An explorative study of shopper-based salient e-servicescape attributes: A Means-End Chain approach     

《International Journal of Information Management》2014,34(4):517-532

This study aims to address the gaps inherent in existing studies by exploring the salient e-servicescape attributes. Employing the Means-End Chain (MEC) approach, empirical evidence was obtained through in-depth interviews with online shoppers using laddering technique to determine the most frequently mentioned attributes from four servicescape dimensions: (1) ambient; (2) design; (3) signs, symbols and artifacts; and (4) interaction. Accordingly, we laddered three levels of online shoppers’ responses from concrete to less concrete abstractions, i.e. attributes, benefits, and end-desirable beliefs. As a result, seven salient e-servicescape attributes were identified. Each of the four dimensions suggests the attributes’ linkage to benefits and end-desirable beliefs. Specifically, the study finds quality photograph, as the salient attribute of the ambient dimension, may set the point of initial attraction and move shoppers from understanding the product to linking the web site contents. In the design dimension, navigation bar, categorization, and simple arrangement are the salient attributes. Company logo represents the most salient attribute under the signs, symbols and artifacts dimension because it not only facilitates recognition and recall of prominent web sites, it also acts as the determinant of perceived risks. In the interaction dimension, although pricing information is the salient attribute that evokes happiness and confidence among online shoppers, it may dilute their perception on web site's success. Instead, confirmation mail can possibly act as a determinant to web site's success. By offering a finer granularity of information, our findings provide insights to online sellers on the salient attributes to consider in order to effectively promote their shopping web sites to create positive emotional response and buying behavior among the online shoppers.  相似文献   

基于CiteSpace的语义网国外研究热点与前沿分析     

周晓剑 《情报探索》2014,(9):24-27

对ISI Web of Knowledge数据库收录的语义网研究论文进行定量及定性分析,利用知识图谱软件CiteSpace绘制出语义网研究的关键论文、关键词以及突变词的知识图谱,分析语义网研究在各国家/地区以及学科领域的分布情况,总结该领域的研究热点,展望未来研究趋势。  相似文献   

转化学习理论视域下虚拟学习共同体中深度学习发生机制设计     

马芳芳  杨静 《人天科学研究》2014,(1):101-103

深度学习指要求学习者能够批判性地进行学习,反思自身原有的认知结构并在此基础上建构新知识的一种学习方式,它有利于培养学习者的高级思维能力。在终身学习的大背景下,学习者要不断提升自己的学习层次,适应新情况,探索新问题,拥有较强的学习能力,因而深度学习的教学价值潜力不言而喻。在转化学习理论的指导下,借助学习共同体环境,对深度学习的发生机制进行了设计,期望引导学习者在解决问题的过程中提升学习能力。  相似文献   

电子政务网络影响力评估与提升策略研究——以广东省21个市政府为例     

李曼  陈建松 《现代情报》2014,34(4):90-93

文章利用层次分析法和链接分析法确定了电子政务网络影响力的评估体系,提出如何合理利用商业搜索引擎搜集数据,并对广东省21个市级政府的网络影响力进行了测评,结合评估结果提出提升电子政务网络影响力的策略方法。  相似文献   

图书馆大数据管理与分析     

王红 《现代情报》2014,34(8):78-82

图书馆是一个重要的大数据应用领域,文章分析了图书馆大数据生成机制、图书馆大数据的类型、图书馆大数据管理手段和分析方法等等,指出面对大量的非结构化数据和迅速增长的服务记录数据,图书馆面临的困境和未来的研究应用方向。  相似文献   

基于Web挖掘的突发事件网络舆情预警研究     

董坚峰 《现代情报》2014,34(2):43-47,51

当前网络突发事件频发,网络舆情与突发事件的相互作用增加了舆情分析和预警的难度,现有舆情预警系统无法满足需求。将Web挖掘技术引入到突发事件网络舆情预警中,构建了包括舆情采集层、舆情挖掘层、舆情分析层、预警研判层的基于Web挖掘的突发事件网络舆情预警系统模型,集成和整合了突发事件网络舆情预警全过程的重要功能,实现突发事件网络舆情采集、分析处理、危机预警的自动化、智能化和实时化。  相似文献   

Key phrase aware transformer for abstractive summarization     

《Information processing & management》2022,59(3):102913

Abstractive summarization aims to generate a concise summary covering salient content from single or multiple text documents. Many recent abstractive summarization methods are built on the transformer model to capture long-range dependencies in the input text and achieve parallelization. In the transformer encoder, calculating attention weights is a crucial step for encoding input documents. Input documents usually contain some key phrases conveying salient information, and it is important to encode these phrases completely. However, existing transformer-based summarization works did not consider key phrases in input when determining attention weights. Consequently, some of the tokens within key phrases only receive small attention weights, which is not conducive to encoding the semantic information of input documents. In this paper, we introduce some prior knowledge of key phrases into the transformer-based summarization model and guide the model to encode key phrases. For the contextual representation of each token in the key phrase, we assume the tokens within the same key phrase make larger contributions compared with other tokens in the input sequence. Based on this assumption, we propose the Key Phrase Aware Transformer (KPAT), a model with the highlighting mechanism in the encoder to assign greater attention weights for tokens within key phrases. Specifically, we first extract key phrases from the input document and score the phrases’ importance. Then we build the block diagonal highlighting matrix to indicate these phrases’ importance scores and positions. To combine self-attention weights with key phrases’ importance scores, we design two structures of highlighting attention for each head and the multi-head highlighting attention. Experimental results on two datasets (Multi-News and PubMed) from different summarization tasks and domains show that our KPAT model significantly outperforms advanced summarization baselines. We conduct more experiments to analyze the impact of each part of our model on the summarization performance and verify the effectiveness of our proposed highlighting mechanism.  相似文献   

Understanding information diffusion with psychological field dynamic     

《Information processing & management》2022,59(4):102956

Several approaches focus on how to automatically capture the latent features from original diffusion data and predict the future scale of cascades utilizing a black box framework. However, they ignore the penetrating insight into the underlying mechanism that how each participant is involved in the cascade. In this work, we bridge the gap between prediction and understanding of information diffusion by incorporating deep learning techniques and social psychology. To characterize individual participation driven by both subjective and objective impetus and integrate it into the macro-level cascade, we propose an end-to-end model, named PFDID, which is designed based on the field dynamics theory of psychology, including the intrinsic cognition field and the extrinsic environment field. We represent these two field dynamics respectively with the pairwise semantic relation between the message itself and corresponding comment and the forwarder’s micro-community activity embedding to provide educated explanations for forwarding behaviour. Afterwards, the cross infusion mechanism is designed to calculate the mutual influence of inhomogeneous field dynamics inside users and cross influence of homogeneous field dynamics among individuals, whose output is fed into the diffusion network aggregation layer for the cascade size prediction. Extensive experiments on two typical social networks, Sina Weibo and Twitter, manifest that the proposed PFDID outperforms state-of-the-art approaches. Our model achieves excellent prediction results, with MSLE = 1.856 on Sina Weibo and MSLE = 1.962 on Twitter, providing 6.54% and 10.53% relative performance gains, respectively. Furthermore, the interpretability is also discussed based on detailed visualization. We observe that the psychological impetus behind social behaviour varies mainly following two patterns with the spread of information, including gradual change and joint influence. Additionally, the indirect dependencies have also been verified.  相似文献   

Ensemble unsupervised autoencoders and Gaussian mixture model for cyberattack detection     

《Information processing & management》2022,59(2):102844

Previous studies have adopted unsupervised machine learning with dimension reduction functions for cyberattack detection, which are limited to performing robust anomaly detection with high-dimensional and sparse data. Most of them usually assume homogeneous parameters with a specific Gaussian distribution for each domain, ignoring the robust testing of data skewness. This paper proposes to use unsupervised ensemble autoencoders connected to the Gaussian mixture model (GMM) to adapt to multiple domains regardless of the skewness of each domain. In the hidden space of the ensemble autoencoder, the attention-based latent representation and reconstructed features of the minimum error are utilized. The expectation maximization (EM) algorithm is used to estimate the sample density in the GMM. When the estimated sample density exceeds the learning threshold obtained in the training phase, the sample is identified as an outlier related to an attack anomaly. Finally, the ensemble autoencoder and the GMM are jointly optimized, which transforms the optimization of objective function into a Lagrangian dual problem. Experiments conducted on three public data sets validate that the performance of the proposed model is significantly competitive with the selected anomaly detection baselines.  相似文献   

Demand forecasting model using hotel clustering findings for hospitality industry     

《Information processing & management》2022,59(1):102816

Tourism has become a growing industry day by day with the developing economic conditions and the increasing communication and social interaction ability of the people. Forecasting tourism demand is not only important for tourism operators to maximize their revenues but also important for the formation of economic plans of the countries on a global scale. Based on the predictions countries are able to regulate the sectors that benefit economically from tourism locally. Therefore, it is crucial to accurately predict the demand in many weeks advance. In this study, we propose a new demand forecasting model for the hospitality industry that forecasts weekly hotel demand four weeks in advance through Attention-Long Short Term Memory (Attention-LSTM). Unlike most of the existing methods, the proposed method utilizes the time series demand data together with additional features obtained from K-Means Clustering findings such as Top 10 Hotel Features or Hotel Embeddings obtained using Neural Networks (NN). While creating our model, the clustering part was influenced by the fact that travelers choose their accommodation according to certain criteria, and the hotels meeting similar criteria may have similar demands. Therefore, before the clustering part, we also applied methods that would enable us to represent the features of the hotels more properly and we observed that 10-D Embedded Hotel Data representation with NN Embeddings came to the fore. In order to observe the performance of the proposed hotel demand forecasting model we used a real-world dataset provided by a tourism agency in Turkey and the results show that the proposed model achieves less mean absolute error and mean absolute percentage error (at worst % 3 and at most % 29 improvements) compared to the currently used machine learning and deep learning models.  相似文献