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  • × author_ss:"Liu, D.-R."
  • × language_ss:"e"
  • × year_i:[2010 TO 2020}
  1. Liu, D.-R.; Lai, C.-H.; Chen, Y.-T.: Document recommendations based on knowledge flows : a hybrid of personalized and group-based approaches (2012) 0.00 
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    Abstract
    Recommender systems can mitigate the information overload problem and help workers retrieve knowledge based on their preferences. In a knowledge-intensive environment, knowledge workers need to access task-related codified knowledge (documents) to perform tasks. A worker's document referencing behavior can be modeled as a knowledge flow (KF) to represent the evolution of his or her information needs over time. Document recommendation methods can proactively support knowledge workers in the performance of tasks by recommending appropriate documents to meet their information needs. However, most traditional recommendation methods do not consider workers' KFs or the information needs of the majority of a group of workers with similar KFs. A group's needs may partially reflect the needs of an individual worker that cannot be inferred from his or her past referencing behavior. In other words, the group's knowledge complements that of the individual worker. Thus, we leverage the group perspective to complement the personal perspective by using hybrid approaches, which combine the KF-based group recommendation method (KFGR) with traditional personalized-recommendation methods. The proposed hybrid methods achieve a trade-off between the group-based and personalized methods by exploiting the strengths of both. The results of our experiment show that the proposed methods can enhance the quality of recommendations made by traditional methods.
  2. Liu, D.-R.; Chen, Y.-H.; Shen, M.; Lu, P.-J.: Complementary QA network analysis for QA retrieval in social question-answering websites (2015) 0.00 
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    Abstract
    With the ubiquity of the Internet and the rapid development of Web 2.0 technology, social question and answering (SQA) websites have become popular knowledge-sharing platforms. As the number of posted questions and answers (QAs) continues to increase rapidly, the massive amount of question-answer knowledge is causing information overload. The problem is compounded by the growing number of redundant QAs. SQA websites such as Yahoo! Answers are open platforms where users can freely ask or answer questions. Users also may wish to learn more about the information provided in an answer so they can use related keywords in the answer to search for extended, complementary information. In this article, we propose a novel approach to identify complementary QAs (CQAs) of a target QA. We define two types of complementarity: partial complementarity and extended complementarity. First, we utilize a classification-based approach to predict complementary relationships between QAs based on three measures: question similarity, answer novelty, and answer correlation. Then we construct a CQA network based on the derived complementary relationships. In addition, we introduce a CQA network analysis technique that searches the QA network to find direct and indirect CQAs of the target QA. The results of experiments conducted on the data collected from Yahoo! Answers Taiwan show that the proposed approach can more effectively identify CQAs than can the conventional similarity-based method. Case and user study results also validate the helpfulness and the effectiveness of our approach.
  3. Liu, D.-R.; Shih, M.-J.: Hybrid-patent classification based on patent-network analysis (2011) 0.00 
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    Date
    22. 1.2011 13:04:21

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