records in three years, 17520 times of Japan earthquake data in four years, news reporting data, transportation network data and etc.) to capture and analyze human emergency mobility following different disasters. By mining these big data, we aim to understand what basic laws govern human mobility following disasters, and develop a general model of human emergency mobility for generating and simulating large amount of human emergency movements. The experimental results and validations demonstrate the efficiency of our simulation model, and suggest that human mobility following disasters may be significantly morepredictable and can be easier simulated than previously thought.