Search (9 results, page 1 of 1)

0.024899656 = product of:
  0.099598624 = sum of:
    0.099598624 = sum of:
      0.0673494 = weight(_text_:model in 5492) [ClassicSimilarity], result of:
        0.0673494 = score(doc=5492,freq=6.0), product of:
          0.1830527 = queryWeight, product of:
            3.845226 = idf(docFreq=2569, maxDocs=44218)
            0.047605187 = queryNorm
          0.36792353 = fieldWeight in 5492, product of:
            2.4494898 = tf(freq=6.0), with freq of:
              6.0 = termFreq=6.0
            3.845226 = idf(docFreq=2569, maxDocs=44218)
            0.0390625 = fieldNorm(doc=5492)
      0.032249227 = weight(_text_:22 in 5492) [ClassicSimilarity], result of:
        0.032249227 = score(doc=5492,freq=2.0), product of:
          0.16670525 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.047605187 = queryNorm
          0.19345059 = fieldWeight in 5492, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0390625 = fieldNorm(doc=5492)
  0.25 = coord(1/4)

Abstract

Purpose Online knowledge communities make great contributions to global knowledge sharing and innovation. Resource tagging approaches have been widely adopted in such communities to describe, annotate and organize knowledge resources mainly through users' participation. However, it is unclear what causes the adoption of a particular resource tagging approach. The purpose of this paper is to identify factors that drive users to use a hybrid social tagging approach. Design/methodology/approach Technology acceptance model and social cognitive theory are adopted to support an integrated model proposed in this paper. Zhihu, one of the most popular online knowledge communities in China, is taken as the survey context. A survey was conducted with a questionnaire and collected data were analyzed through structural equation model. Findings A new hybrid social resource tagging approach was refined and described. The empirical results revealed that self-efficacy, perceived usefulness (PU) and perceived ease of use exert positive effect on users' attitude. Moreover, social influence, PU and attitude impact significantly on users' intention to use a hybrid social resource tagging approach. Originality/value Theoretically, this study enriches the type of resource tagging approaches and recognizes factors influencing user adoption to use it. Regarding the practical parts, the results provide online information system providers and designers with referential strategies to improve the performance of the current tagging approaches and promote them.

Date

20. 1.2015 18:30:22

0.010868462 = product of:
  0.043473847 = sum of:
    0.043473847 = product of:
      0.086947694 = sum of:
        0.086947694 = weight(_text_:model in 3642) [ClassicSimilarity], result of:
          0.086947694 = score(doc=3642,freq=10.0), product of:
            0.1830527 = queryWeight, product of:
              3.845226 = idf(docFreq=2569, maxDocs=44218)
              0.047605187 = queryNorm
            0.4749872 = fieldWeight in 3642, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              3.845226 = idf(docFreq=2569, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3642)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Abstract

With the rapid development of the Internet and its applications, growing volumes of documents increasingly become interconnected to form large-scale document networks. Accordingly, topic modeling in a network of documents has been attracting continuous research attention. Most of the existing network-based topic models assume that topics in a document are influenced by its directly linked neighbouring documents in a document network and overlook the potential influence from indirectly linked ones. The existing work also has not carefully modeled variations of such influence among neighboring documents. Recognizing these modeling limitations, this paper introduces a novel Local Context-Aware LDA Model (LC-LDA), which is capable of observing a local context comprising a rich collection of documents that may directly or indirectly influence the topic distributions of a target document. The proposed model can also differentiate the respective influence of each document in the local context on the target document according to both structural and temporal relationships between the two documents. The proposed model is extensively evaluated through multiple document clustering and classification tasks conducted over several large-scale document sets. Evaluation results clearly and consistently demonstrate the effectiveness and superiority of the new model with respect to several state-of-the-art peer models.

0.010868462 = product of:
  0.043473847 = sum of:
    0.043473847 = product of:
      0.086947694 = sum of:
        0.086947694 = weight(_text_:model in 98) [ClassicSimilarity], result of:
          0.086947694 = score(doc=98,freq=10.0), product of:
            0.1830527 = queryWeight, product of:
              3.845226 = idf(docFreq=2569, maxDocs=44218)
              0.047605187 = queryNorm
            0.4749872 = fieldWeight in 98, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              3.845226 = idf(docFreq=2569, maxDocs=44218)
              0.0390625 = fieldNorm(doc=98)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Abstract

With the development of Web-based social networks, many personalized tag recommendation approaches based on multi-information have been proposed. Due to the differences in users' preferences, different users care about different kinds of information. In the meantime, different elements within each kind of information are differentially informative for user tagging behaviors. In this context, how to effectively integrate different elements and different information separately becomes a key part of tag recommendation. However, the existing methods ignore this key part. In order to address this problem, we propose a deep neural network for tag recommendation. Specifically, we model two important attentive aspects with a hierarchical attention model. For different user-item pairs, the bottom layered attention network models the influence of different elements on the features representation of the information while the top layered attention network models the attentive scores of different information. To verify the effectiveness of the proposed method, we conduct extensive experiments on two real-world data sets. The results show that using attention network and different kinds of information can significantly improve the performance of the recommendation model, and verify the effectiveness and superiority of our proposed model.

0.008418675 = product of:
  0.0336747 = sum of:
    0.0336747 = product of:
      0.0673494 = sum of:
        0.0673494 = weight(_text_:model in 606) [ClassicSimilarity], result of:
          0.0673494 = score(doc=606,freq=6.0), product of:
            0.1830527 = queryWeight, product of:
              3.845226 = idf(docFreq=2569, maxDocs=44218)
              0.047605187 = queryNorm
            0.36792353 = fieldWeight in 606, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.845226 = idf(docFreq=2569, maxDocs=44218)
              0.0390625 = fieldNorm(doc=606)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Abstract

With more and more information available on the Internet, the task of making personalized recommendations to assist the user's navigation has become increasingly important. Considering there might be millions of users with different backgrounds accessing a Web site everyday, it is infeasible to build a separate recommendation system for each user. To address this problem, clustering techniques can first be employed to discover user groups. Then, user navigation patterns for each group can be discovered, to allow the adaptation of a Web site to the interest of each individual group. In this paper, we propose to model user access sequences as stochastic processes, and a mixture of Markov models based approach is taken to cluster users and to capture the sequential relationships inherent in user access histories. Several important issues that arise in constructing the Markov models are also addressed. The first issue lies in the complexity of the mixture of Markov models. To improve the efficiency of building/maintaining the mixture of Markov models, we develop a lightweight adapt-ive algorithm to update the model parameters without recomputing model parameters from scratch. The second issue concerns the proper selection of training data for building the mixture of Markov models. We investigate two different training data selection strategies and perform extensive experiments to compare their effectiveness on a real dataset that is generated by a Web-based knowledge management system, Livelink.

0.006873818 = product of:
  0.027495272 = sum of:
    0.027495272 = product of:
      0.054990545 = sum of:
        0.054990545 = weight(_text_:model in 234) [ClassicSimilarity], result of:
          0.054990545 = score(doc=234,freq=4.0), product of:
            0.1830527 = queryWeight, product of:
              3.845226 = idf(docFreq=2569, maxDocs=44218)
              0.047605187 = queryNorm
            0.30040827 = fieldWeight in 234, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.845226 = idf(docFreq=2569, maxDocs=44218)
              0.0390625 = fieldNorm(doc=234)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Abstract

The scale of knowledge is growing rapidly in the big data environment, and traditional knowledge organization and services have faced the dilemma of semantic inaccuracy and untimeliness. From a knowledge fusion perspective-combining the precise semantic superiority of traditional ontology with the large-scale graph processing power and the predicate attribute expression ability of property graph-this paper presents an ontology and property graph fusion framework (OPGFF). The fusion process is divided into content layer fusion and constraint layer fusion. The result of the fusion, that is, the knowledge representation model is called knowledge big graph. In addition, this paper applies the knowledge big graph model to the ownership network in the China's financial field and builds a financial ownership knowledge big graph. Furthermore, this paper designs and implements six consistency inference algorithms for finding contradictory data and filling in missing data in the financial ownership knowledge big graph, five of which are completely domain agnostic. The correctness and validity of the algorithms have been experimentally verified with actual data. The fusion OPGFF framework and the implementation method of the knowledge big graph could provide technical reference for big data knowledge organization and services.

0.0064498456 = product of:
  0.025799382 = sum of:
    0.025799382 = product of:
      0.051598765 = sum of:
        0.051598765 = weight(_text_:22 in 7744) [ClassicSimilarity], result of:
          0.051598765 = score(doc=7744,freq=2.0), product of:
            0.16670525 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.047605187 = queryNorm
            0.30952093 = fieldWeight in 7744, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=7744)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

22. 6.1997 16:34:51

0.0048605236 = product of:
  0.019442094 = sum of:
    0.019442094 = product of:
      0.03888419 = sum of:
        0.03888419 = weight(_text_:model in 5382) [ClassicSimilarity], result of:
          0.03888419 = score(doc=5382,freq=2.0), product of:
            0.1830527 = queryWeight, product of:
              3.845226 = idf(docFreq=2569, maxDocs=44218)
              0.047605187 = queryNorm
            0.21242073 = fieldWeight in 5382, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.845226 = idf(docFreq=2569, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5382)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Abstract

Two general challenges faced by data analysis are the existence of noise and the extraction of meaningful information from collected data. In this study, we used a multiscale framework to reduce the effects caused by noise and to extract explainable geometric properties to characterize finite metric spaces. We conducted lab experiments that integrated the use of eye-tracking, electrodermal activity (EDA), and user logs to explore users' information-seeking behaviors on search engine result pages (SERPs). Experimental results of 1,590 search queries showed that the proposed strategies effectively predicted query-level user satisfaction using EDA and eye-tracking data. The bootstrap analysis showed that combining EDA and eye-tracking data with user behavior data extracted from user logs led to a significantly better linear model fit than using user behavior data alone. Furthermore, cross-user and cross-task validations showed that our methods can be generalized to different search engine users performing different preassigned tasks.

0.003888419 = product of:
  0.015553676 = sum of:
    0.015553676 = product of:
      0.031107351 = sum of:
        0.031107351 = weight(_text_:model in 4239) [ClassicSimilarity], result of:
          0.031107351 = score(doc=4239,freq=2.0), product of:
            0.1830527 = queryWeight, product of:
              3.845226 = idf(docFreq=2569, maxDocs=44218)
              0.047605187 = queryNorm
            0.16993658 = fieldWeight in 4239, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.845226 = idf(docFreq=2569, maxDocs=44218)
              0.03125 = fieldNorm(doc=4239)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Abstract

With the advent of various services and applications of Semantic Web, semantic annotation has emerged as an important research topic. The application of semantically annotated ontology had been evident in numerous information processing and retrieval tasks. One of such tasks is utilizing the semantically annotated ontology in product design which is able to suggest many important applications that are critical to aid various design related tasks. However, ontology development in design engineering remains a time consuming and tedious task that demands considerable human efforts. In the context of product family design, management of different product information that features efficient indexing, update, navigation, search and retrieval across product families is both desirable and challenging. For instance, an efficient way of retrieving timely information on product family can be useful for tasks such as product family redesign and new product variant derivation when requirements change. However, the current research and application of information search and navigation in product family is mostly limited to its structural aspect which is insufficient to handle advanced information search especially when the query targets at multiple aspects of a product. This paper attempts to address this problem by proposing an information search and retrieval framework based on the semantically annotated multi-facet product family ontology. Particularly, we propose a document profile (DP) model to suggest semantic tags for annotation purpose. Using a case study of digital camera families, we illustrate how the faceted search and retrieval of product information can be accomplished. We also exemplify how we can derive new product variants based on the designer's query of requirements via the faceted search and retrieval of product family information. Lastly, in order to highlight the value of our current work, we briefly discuss some further research and applications in design decision support, e.g. commonality analysis and variety comparison, based on the semantically annotated multi-facet product family ontology.

0.003888419 = product of:
  0.015553676 = sum of:
    0.015553676 = product of:
      0.031107351 = sum of:
        0.031107351 = weight(_text_:model in 1485) [ClassicSimilarity], result of:
          0.031107351 = score(doc=1485,freq=2.0), product of:
            0.1830527 = queryWeight, product of:
              3.845226 = idf(docFreq=2569, maxDocs=44218)
              0.047605187 = queryNorm
            0.16993658 = fieldWeight in 1485, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.845226 = idf(docFreq=2569, maxDocs=44218)
              0.03125 = fieldNorm(doc=1485)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Abstract

Product family design is one of the prevailing approaches in realizing mass customization. With the increasing number of product offerings targeted at different market segments, the issue of information management in product family design, that is related to an efficient and effective storage, sharing and timely retrieval of design information, has become more complicated and challenging. Product family modelling schema reported in the literature generally stress the component aspects of a product family and its analysis, with a limited capability to model complex inter-relations between physical components and other required information in different semantic orientations, such as manufacturing, material and marketing wise. To tackle this problem, ontology-based representation has been identified as a promising solution to redesign product platforms especially in a semantically rich environment. However, ontology development in design engineering demands a great deal of time commitment and human effort to process complex information. When a large variety of products are available, particularly in the consumer market, a more efficient method for building a product family ontology with the incorporation of multi-faceted semantic information is therefore highly desirable. In this study, we propose a methodology for building a semantically annotated multi-faceted ontology for product family modelling that is able to automatically suggest semantically-related annotations based on the design and manufacturing repository. The six steps of building such ontology: formation of product family taxonomy; extraction of entities; faceted unit generation and concept identification; facet modelling and semantic annotation; formation of a semantically annotated multi-faceted product family ontology (MFPFO); and ontology validation and evaluation are discussed in detail. Using a family of laptop computers as an illustrative example, we demonstrate how our methodology can be deployed step by step to create a semantically annotated MFPFO. Finally, we briefly discuss future research issues as well as interesting applications that can be further pursued based on the MFPFO developed.