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Sven H. Koch Rumyana Proynova Barbara Paech Thomas Wetter 《Ethics and Information Technology》2013,15(1):45-61
Software users have different sets of personal values, such as benevolence, self-direction, and tradition. Among other factors, these personal values influence users’ emotions, preferences, motivations, and ways of performing tasks—and hence, information needs. Studies of user acceptance indicate that personal traits like values and related soft issues are important for the user’s approval of software. If a user’s dominant personal value were known, software could automatically show an interface variant which offers information and functionality that best matches his or her dominant value. A user’s dominant personal value is the one that most strongly influences his or her attitudes and behaviors. However, existing methods for measuring a user’s values are work intensive and/or interfere with the user’s privacy needs. If interface tailoring for very large groups of users is planned, value approximation has to be achieved on a large scale to assign individualized software to all users of the software. Our work focuses on approximating the dominant values of a user with less effort and less impact on privacy. Instead of probing for a user’s values directly, we explore the potential of approximating these values based on the user’s preferences for key tasks. Producing tailored versions of software is a separate topic not in the focus here. In this paper we rather describe a method to identify user values from task preferences and an empirical study of applying parts of this method. We are proposing the method in this paper for the first time except for a preliminary version orally presented at a workshop. The method consists of a research process and an application process. In the research process a researcher has to identify key tasks occurring in a context under investigation which have a relationship to personal values. These key tasks can be used in the application process to approximate the dominant values of new users in a similar context. In this empirical study we show that the research process of our method allows us to determine key tasks which approximate values in the shared context of nursing. The majority of the nurses were found to have one of the three following dominant values: benevolence, self-direction, or hedonism. Data confirmed common expectations: that nurses with the value of benevolence, when compared to all other nurses, had a higher preference for tasks which helped people immediately or improved their circumstances of the treatment. In relation to all other nurses, participants with self-direction disliked tasks which affected their personal freedom, and users with hedonism had a lower preference for tasks which involved physical work and preferred tasks which promised gratification. Our findings advance measurement of personal values in large user groups by asking questions with less privacy concern. However, the method requires substantial efforts during the initial research process to prepare such measurements. Future work includes replicating our method in other contexts and identifying value-dependent tasks for users with other values than the three values our empirical study mainly focused on. 相似文献
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In this paper, we define and present a comprehensive classification of user intent for Web searching. The classification consists of three hierarchical levels of informational, navigational, and transactional intent. After deriving attributes of each, we then developed a software application that automatically classified queries using a Web search engine log of over a million and a half queries submitted by several hundred thousand users. Our findings show that more than 80% of Web queries are informational in nature, with about 10% each being navigational and transactional. In order to validate the accuracy of our algorithm, we manually coded 400 queries and compared the results from this manual classification to the results determined by the automated method. This comparison showed that the automatic classification has an accuracy of 74%. Of the remaining 25% of the queries, the user intent is vague or multi-faceted, pointing to the need for probabilistic classification. We discuss how search engines can use knowledge of user intent to provide more targeted and relevant results in Web searching. 相似文献
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《Information processing & management》2023,60(3):103300
Pre-adoption expectations often serve as an implicit reference point in users’ evaluation of information systems and are closely associated with their goals of interactions, behaviors, and overall satisfaction. Despite the empirically confirmed impacts, users’ search expectations and their connections to tasks, users, search experiences, and behaviors have been scarcely studied in the context of online information search. To address the gap, we collected 116 sessions from 60 participants in a controlled-lab Web search study and gathered direct feedback on their in-situ expected information gains (e.g., number of useful pages) and expected search efforts (e.g., clicks and dwell time) under each query during search sessions. Our study aims to examine (1) how users’ pre-search experience, task characteristics, and in-session experience affect their current expectations and (2) how user expectations are correlated with search behaviors and satisfaction. Our results with both quantitative and qualitative evidence demonstrate that: (1) user expectation is significantly affected by task characteristics, previous and in-situ search experience; (2) user expectation is closely associated with users’ browsing behaviors and search satisfaction. The knowledge learned about user expectation advances our understanding of users’ search behavioral patterns and their evaluations of interaction experience and will also facilitate the design, implementation, and evaluation of expectation-aware user models, metrics, and information retrieval (IR) systems. 相似文献
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【目的/意义】虚拟品牌社区日渐成为企业和用户之间的重要沟通渠道,对于企业而言,如何引导用户助力
社区发展至关重要。基于自我决定理论探寻用户参与行为的影响因素及形成机制。【方法/过程】运用自我决定理
论,构建信息交互对用户参与行为（奉献行为、浏览行为）的影响机制模型,并探寻用户承诺（情感承诺、算计承诺）
在其中的中介作用。利用结构方程模型对收集的386份有效问卷进行分析。【结果/结论】研究结果表明,信息交互
能够有效促进用户参与行为,如奉献行为及浏览行为;情感承诺在信息交互与用户奉献行为间具有中介效应,算计
承诺在信息交互与用户浏览行为间具有中介效应;与算计承诺相比,情感承诺对奉献行为具有较强影响;与情感承
诺相比,算计承诺对浏览行为具有较强影响。【创新/局限】本文基于动机视角探寻信息交互对用户不同参与行为的
影响及作用机制,但尚未对其它影响因素如社会影响及社区类型等开展研究。 相似文献
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《Information processing & management》2020,57(6):102349
Mouse interaction data contain a lot of interaction information between users and Search Engine Result Pages (SERPs), which can be useful for evaluating search satisfaction. Existing studies use aggregated features or anchor elements to capture the spatial information in mouse interaction data, which might lose valuable mouse cursor movement patterns for estimating search satisfaction. In this paper, we leverage regions together with actions to extract sequences from mouse interaction data. Using regions to capture the spatial information in mouse interaction data would reserve more details of the interaction processes between users and SERPs. To modeling mouse interaction sequences for search satisfaction evaluation, we propose a novel LSTM unit called Region-Action LSTM (RALSTM), which could capture the interactive relations between regions and actions without subjecting the network to higher training complexity. Simultaneously, we propose a data augmentation strategy Multi-Factor Perturbation (MFP) to increase the pattern variations on mouse interaction sequences. We evaluate the proposed approach on open datasets. The experimental results show that the proposed approach achieves significant performance improvement compared with the state-of-the-art search satisfaction evaluation approach. 相似文献
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Fattane Zarrinkalam Mohsen Kahani Ebrahim Bagheri 《Information processing & management》2018,54(2):339-357
Inferring users’ interests from their activities on social networks has been an emerging research topic in the recent years. Most existing approaches heavily rely on the explicit contributions (posts) of a user and overlook users’ implicit interests, i.e., those potential user interests that the user did not explicitly mention but might have interest in. Given a set of active topics present in a social network in a specified time interval, our goal is to build an interest profile for a user over these topics by considering both explicit and implicit interests of the user. The reason for this is that the interests of free-riders and cold start users who constitute a large majority of social network users, cannot be directly identified from their explicit contributions to the social network. Specifically, to infer users’ implicit interests, we propose a graph-based link prediction schema that operates over a representation model consisting of three types of information: user explicit contributions to topics, relationships between users, and the relatedness between topics. Through extensive experiments on different variants of our representation model and considering both homogeneous and heterogeneous link prediction, we investigate how topic relatedness and users’ homophily relation impact the quality of inferring users’ implicit interests. Comparison with state-of-the-art baselines on a real-world Twitter dataset demonstrates the effectiveness of our model in inferring users’ interests in terms of perplexity and in the context of retweet prediction application. Moreover, we further show that the impact of our work is especially meaningful when considered in case of free-riders and cold start users. 相似文献
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[目的/意义]本文以用户行为感知视角，研究影响移动社交网络主导型用户与浏览型用户持续使用的因素，为移动社交网络信息服务提供理论基础，并为移动社交网络提供商提出参考与应用借鉴。[方法/过程]分析移动社交网络主导型用户与浏览型用户持续使用意愿影响因素，引入相关变量，构建了基于用户行为感知的移动社交网络信息服务持续使用意愿模型并提出假设，最后通过结构方程模型进行实证分析。[结果/结论]感知有用性、感知易用性、感知娱乐、感知质量等因素均显著影响主导型及浏览型两类用户；服务质量、感知风险、知识获取、个人创新、社会认可、感知信任、感知转换成本等因素对两类用户有不同程度的影响。 相似文献
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[目的/意义]文章旨在丰富在线健康社区领域用户信息浏览行为的研究成果,为在线健康社区建设提供新思路。[方法/过程]本研究设计了一个用户无明确目标导向浏览有问必答网失眠社区信息的实验,借助眼动追踪技术分析用户浏览时的行为特征和影响因素,并比较用户在浏览和查询两种情境下的行为差异。[结果/结论]结果发现,用户的浏览方向在首页页面和内容页面上存在差异,主要使用广度优先策略选择帖子,浏览过程中存在固定的行为模式,浏览和查询两种情境中用户的浏览路径存在一定的区别和联系;在信息加工方面,用户浏览社区首页页面时付出的认知努力比内容页面多,重点关注网页中部的元素;帖子链接排名、广告位置、信息元组织模式、内容主题的差异均对浏览行为有显著影响。最后,本研究根据这些发现提出相应的改善建议。 相似文献
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The main objectives of this research were to compare and contrast the findings of the relationships among different stages of information systems (IS) strategic planning, system planning, plan implementation, and IS effectiveness across various organizations in Taiwan. The intent was to take a broad look at the key concerns of IS planning across cultures. The relationships among three phases of planning (strategic planning, systems planning, and plan implementation), and their relationships with user satisfaction were examined. Five hypotheses are used to examine the relationships. The correlation analysis results support the following hypotheses: (1) the extent of IS systems planning is positively associated with the extent of IS strategic planning; (2) the extent of plan implementation is positively associated with the extent of IS systems planning; and (3) the extent of IS strategic planning is positively associated with the extent of user satisfaction. IS systems planning has no effect on user satisfaction. IS plan implementation has no relationship with user satisfaction. 相似文献
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The ECDIN (Environmental Chemicals Data and Information Network) project started in 1973. During the pilot phase of operation the feasibility of the system was demonstrated using a data base of 4000 compounds and the SIMAS information retrieval system. It was quickly realised that for ECDIN data management was as important as information retrieval and in November 1977, after a study of available software, the ADABAS data base management system was installed at JRC Ispra for ECDIN and other JRC data banks. A design exercise for the ECDIN ADABAS data base has been completed and parts of the existing ECDIN data base have been converted to the new system. The problems encountered and the solutions adopted are discussed. The user interface to ECDIN is still under development. When fully operational ECDIN will be available through EURONET to both casual and specialist users and, in consequence, at least two levels of user interface will be required: (a) a user friendly conversational language designed for the casual user and capable of dealing with the more common types of question, (b) a sophisticated query language capable of answering the more difficult questions, producing “one-off” reports and probably requiring both a specialist knowledge of the data base and a programmer oriented background. The first tentative steps in this direction are described. 相似文献
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《Information processing & management》2020,57(2):102056
Identifying and extracting user communities is an important step towards understanding social network dynamics from a macro perspective. For this reason, the work in this paper explores various aspects related to the identification of user communities. To date, user community detection methods employ either explicit links between users (link analysis), or users’ topics of interest in posted content (content analysis), or in tandem. Little work has considered temporal evolution when identifying user communities in a way to group together those users who share not only similar topical interests but also similar temporal behavior towards their topics of interest. In this paper, we identify user communities through multimodal feature learning (embeddings). Our core contributions can be enumerated as (a) we propose a new method for learning neural embeddings for users based on their temporal content similarity; (b) we learn user embeddings based on their social network connections (links) through neural graph embeddings; (c) we systematically interpolate temporal content-based embeddings and social link-based embeddings to capture both social network connections and temporal content evolution for representing users, and (d) we systematically evaluate the quality of each embedding type in isolation and also when interpolated together and demonstrate their performance on a Twitter dataset under two different application scenarios, namely news recommendation and user prediction. We find that (1) content-based methods produce higher quality communities compared to link-based methods; (2) methods that consider temporal evolution of content, our proposed method in particular, show better performance compared to their non-temporal counter-parts; (3) communities that are produced when time is explicitly incorporated in user vector representations have higher quality than the ones produced when time is incorporated into a generative process, and finally (4) while link-based methods are weaker than content-based methods, their interpolation with content-based methods leads to improved quality of the identified communities. 相似文献
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基于协同过滤（CF）的个性化推荐技术,提出一种移动设备个性化软件推荐系统.该系统根据协同过滤的理论,首先通过软件类别兴趣相似度的计算,筛选出软件类别相似的用户候选集,过滤所有移动用户,减小产生的用户候选推荐集;然后对用户候选推荐集进行最近邻居的相似性计算以找出目标用户的邻居集合,并且对邻居集合中的邻居评分进行实时更新;最后根据兴趣相似度最大的K个邻居形成目标用户的Top-N推荐集.在第三方手机软件管理平台上通过监测推荐软件的下载或浏览量,验证系统的有效性和准确性. 相似文献
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With the increasing provenance of hedonic and social information systems, systems are observed to employ other forms of feedback and design than purely informational in order to increase user engagement and motivation. Three principle classes of motivational design pursuing user engagement have become increasingly established; gamification, quantified-self and social networking. This study investigates how the perceived prominence of these three design classes in users’ use of information system facilitate experiences of affective, informational and social feedback as well as user’s perceived benefits from a system and their continued use intentions. We employ survey data (N = 167) gathered from users of HeiaHeia; an exercise encouragement system that employs features belonging to the three design classes. The results indicate that gamification is positively associated with experiences of affective feedback, quantified-self with experiences of both affective and informational feedback and social networking with experiences of social feedback. Experiences of affective feedback are further strongly associated with user perceived benefits and continued use intentions, whereas experiences of informational feedback are only associated with continued use intentions. Experiences of social feedback had no significant relationship with neither. The findings provide practical insights into how systems can be designed to facilitate different types of feedback that increases users’ engagement, benefits and intentions to continue the use of a system. 相似文献
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This article presents conceptual navigation and NavCon, an architecture that implements this navigation in World Wide Web pages. NavCon architecture makes use of ontology as metadata to contextualize user search for information. Based on ontologies, NavCon automatically inserts conceptual links in Web pages. By using these links, the user may navigate in a graph representing ontology concepts and their relationships. By browsing this graph, it is possible to reach documents associated with the user desired ontology concept. This Web navigation supported by ontology concepts we call conceptual navigation. Conceptual navigation is a technique to browse Web sites within a context. The context filters relevant retrieved information. The context also drives user navigation through paths that meet his needs. A company may implement conceptual navigation to improve user search for information in a knowledge management environment. We suggest that the use of an ontology to conduct navigation in an Intranet may help the user to have a better understanding about the knowledge structure of the company. 相似文献
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Chunlin Li Jingpan Bai Zhao Wenjun Yang Xihao 《Information processing & management》2019,56(1):91-109
Recently, social network has been paid more and more attention by people. Inaccurate community detection in social network can provide better product designs, accurate information recommendation and public services. Thus, the community detection (CD) algorithm based on network topology and user interests is proposed in this paper. This paper mainly includes two parts. In first part, the focused crawler algorithm is used to acquire the personal tags from the tags posted by other users. Then, the tags are selected from the tag set based on the TFIDF weighting scheme, the semantic extension of tags and the user semantic model. In addition, the tag vector of user interests is derived with the respective tag weight calculated by the improved PageRank algorithm. In second part, for detecting communities, an initial social network, which consists of the direct and unweighted edges and the vertexes with interest vectors, is constructed by considering the following/follower relationship. Furthermore, initial social network is converted into a new social network including the undirected and weighted edges. Then, the weights are calculated by the direction and the interest vectors in the initial social network and the similarity between edges is calculated by the edge weights. The communities are detected by the hierarchical clustering algorithm based on the edge-weighted similarity. Finally, the number of detected communities is detected by the partition density. Also, the extensively experimental study shows that the performance of the proposed user interest detection (PUID) algorithm is better than that of CF algorithm and TFIDF algorithm with respect to F-measure, Precision and Recall. Moreover, Precision of the proposed community detection (PCD) algorithm is improved, on average, up to 8.21% comparing with that of Newman algorithm and up to 41.17% comparing with that of CPM algorithm. 相似文献
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《Information processing & management》2022,59(2):102861
Integrating useful input information is essential to provide efficient recommendations to users. In this work, we focus on improving items ratings prediction by merging both multiple contexts and multiple criteria based research directions which were addressed separately in most existent literature. Throughout this article, Criteria refer to the items attributes, while Context denotes the circumstances in which the user uses an item. Our goal is to capture more fine grained preferences to improve items recommendation quality using users’ multiple criteria ratings under specific contextual situations. Therefore, we examine the recommenders’ data from the graph theory based perspective by representing three types of entities (users, contextual situations and criteria) as well as their relationships as a tripartite graph. Upon the assumption that contextually similar users tend to have similar interests for similar item criteria, we perform a high-order co-clustering on the tripartite graph for simultaneously partitioning the graph entities representing users in similar contextual situations and their evaluated item criteria. To predict cluster-based multi-criteria ratings, we introduce an improved rating prediction method that considers the dependency between users and their contextual situations, and also takes into account the correlation between criteria in the prediction process. The predicted multi-criteria ratings are finally aggregated into a single representative output corresponding to an overall item rating. To guide our investigation, we create a research hypothesis to provide insights about the tripartite graph partitioning and design clear and justified preliminary experiments including quantitative and qualitative analyzes to validate it. Further thorough experiments on the two available context-aware multi-criteria datasets, TripAdvisor and Educational, demonstrate that our proposal exhibits substantial improvements over alternative recommendations approaches. 相似文献