用户新闻信息浏览行为和感知价值研究

随着互联网技术的发展,用户信息行为的研究越来越受到重视,浏览行为是用户信息行为的重要组成部分,浏览行为的研究对提升用户体验具有重要意义.以今日头条用户为研究对象,基于顾客感知价值理论,使用访谈法与问卷调查法相结合的方法,探索影响用户浏览新闻的行为的因素.研究发现,用户在移动端浏览新闻场景下,只有实用价值对新闻浏览行为正...     

MONERS: A news recommender for the mobile web

Mobile web news services, which served by mobile service operators collecting news articles from diverse news contents providers, provide articles sorted by category or on the basis of attributes, such as the time at which they were posted. The mobile web should provide easy access to the categories or news contents preferred by users because user interface of wireless devices, particularly cell phones is limited for browsing between contents.This paper presents a mobile web news recommendation system (MONERS) that incorporates news article attributes and user preferences with regard to categories and news articles. User preference of news articles are estimated by aggregating news article importance and recency, user preference change, and user segment’s preference on news categories and articles. Performance of MONERS was tested in an actual mobile web environment; news organized by category had more page hits, while recommended news had a higher overall article read ratio.     

蚁群算法在Web站点导航中的应用研究

Web站点导航是Web数据挖掘的一个重要研究领域,是准确理解用户访问网站行为的关键;传统Web站点导航技术很难全面反映出用户对页面浏览的兴趣程度,找到用户感兴趣页面路径准确度比较低;为提高找到用户感兴趣页面路径准确度,提出一种基于蚁群算法的Web站点导航技术;将网络用户看作人工的蚂蚁,用户的浏览兴趣作蚂蚁的信息素,通过利用Web日志数据采用正负反馈机制和路径概率选择机制建立一个Web站点导航模型,挖掘用户感兴趣页面的导航路径;仿真实验结果表明,基于蚁群算法的Web站点导航技术提高了找到用户感兴趣页面路径准确度,更加能够准确反映出用户的浏览兴趣,用于Web站点导航是可行的。     

B-CWB: Bilingual Comparative Web Browser Based on Content-Synchronization and Viewpoint Retrieval

We propose a new way of browsing bilingual web sites through concurrent browsing with automatic similar-content synchronization and viewpoint retrieval facilities. Our prototype browser system is called the Bilingual Comparative Web Browser (B-CWB) and it concurrently presents bilingual web pages in a way that enables their contents to be automatically synchronized. The B-CWB allows users to browse multiple web news sites concurrently and compare their viewpoint of news articles written in different languages (English and Japanese). Our viewpoint retrieval is based on similar and different detection. We described categorizing pages in terms of viewpoint: the entire similarity, the content difference, and subject difference. Content synchronization means that user operation (scrolling or clicking) on one web page does not necessarily invoke the same operations on the other web page to preserve similarity of content between the multiple web pages. For example, scrolling a web page may invoke passage-level viewpoint retrieval on the other web page. Clicking a web page (and obtaining a new web page) invokes page-level viewpoint retrieval within the other site's pages through the use of an English-Japanese dictionary.     

User-centric adaptation of Web information for small screens

A global increase in PDA and cell phone ownership and a rise in the use of wireless services lead mobile browsing to become an important means of Internet access. However, mobile devices have a small screen, which limits the usability of mobile browsing. This paper presents a novel method that automatically adapts a desktop presentation to a mobile presentation, proceeding in two steps: detecting boundaries among different information blocks (i.e., page segmentation) and then generating a user preferred adaptive layout. Distinct from other approaches, our approach analyzes both the DOM structure and the visual layout to detect closely related contents. In the process of page segmentation, our approach first divides a Web page into several common areas (such as top, bottom, and main content), and then further identifies different topics in the main content based on the visual and structural analysis. The page segmentation produces a block tree, which represents the information organization underlying a Web page. Based on the block tree, an adaptive layout, which places closely related information in proximity and minimizes scrolling, is automatically generated. All current approaches support only one fixed style (e.g., a narrow-page style) to render an adaptive layout for a diverse range of users, Websites, and devices. This “one size fits all” strategy could not offer universal usability. Our approach supports a user to flexibly specify the style of an adaptive layout according to his/her personal preferences. The theoretical foundation of such a user-centric adaptive layout is a feature space, which specifies an adaptive layout from different aspects, such as the location and presentation of navigation options. A user can visually manipulate those features to create a personalized style in a graphical user interface, and then an adaptive layout consistent with the personalized style is automatically generated. The user study based on a prototype shows the usability and efficiency of mobile browsing are significantly improved.     

Real-time Internet news browsing: Information vs. experience-related gratifications and behaviors

This study utilized lab observations with 49 subjects to observe what users encounter and how users behave in real-time Internet news browsing. We analyzed users’ selection of news platform, exposure to different topics of news content, and usage of different presentation elements by coding the screen videos. In addition, survey data with the subjects allow us to explore the links between gratifications and Internet news browsing behaviors. Our analyses suggest that users exert their control through actively and selectively interacting with the news services at the platform, content and presentation level to fulfill their different gratifications. In specific, gratifications based on information utility and those based on usage experience show different relations with different kinds of news browsing behaviors. Both the theoretical and methodological contributions are discussed at the end of this paper.     

Exploring casual point-and-tilt interactions for mobile geo-blogging

People record and share their experiences through text, audio and video. Increasingly they do this blogging from mobile devices. We illustrate a novel, mobile, low interaction cost approach to support the creation of a rich record of journeys made and places encountered: by pointing and tilting a mobile, users indicate their interests in a location. We built three mobile prototypes to explore the approach—the first one combines gestures and visual map feedback; the second provides a simpler visual interface; the third supports eyes-free interaction, allowing the user to simply point-and-tilt, with no visual display required. We describe two field studies undertaken to understand the value of the interaction styles afforded, then continue with a further user study to assess the interaction speed and accuracy between these interaction methods. We present the results of these studies and raise issues relevant to their design and to the wider class of devices and services concerned with mobile spatial information access.     

An optimized approach for massive web page classification using entity similarity based on semantic network

With the development of mobile technology, the users browsing habits are gradually shifted from only information retrieval to active recommendation. The classification mapping algorithm between users interests and web contents has been become more and more difficult with the volume and variety of web pages. Some big news portal sites and social media companies hire more editors to label these new concepts and words, and use the computing servers with larger memory to deal with the massive document classification, based on traditional supervised or semi-supervised machine learning methods. This paper provides an optimized classification algorithm for massive web page classification using semantic networks, such as Wikipedia, WordNet. In this paper, we used Wikipedia data set and initialized a few category entity words as class words. A weight estimation algorithm based on the depth and breadth of Wikipedia network is used to calculate the class weight of all Wikipedia Entity Words. A kinship-relation association based on content similarity of entity was therefore suggested optimizing the unbalance problem when a category node inherited the probability from multiple fathers. The keywords in the web page are extracted from the title and the main text using N-gram with Wikipedia Entity Words, and Bayesian classifier is used to estimate the page class probability. Experimental results showed that the proposed method obtained good scalability, robustness and reliability for massive web pages.     

基于微博用户模型的个性化新闻推荐

新闻推荐是互联网推荐系统的研究热点之一,传统的新闻推荐方法是在新闻网站内,通过记录用户浏览的新闻来实现推荐应用。然而,许多新闻网站并不强制要求用户必须注册才能浏览新闻。微博作为目前最主流的自媒体形式,它由用户自己发起或传递,进而实现草根媒体的职能。对新闻进行高效组织并使用微博进行新闻推荐,这是之前研究欠缺的。该文通过提出基于微博分析的新闻推荐,提出了基于新闻和微博本身特点的解决方法,从而实现微博和新闻的关联。实验表明,该文设计的各模块具备较高的效率和实用效果。     

Visual guided navigation for image retrieval

In this work, we are interested in technologies that will allow users to actively browse and navigate large image databases and to retrieve images through interactive fast browsing and navigation. The development of a browsing/navigation-based image retrieval system has at least two challenges. The first is that the system's graphical user interface (GUI) should intuitively reflect the distribution of the images in the database in order to provide the users with a mental picture of the database content and a sense of orientation during the course of browsing/navigation. The second is that it has to be fast and responsive, and be able to respond to users actions at an interactive speed in order to engage the users. We have developed a method that attempts to address these challenges of a browsing/navigation based image retrieval systems. The unique feature of the method is that we take an integrated approach to the design of the browsing/navigation GUI and the indexing and organization of the images in the database. The GUI is tightly coupled with the algorithms that run in the background. The visual cues of the GUI are logically linked with various parts of the repository (image clusters of various particular visual themes) thus providing intuitive correspondences between the GUI and the database contents. In the backend, the images are organized into a binary tree data structure using a sequential maximal information coding algorithm and each image is indexed by an n-bit binary index thus making response to users’ action very fast. We present experimental results to demonstrate the usefulness of our method both as a pre-filtering tool and for developing browsing/navigation systems for fast image retrieval from large image databases.     

A personal Web page tailoring toolkit for mobile devices

It is common to browse web pages via mobile devices. However, most of the web pages were designed for desktop computers equipped with big screens. When browsing on mobile devices, a user has to scroll up and down to find the information they want because of the limited screen size. Some commercial products reformat web pages. However, the result pages still contain irrelevant information. We propose a system to personalize users’ mobile web pages. A user can determine which blocks in a web page should be retained. The sequence of these blocks can also be altered according to individual preferences.     

Distraction effects of contextual advertising on online news processing: an eye-tracking study

Although recent scholarship has shown that congruency between editorial content and display advertising on web pages can lead to favourable outcomes for the advertiser, it is unclear whether these gains for advertisers come at the expense of users’ ability to process the content. To examine whether contextual in-page advertising distracts users during information processing, a 2 (target message argument type: weak/strong)?×?2 (ad relevance: high/low) between-subjects factorial experiment (N?=?99) examined how readers of a news article about risks associated with texting while driving (a) paid attention to the article, (b) paid attention to the advertisements, and (c) were persuaded by the article contents. Participants’ visual attention was captured unobtrusively using a device-mounted eye-tracking device. The findings show that readers were more likely to be persuaded by weaker arguments when the article was presented alongside highly relevant display ads than when the article was presented alongside less relevant ads. Readers also paid greater attention to relevant ads than irrelevant ads, and, surprisingly, readers in the strong argument condition paid more attention to the story content when it was presented alongside relevant ads. The implications for theories of visual attention and for online content publishers are discussed.     

Using a cognitive model to generate web navigation support

A computational cognitive model of web navigation is proposed. Based on theories and models of text comprehension and web navigation, the plausibility of the proposed model is discussed. The model was used to generate navigation support and this support was offered to users in real time during their navigation sessions, in two experiments. In the first experiment navigation support was offered in the auditory modality and it had a positive effect on user's task performance, especially for users with low spatial abilities. In the second experiment navigation support was offered in the visual modality and users positively evaluated it. Users navigated in a more structured way, judged the system as more usable, and perceived themselves as less disoriented. Support did also here lead to better task performance. Finally, some aspects concerning further enhancement of the validity of the proposed model and its practical relevance are discussed.     

Age differences in information finding tasks: Performance and visual exploration strategy with different web page layouts

Recent research suggests that older Internet users seem to find it more difficult to locate navigation links than to find information content in web pages. One possibility is that older Internet users’ visual exploration of web pages is more linear in nature, even when this type of processing is not appropriate for the task. In the current study, the eye movements of young and older Internet users were recorded using an ecological version of the web pages or a discursive version designed to induce a linear exploration. The older adults found more targets when performing content-oriented compared to navigation-oriented searches, thus replicating previous results. Moreover, they performed less well than young people only when required to locate navigation links and tended to outperform the younger participants in content-oriented searches. Although the type of search task and type of web page resulted in different visual strategies, little or no support was found for the hypothesis that older participants explore web pages in a more linear way in cases where this strategy was not appropriate. The main conclusion is that differences in visual exploration do not seem to mediate the specific difficulty older adults experience in navigation-oriented searches in web pages.     

结合使用挖掘和内容挖掘的web推荐服务

随着Internet的基础结构不断扩大和其所含信息的持续增长,Internet用户越来越感觉容易在WWW服务中“资源迷向”。提高用户访问效率的方法有页面预取技术,站点动态重构技术和web个性化推荐技术等。现有的大多数web个性化推荐技术主要是基于用户使用记录的数据挖掘方法,没有或很少考虑结合页面内容—这才是用户真正感兴趣的。该文提出一种结合用户使用挖掘和内容挖掘的web推荐服务,该推荐服务根据频繁最大前向访问路径,提出含有导航页和内容页的频繁访问路径图概念,根据滑动窗口内的最近用户访问页面内容和候选推荐集中页面内容相关性,来向用户提供个性化推荐服务。经推荐质量分析,这种方法具有较好的推荐优化能力。     

一种融合读者心情要素的新闻推送方法

互联网技术的飞速发展增强了用户与网络新闻间的交互性,使得网络新闻不仅包含传统的新闻内容和时间信息,还包含读者心情等交互信息。如何充分挖掘新闻特性,为用户提供便捷的浏览体验已逐渐成为新闻相关领域的研究热点。为方便用户通过输入查询词和心情浏览感兴趣的新闻,该文在考虑新闻的传统特性的同时,融合读者心情要素,提出一种全新的新闻推送方法。该方法重点研究依据读者心情的新闻排序算法,并考虑新闻内容与用户查询的主题相关性,以及新闻重要程度随时间衰减的特性,最终实现一种全新的新闻推送模式。基于所提方法,该文设计了一个融合读者心情要素的新闻推送系统,验证了该方法的有效性。     

Web news mining in an evolving framework

Online news has become one of the major channels for Internet users to get news. News websites are daily overwhelmed with plenty of news articles. Huge amounts of online news articles are generated and updated everyday, and the processing and analysis of this large corpus of data is an important challenge. This challenge needs to be tackled by using big data techniques which process large volume of data within limited run times. Also, since we are heading into a social-media data explosion, techniques such as text mining or social network analysis need to be seriously taken into consideration.In this work we focus on one of the most common daily activities: web news reading. News websites produce thousands of articles covering a wide spectrum of topics or categories which can be considered as a big data problem. In order to extract useful information, these news articles need to be processed by using big data techniques. In this context, we present an approach for classifying huge amounts of different news articles into various categories (topic areas) based on the text content of the articles. Since these categories are constantly updated with new articles, our approach is based on Evolving Fuzzy Systems (EFS). The EFS can update in real time the model that describes a category according to the changes in the content of the corresponding articles. The novelty of the proposed system relies in the treatment of the web news articles to be used by these systems and the implementation and adjustment of them for this task. Our proposal not only classifies news articles, but it also creates human interpretable models of the different categories. This approach has been successfully tested using real on-line news.     

Navigation techniques for small-screen devices: An evaluation on maps and web pages

Several techniques have been proposed to support user navigation of large information spaces (e.g., maps or web pages) on small-screen devices such as PDAs and Smartphones. In this paper, we present the results of an evaluation that compared three of these techniques to determine how they might affect performance and satisfaction of users. Two of the techniques are quite common on mobile devices: the first one (DoubleScrollbar) is the standard combination of two scrollbars for separate horizontal and vertical scrolling with zoom buttons to change the scale of the information space, the second one (Grab&Drag) enables users to navigate the information space by directly dragging its currently displayed portion, while zooming is handled through a slider control. The last technique (Zoom-Enhanced Navigator or ZEN) is an extension and adaptation to mobile screens of Overview&Detail approaches, which are based on displaying an overview of the information space together with a detail view of a portion of that space. In these approaches, navigation is usually supported by (i) highlighting in the overview which portion of space is displayed in the detail view, and (ii) allowing users to move the highlight within the overview area to change the corresponding portion of space in the detail area. Our experimental evaluation concerned tasks involving maps as well as web page navigation. The paper analyzes in detail the obtained results in terms of task completion times, number and duration of user interface actions, accuracy of the gained spatial knowledge, and subjective preferences.     

基于用户浏览日志的上下文相关新闻推荐

个性化新闻推荐的难点在于用户在浏览新闻时目的性不强,容易受各种环境因素的影响,导致其浏览行为难以预测。以往的研究仅仅强调推荐内容相关的或者和用户长期兴趣相匹配的新闻,忽视了环境因素的影响。为此,需要研究上下文相关的新闻推荐算法。具体做法是从用户的浏览日志中提取上下文相关特征,然后训练一个Logistic回归模型来预测用户接下来最可能阅读的新闻。真实数据上的实验结果表明,上下文相关新闻推荐方法效果明显好于传统方法,也验证了上下文信息对用户浏览行为的重要影响。