Qing Li

City University of Hong Kong, Chiu-lung, Kowloon City, Hong Kong

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Publications (271)108.06 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: Online comment has become a popular and efficient way for sellers to acquire feedback from customers and improve their service quality. However, some key issues need to be solved about evaluating and improving the hotel service quality based on online comments automatically, such as how to use the less trustworthy online comments, how to discover the quality defects from online comments, and how to recommend more feasible or economical evaluation indexes to improve the service quality based on online comments. To solve the above problems, this paper first improves fuzzy comprehensive evaluation (FCE) by importing trustworthy degree to it and proposes an automatic hotel service quality assessment method using the improved FCE, which can automatically get more trustworthy evaluation from a large amount of less trustworthy online comments. Then, the causal relations among evaluation indexes are mined from online comments to build the fuzzy cognitive map for the hotel service quality, which is useful to unfold the problematic areas of hotel service quality, and recommend more economical solutions to improving the service quality. Finally, both case studies and experiments are conducted to demonstrate that the proposed methods are effective in evaluating and improving the hotel service quality using online comments.
    IEEE Transactions on Fuzzy Systems 02/2015; 23(1):72-84. DOI:10.1109/TFUZZ.2015.2390226 · 6.31 Impact Factor
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    ABSTRACT: In this paper, for the first time, we identify and solve the problem of efficient reverse k-skyband (RkSB) query processing. Given a set P of multi-dimensional points and a query point q, an RkSB query returns all the points in P whose dynamic k-skyband contains q. We formalize RkSB retrieval, and then propose five algorithms for computing the RkSB of an arbitrary query point efficiently. Our methods utilize a conventional data-partitioning index (e.g., R-tree) on the dataset, and employ pre-computation, reuse and pruning techniques to boost the query efficiency. In addition, we extend our solutions to tackle an interesting variant of reverse skyline queries, namely, ranked reverse skyline (RRS) query where, given a data set P, a parameter K, and a preference function f, the goal is to find the K reverse skyline points that have the minimal score according to the user-specified function f. Extensive experiments using both real and synthetic data sets demonstrate the effectiveness of our proposed pruning heuristics and the performance of our proposed algorithms under a variety of experimental settings.
    Information Sciences 02/2015; 293:11–34. DOI:10.1016/j.ins.2014.08.052 · 3.89 Impact Factor
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    ABSTRACT: In this paper, we define a novel type of skyline query, namely top-k combinatorial metric skyline (kCMS) query. The kCMS query aims to find k combinations of data points according to a monotonic preference function such that each combination has the query object in its metric skyline. The kCMS query will enable a new set of location-based applications that the traditional skyline queries cannot offer. To answer the kCMS query, we propose two efficient query algorithms, which leverage a suite of techniques including the sorting and threshold mechanisms, reusing technique, and heuristics pruning to incrementally and quickly generate combinations of possible query results. We have conducted extensive experimental studies, and the results demonstrate both effectiveness and efficiency of our proposed algorithms.
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    ABSTRACT: The skyline operator has been extensively explored in the literature, and most of the existing approaches assume that all dimensions are available for all data items. However, many practical applications such as sensor networks, decision making, and location-based services, may involve incomplete data items, i.e., some dimensional values are missing, due to the device failure or the privacy preservation. This paper is the first, to our knowledge, study of k-skyband (kSB) query processing on incomplete data, where multi-dimensional data items are missing some values of their dimensions. We formalize the problem, and then present two efficient algorithms for processing it. Our methods introduce some novel concepts including expired skyline, shadow skyline, and thickness warehouse, in order to boost the search performance. As a second step, we extend our techniques to tackle constrained skyline (CS) and group-by skyline (GBS) queries over incomplete data. Extensive experiments with both real and synthetic data sets demonstrate the effectiveness and efficiency of our proposed algorithms under various experimental settings.
    Expert Systems with Applications 08/2014; 41(10):4959–4974. DOI:10.1016/j.eswa.2014.02.033 · 1.97 Impact Factor
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    ABSTRACT: Rare category discovery aims at identifying unlabeled data examples of rare categories in a given data set. The existing approaches to rare category discovery often need a certain number of labeled data examples as the training set, which are usually difficult and expensive to acquire in practice. To save the cost however, if these methods only use a small training set, their accuracy may not be satisfactory for real applications. In this paper, for the first time, we propose the concept of rare category exploration, aiming to discover all data examples of a rare category from a seed (which is a labeled data example of this rare category) instead of from a training set. To this end, we present an approach known as the FRANK algorithm which transforms rare category exploration to local community detection from a seed in a kNN (k-nearest neighbors) graph with an automatically selected k value. Extensive experimental results on real data sets verify the effectiveness and efficiency of our FRANK algorithm.
    Expert Systems with Applications 07/2014; 41(9):4197–4210. DOI:10.1016/j.eswa.2013.12.039 · 1.97 Impact Factor
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    ABSTRACT: With the increase in resource-sharing websites such as YouTube and Flickr, many shared resources have arisen on the Web. Personalized searches have become more important and challenging since users demand higher retrieval quality. To achieve this goal, personalized searches need to take users' personalized profiles and information needs into consideration. Collaborative tagging (also known as folksonomy) systems allow users to annotate resources with their own tags, which provides a simple but powerful way for organizing, retrieving and sharing different types of social resources. In this article, we examine the limitations of previous tag-based personalized searches. To handle these limitations, we propose a new method to model user profiles and resource profiles in collaborative tagging systems. We use a normalized term frequency to indicate the preference degree of a user on a tag. A novel search method using such profiles of users and resources is proposed to facilitate the desired personalization in resource searches. In our framework, instead of the keyword matching or similarity measurement used in previous works, the relevance measurement between a resource and a user query (termed the query relevance) is treated as a fuzzy satisfaction problem of a user's query requirements. We implement a prototype system called the Folksonomy-based Multimedia Retrieval System (FMRS). Experiments using the FMRS data set and the MovieLens data set show that our proposed method outperforms baseline methods.
    Neural networks: the official journal of the International Neural Network Society 06/2014; 58. DOI:10.1016/j.neunet.2014.05.017 · 1.88 Impact Factor
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    ABSTRACT: One of the most challenging problems in aspect-based opinion mining is aspect extraction, which aims to identify expressions that describe aspects of products (called aspect expressions) and categorize domain-specific synonymous expressions. Although a number of methods of aspect extraction have been proposed before, very few of them are designed to improve the interpretability of generated aspects. Existing methods either generate multiple fine-grained aspects without proper categorization or categorize semantically unrelated product aspects (e.g., by unsupervised topic modeling). In this paper, we first examine previous studies on product aspect extraction. To overcome the limitations of existing methods, two novel semi-supervised models for product aspect extraction are then proposed. More specifically, the proposed methodology first extracts seeding aspects and related terms from detailed product descriptions readily available on E-commerce websites. Next, product reviews are regrouped according to these seeding aspects so that more effective textual contexts for topic modeling are built. Finally, two novel semi-supervised topic models are developed to extract human-comprehensible product aspects. For the first proposed topic model, the Fine-grained Labeled LDA (FL-LDA), seeding aspects are applied to guide the model to discover words that are related to these seeding aspects. For the second model, the Unified Fine-grained Labeled LDA (UFL-LDA), we incorporate unlabeled documents to extend the FL-LDA model so that words related to the seeding aspects or other high-frequency words in customer reviews are extracted. Our experimental results demonstrate that the proposed methods outperform state-of-the-art methods.
    Knowledge-Based Systems 06/2014; DOI:10.1016/j.knosys.2014.05.018 · 3.06 Impact Factor
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    ABSTRACT: This paper presents an efficient and robust content-based large medical image retrieval method in mobile Cloud computing environment, called the Mirc. The whole query process of the Mirc is composed of three steps. First, when a clinical user submits a query image Iq, a parallel image set reduction process is conducted at a master node. Then the candidate images are transferred to the slave nodes for a refinement process to obtain the answer set. The answer set is finally transferred to the query node. The proposed method including an priority-based robust image block transmission scheme is specifically designed for solving the instability and the heterogeneity of the mobile cloud environment, and an index-support image set reduction algorithm is introduced for reducing the data transfer cost involved. We also propose a content-aware and bandwidth-conscious multi-resolution-based image data replica selection method and a correlated data caching algorithm to further improve the query performance. The experimental results show that the performance of our approach is both efficient and effective, minimizing the response time by decreasing the network transfer cost while increasing the parallelism of I/O and CPU.
    Information Sciences 04/2014; 263:60–86. DOI:10.1016/j.ins.2013.10.013 · 3.89 Impact Factor
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    ABSTRACT: In this paper, we study a new skyline operator, namely, mutual skyline query (MSQ), which retrieves all the data objects that are contained in the dynamic skyline and meanwhile the reverse skyline of a specified query object q. MSQ has many applications such as marketing analysis, task allocation, and personalized matching. Motivated by this, we first formalize MSQ in both monochromatic and bichromatic cases, and then propose several algorithms for processing MSQ. Our methods utilize a conventional data-partitioning index on the dataset, employ the advantage of reusing technique, and exploit effective pruning heuristics to improve the query processing. Extensive experiments using both real and synthetic datasets demonstrate the effectiveness and efficiency of our proposed algorithms under various experimental settings.
    Expert Systems with Applications 03/2014; 41(4):1885-1900. DOI:10.1016/j.eswa.2013.08.085 · 1.97 Impact Factor
  • ICWL Workshops; 01/2014
  • Automatika 12/2013; 54(4). DOI:10.7305/automatika.54-4.419 · 0.30 Impact Factor
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    ABSTRACT: Distributed coordination is critical for a multi-robot system in collective cleanup task under a dynamic environment. In traditional methods, robots easily drop into premature convergence. In this paper, we propose a swarm-intelligence based algorithm to reduce the expectation time for searching targets and removing. We modify the traditional PSO algorithm with a random factor to tackle premature convergence problem, and it can achieve a significant improvement in multi-robot system. The proposed method has been implemented on self-developed simulator for searching task. The simulation results demonstrate the feasibility, robustness, and scalability of our proposed method than previous methods.
    2013 International Joint Conference on Awareness Science and Technology & Ubi-Media Computing (iCAST-UMEDIA); 11/2013
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    ABSTRACT: The Skyline query and its variants have been extensively explored in the literature. Existing approaches, except one, assume that all dimensions are available for all data items. However, many practical applications such as sensor networks, decision making, and location-based services, may involve incomplete data items, i.e., some dimensional values are missing, due to the device failure or the privacy preservation. In this paper, for the first time, we study the problem of efficient k-Skyband (kSB) query processing on incomplete data, where multi-dimensional data items are missing some values of their dimensions. We formalize the problem, and then present several efficient algorithms for tackling it. Our methods employ some novel concepts/structures (e.g., expired skyline, shadow skyline, thickness warehouse, etc.) to improve the search performance. Extensive experiments with both real and synthetic data sets demonstrate the effectiveness and efficiency of our proposed algorithms.
    DASFAA 2013; 04/2013
  • 04/2013; 7(2):49-64. DOI:10.4018/ijcini.2013040104
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    ABSTRACT: Text representation is one of the most fundamental works in text comprehension, processing, and search. Various works have been proposed to mine the semantics in texts and then to represent them. However, most of them only focus on how to mine semantics from the text itself while the background knowledge, which is very important to text understanding, is not taken into consideration. In this paper, on the basis of human cognitive process, we propose a multi-level text representation model within background knowledge, called TRMBK. It is composed of three levels, which are machine surface code (MSC), machine text base (MTB) and machine situational model (MSM). All of the three are able to be automatically constructed to acquire semantics both inside and outside of the text. Simultaneously, we also propose a method to automatically establish background knowledge and offer supports for the current text comprehension. Finally, experiments and comparisons have been presented to show the better performance of TRMBK.
    Cognitive Informatics & Cognitive Computing (ICCI*CC), 2013 12th IEEE International Conference on; 01/2013
  • International Journal of Distributed Sensor Networks 01/2013; 2013:1-9. DOI:10.1155/2013/560579 · 0.92 Impact Factor
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    ABSTRACT: A location-aware news feed system enables mobile users to share geo-tagged user-generated messages, e.g., a user can receive nearby messages that are the most relevant to her. In this paper, we present MobiFeed that is a framework designed for scheduling news feeds for mobile users. MobiFeed consists of three key functions, location prediction, relevance measure, and news feed scheduler. The location prediction function is designed to predict a mobile user's locations based on an existing path prediction algorithm. The relevance measure function is implemented by combining the vector space model with non-spatial and spatial factors to determine the relevance of a message to a user. The news feed scheduler works with the other two functions to generate news feeds for a mobile user at her current and predicted locations with the best overall quality. To ensure that MobiFeed can scale up to a larger number of messages, we design a heuristic news feed scheduler.
    Proceedings of the 20th International Conference on Advances in Geographic Information Systems; 11/2012
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    ABSTRACT: The k -nearest-neighbor ( k -NN) query is one of the most popular spatial query types for location-based services (LBS). In this paper, we focus on k -NN queries in time-dependent road networks, where the travel time between two locations may vary significantly at different time of the day. In practice, it is costly for a LBS provider to collect real-time traffic data from vehicles or roadside sensors to compute the best route from a user to a spatial object of interest in terms of the travel time. Thus, we design SMashQ, a server-side spatial mashup framework that enables a database server to efficiently evaluate k -NN queries using the route information and travel time accessed from an external Web mapping service, e.g., Microsoft Bing Maps. Due to the expensive cost and limitations of retrieving such external information, we propose three shared execution optimizations for SMashQ, namely, object grouping , direction sharing , and user grouping , to reduce the number of external Web mapping requests and provide highly accurate query answers. We evaluate SMashQ using Microsoft Bing Maps, a real road network, real data sets, and a synthetic data set. Experimental results show that SMashQ is efficient and capable of producing highly accurate query answers.
    Distributed and Parallel Databases 09/2012; 31(2):1-29. DOI:10.1007/s10619-012-7110-6 · 1.00 Impact Factor
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    ABSTRACT: This paper, for the first time, addresses the problem of efficient reverse k-skyband (RkSB) query processing. Given a set P of multi-dimensional points and a query point q, an RkSB query returns all the points in P whose dynamic k-skyband contains q. We formalize the RkSB query, and then propose three algorithms for computing the RkSB of an arbitrary query point efficiently. Our methods utilize a conventional data-partitioning index (e.g., R-tree) on the dataset, as well as employ pre-computation and pruning techniques to improve the query performance. Extensive experiments using both real and synthetic datasets demonstrate the effectiveness of our proposed pruning heuristics and the performance of our proposed algorithms.
    Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I; 04/2012
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    ABSTRACT: Currently, recommender system becomes more and more important and challenging, as users demand higher recommendation quality. Collaborative tagging systems allow users to annotate resources with their own tags which can reflect users' attitude on these resources and some attributes of resources. Based on our observation, we notice that there is co-occurrence effect of features, which may cause the change of user's favor on resources. Current recommendation methods do not take it into consideration. In this paper, we propose an assistant and enhanced method to improve the performance of other methods by combining co-occurrence effect of features in collaborative tagging environment.
    Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications; 04/2012

Publication Stats

1k Citations
108.06 Total Impact Points

Institutions

  • 1999–2015
    • City University of Hong Kong
      • Department of Computer Science
      Chiu-lung, Kowloon City, Hong Kong
  • 2014
    • Lands Department of The Government of the Hong Kong Special Administrative Region
      Hong Kong, Hong Kong
  • 1970–2014
    • The University of Hong Kong
      • • Department of Computer Science
      • • Department of Information Technology & Engineering
      Hong Kong, Hong Kong
  • 2007–2012
    • USTC-CityU Joint Advanced Research Center
      Hong Kong, Hong Kong
  • 2010
    • Shanghai University
      • School of Computer Engineering and Sciences
      Shanghai, Shanghai Shi, China
  • 2009
    • Pennsylvania State University
      • Department of Computer Science and Engineering
      University Park, Maryland, United States
  • 2007–2009
    • Arizona State University
      Phoenix, Arizona, United States
    • Zhejiang Normal University
      Jinhua, Zhejiang Sheng, China
  • 2008
    • Southwestern University of Finance and Economics
      Hua-yang, Sichuan, China
  • 2002
    • The Hong Kong Institute of Education
      Hong Kong, Hong Kong
  • 1998
    • The Hong Kong Polytechnic University
      • Department of Computing
      Hong Kong, Hong Kong
  • 1997
    • University of New South Wales
      • School of Computer Science and Engineering
      Kensington, New South Wales, Australia
  • 1994–1996
    • The Hong Kong University of Science and Technology
      • Department of Computer Science and Engineering
      Chiu-lung, Kowloon City, Hong Kong