Identifying humanitarian information for emergency response by modeling the correlation and independence between text and images

Information residing in multiple modalities (e.g., text, image) of social media posts can jointly provide more comprehensive and clearer insights into an ongoing emergency. To identify information valuable for humanitarian aid from noisy multimodal data, we first clarify the categories of humanitarian information, and define a multi-label multimodal humanitarian information identification task, which can adapt to the label inconsistency issue caused by modality independence while maintaining the correlation between modalities. We proposed a Multimodal Humanitarian Information Identification Model that simultaneously captures the Correlation and Independence between modalities (CIMHIM). A tailor-made dataset containing 4,383 annotated text-image pairs was built to evaluate the effectiveness of our model. The experimental results show that CIMHIM outperforms both unimodal and multimodal baseline methods by at least 0.019 in macro-F1 and 0.022 in accuracy. The combination of OCR text, object-level features, and the decision rule based on label correlations enhances the overall performance of CIMHIM. Additional experiments on a similar dataset (CrisisMMD) also demonstrate the robustness of CIMHIM. The task, model, and dataset proposed in this study contribute to the practice of leveraging multimodal social media resources to support effective emergency response.     

知识共创视域下健康信息用户依从意愿的影响机制研究

【目的/意义】从知识共创理论和信息采纳模型出发探究在线健康信息用户依从意愿的影响机制,为改善在 线健康信息服务、提高在线健康信息用户依从有效性提供参考和建议。【方法/过程】首先,基于信息采纳模型,构建 知识共创视域下的在线健康信息用户依从意愿模型。其次,选取知乎问答社区中的牙齿美白话题为背景材料,设 计 2（治疗方案信息类型：保守与新兴）* 2（信息质量：高与低）* 2（来源可信度：高与低）的析因实验。最后,利用 SmartPLS 3.0软件对收集到的408份有效问卷数据进行分析,以检验模型有效性。【结果/结论】健康信息质量和信息 来源可信度正向影响信息有用性和信息不对称,信息有用性和信息不对称显著影响用户依从意愿,健康治疗方案 类型在信息不对称与用户依从意愿、信息有用性与用户依从意愿,以及用户卷入度的调节作用等路径中发挥权变 效应。【创新/局限】引入信息不对称和用户卷入度两个因素探讨了在线健康信息用户依从意愿的影响机制及其管 理启示,但研究对象局限于在校大学生,后续研究还应关注用户依从在在线健康社区知识共创中的作用机制。     

Inferring user knowledge level from eye movement patterns

The acquisition of information and the search interaction process is influenced strongly by a person’s use of their knowledge of the domain and the task. In this paper we show that a user’s level of domain knowledge can be inferred from their interactive search behaviors without considering the content of queries or documents. A technique is presented to model a user’s information acquisition process during search using only measurements of eye movement patterns. In a user study (n = 40) of search in the domain of genomics, a representation of the participant’s domain knowledge was constructed using self-ratings of knowledge of genomics-related terms (n = 409). Cognitive effort features associated with reading eye movement patterns were calculated for each reading instance during the search tasks. The results show correlations between the cognitive effort due to reading and an individual’s level of domain knowledge. We construct exploratory regression models that suggest it is possible to build models that can make predictions of the user’s level of knowledge based on real-time measurements of eye movement patterns during a task session.     

Improving the multimodal probabilistic semantic model by ELM classifiers

The multi-modal retrieval is considered as performing information retrieval among different modalities of multimedia information. Nowadays, it becomes increasingly important in the information science field. However, it is so difficult to bridge the meanings of different multimedia modalities that the performance of multimodal retrieval is deteriorated now. In this paper, we propose a new mechanism to build the relationship between visual and textual modalities and to verify the multimodal retrieval. Specifically, this mechanism depends on the multimodal binary classifiers based on the Extreme Learning Machine (ELM) to verify whether the answers are related to the query examples. Firstly, we propose the multimodal probabilistic semantic model to rank the answers according to their generative probabilities. Furthermore, we build the multimodal binary classifiers to filter out unrelated answers. The multimodal binary classifiers are called the word classifiers. It can improve the performance of the multimodal probabilistic semantic model. The experimental results show that the multimodal probabilistic semantic model and the word classifiers are effective and efficient. Also they demonstrate that the word classifiers based on ELM not only can improve the performance of the probabilistic semantic model but also can be easily applied to other probabilistic semantic models.     

Implicit information need as explicit problems,help, and behavioral signals

Information need is one of the most fundamental aspects of information seeking, which traditionally conceptualizes as the initiation phase of an individual’s information seeking behavior. However, the very elusive and inexpressible nature of information need makes it hard to elicit from the information seeker or to extract through an automated process. One approach to understanding how a person realizes and expresses information need is to observe their seeking behaviors, to engage processes with information retrieval systems, and to focus on situated performative actions. Using Dervin’s Sense-Making theory and conceptualization of information need based on existing studies, the work reported here tries to understand and explore the concept of information need from a fresh methodological perspective by examining users’ perceived barriers and desired helps in different stages of information search episodes through the analyses of various implicit and explicit user search behaviors. In a controlled lab study, each participant performed three simulated online information search tasks. Participants’ implicit behaviors were collected through search logs, and explicit feedback was elicited through pre-task and post-task questionnaires. A total of 208 query segments were logged, along with users’ annotations on perceived problems and help. Data collected from the study was analyzed by applying both quantitative and qualitative methods. The findings identified several behaviors – such as the number of bookmarks, query length, number of the unique queries, time spent on search results observed in the previous segment, the current segment, and throughout the session – strongly associated with participants’ perceived barriers and help needed. The findings also showed that it is possible to build accurate predictive models to infer perceived problems of articulation of queries, useless and irrelevant information, and unavailability of information from users’ previous segment, current segment, and whole session behaviors. The findings also demonstrated that by combining perceived problem(s) and search behavioral features, it was possible to infer users’ needed help(s) in search with a certain level of accuracy (78%).     

Graph neural news recommendation with long-term and short-term interest modeling

With the information explosion of news articles, personalized news recommendation has become important for users to quickly find news that they are interested in. Existing methods on news recommendation mainly include collaborative filtering methods which rely on direct user-item interactions and content based methods which characterize the content of user reading history. Although these methods have achieved good performances, they still suffer from data sparse problem, since most of them fail to extensively exploit high-order structure information (similar users tend to read similar news articles) in news recommendation systems. In this paper, we propose to build a heterogeneous graph to explicitly model the interactions among users, news and latent topics. The incorporated topic information would help indicate a user’s interest and alleviate the sparsity of user-item interactions. Then we take advantage of graph neural networks to learn user and news representations that encode high-order structure information by propagating embeddings over the graph. The learned user embeddings with complete historic user clicks capture the users’ long-term interests. We also consider a user’s short-term interest using the recent reading history with an attention based LSTM model. Experimental results on real-world datasets show that our proposed model significantly outperforms state-of-the-art methods on news recommendation.     

信息系统实施用户抵制研究综述

综述IS领域解释用户抵制行为的研究,根据其研究对象,分为个体层抵制行为研究和组织层抵制行为研究,探讨其研究视角和理论发展,试图厘清IS领域中用户抵制研究的发展脉络,希望给出研究理论的全貌,为今后更好的进行用户抵制研究打下基础。     

面向服务的信息系统持续使用意愿的实证研究

在信息系统持续使用模型（ECT-IT）的基础上,结合营销学的关系质量理论,在该模型中加入用户信任和转移成本两个因子,用电子服务质量代替期望确认变量,选取大学生网上银行持续使用行为作为研究对象进行实证研究。结果表明：用户感知的信息质量、系统质量和服务质量,对用户感知有用性和用户满意有着显著的正向影响;用户感知有用性、满意、信任和转移成本,都对用户持续使用意愿有着显著的正向影响,影响程度从大到小分别是满意、转移成本、信任和感知有用性。     

Promoting the continuing usage of strategic information systems: The role of supervisory leadership in the successful implementation of enterprise systems

Organizations face significant challenges in capturing value from their investments in strategic information systems such as enterprise systems (ES). Managers are a powerful source of influence shaping the post-adoption attitudes and behaviors of users and the success of ES. However, the extant IS literature has focused primarily on the role of top management and theoretical explanations of the role of supervisors in fostering continuing usage of ES are lacking. Drawing on transformational leadership theory and the IS continuance (ISC) model, this paper conceptualizes a theoretical model differentiating the influence mechanisms through which different types of leadership behaviors influence the success of ES. Data collected from 192 users of ES confirms our theorization. We find that transformational leadership behaviors of supervisors influence users’ evaluations of satisfaction and perceived usefulness, while their transactional leadership behaviors influence users’ ES continuance intention by moderating the effects of user satisfaction and perceived usefulness on ES continuance intention. This study advances research on the role of leadership behaviors of supervisors in capturing value from enterprise systems. The research also contributes to practice by suggesting effective strategies for promoting continued usage of mission critical systems such as enterprise systems and delivering value from firms’ IT investments.     

An information delivery model for banking business

Banking is an enterprise consists of different levels of users with the requirement of different levels of information. We propose an information delivery model for banking business which takes information from business analysis and finds the best user for this information with respect to criteria and delivers the multi criteria reporting. There are many multi criteria decision making techniques [MCDM] available to find the best alternative in MCDM problem. We applied fuzzy MCDM technique which resolves inconsistency and uncertainty issues involved in decision making of information delivery for bank users. This model classifies most preferred user to least preferred user for the given information using fuzzy score. This information delivery model and its layers can be applied to other domains to build information delivery model.     

电子服务质量对社会化阅读服务用户持续使用的影响研究——以移动新闻APP为例

[目的/意义]本研究以期望-确认理论（ECT）和技术接受模型（TAM）为理论基础，同时引入IT自我效能和电子服务质量因素，提出一个整合的模型用以解释和预测用户对社会化阅读服务的持续使用行为。[方法/过程]采用问卷方式共收到有效样本589份，使用结构方程模型（SEM）对理论模型中的变量关系进行假设检验。[结果/结论]结果发现：1）整合的模型能够分别解释用户满意度和持续使用意向52%和45%的方差变异量。2）感知有用性、感知易用性和满意度是用户持续使用意向的决定性因素。3） IT自我效能通过感知易用性对用户持续使用意向产生间接影响。4）此外，电子服务质量中的效率、信息质量和隐私安全等外生因素分别对用户的确认程度产生显著正向影响，进而影响用户满意度。     

Integration of user profiles: models and experiments in information retrieval

One difficult problem in information retrieval (IR) is the proper interpretation of user queries. It is extremely hard for users to express their information needs in a specific yet exhaustive way. In an effort to alleviate this problem, two theoretical models have been proposed to utilize user characteristics maintained in the form of a user profile. Although the idea of integrating user profiles into an IR system is intuitively appealing, and the models seem viable, no research to date has established a foundation for the roles of user profiles in such a system. Aiming at the investigation of the roles of user profiles, therefore, this study first identifies and extends various query/profile interaction models to provide a ground upon which the investigation can be undertaken. From a continuum of models characterized on the basis of interaction types, metrics, and parameters, nearly 400 models are chosen to investigate the “model space.” New measures are developed based on the notion of user satisfaction/frustration. In addition, three different criteria are used to guide users in making judgments on the quality of retrieved items. Analysis of the data obtained from the experiments shows that, for a wide variety of criteria and metrics, there are always some query/profile interaction models that outperform the query alone model. In addition, preferable characteristics for different criteria are identified in terms of interaction types, parameters, and metrics.     

MGAT: Multimodal Graph Attention Network for Recommendation

Graph neural networks (GNNs) have shown great potential for personalized recommendation. At the core is to reorganize interaction data as a user-item bipartite graph and exploit high-order connectivity among user and item nodes to enrich their representations. While achieving great success, most existing works consider interaction graph based only on ID information, foregoing item contents from multiple modalities (e.g., visual, acoustic, and textual features of micro-video items). Distinguishing personal interests on different modalities at a granular level was not explored until recently proposed MMGCN (Wei et al., 2019). However, it simply employs GNNs on parallel interaction graphs and treats information propagated from all neighbors equally, failing to capture user preference adaptively. Hence, the obtained representations might preserve redundant, even noisy information, leading to non-robustness and suboptimal performance. In this work, we aim to investigate how to adopt GNNs on multimodal interaction graphs, to adaptively capture user preference on different modalities and offer in-depth analysis on why an item is suitable to a user. Towards this end, we propose a new Multimodal Graph Attention Network, short for MGAT, which disentangles personal interests at the granularity of modality. In particular, built upon multimodal interaction graphs, MGAT conducts information propagation within individual graphs, while leveraging the gated attention mechanism to identify varying importance scores of different modalities to user preference. As such, it is able to capture more complex interaction patterns hidden in user behaviors and provide a more accurate recommendation. Empirical results on two micro-video recommendation datasets, Tiktok and MovieLens, show that MGAT exhibits substantial improvements over the state-of-the-art baselines like NGCF (Wang, He, et al., 2019) and MMGCN (Wei et al., 2019). Further analysis on a case study illustrates how MGAT generates attentive information flow over multimodal interaction graphs.     

高校图书馆微信公众号用户消极使用行为影响因素研究

[目的/意义]探究高校图书馆微信公众号用户消极使用行为的影响因素,为改进服务质量、优化使用体验和提升用户活跃度等提供有价值的意见和建议。[方法/过程]借鉴信息过载、信息系统成功模型及媒介系统依赖理论的相关研究成果,构建图书馆微信公众平台用户消极使用行为模型,并通过问卷调查和结构方程模型进行验证。[结果/结论]信息过载正向影响用户不满意度,信息质量、服务质量及依赖均负向影响用户不满意度,不满意度正向影响用户的忽略和取关行为,依赖负向影响用户的忽略行为和取关行为;不满意度对忽略行为影响程度大于取关行为,而依赖对取关行为的影响程度大于忽略行为。     

在线健康社区用户诊疗信息求助行为——外部因素、个体动机与形成路径

[目的/意义]探究影响在线健康社区用户诊疗信息求助行为的外部因素、个体动机与形成路径,为在线健康社区生态圈的平衡和可持续发展提供参考建议。[方法/过程]以信息生态理论为分析视角,从信息、信息人、信息技术和信息环境4个维度提炼出影响因素和个体动机,并选择技术接受模型为研究框架提出研究假设,进而构建形成路径的理论模型。选取"好大夫在线"、"寻医问药网"、"39健康网"等在线健康社区为实证研究数据来源,采用"情境实验+调查问卷"的研究方法获取437份有效样本数据,利用SmartPLS2.0检验理论模型。[结果/结论]求助自我效能负向影响感知有用性,健康信息素养、求助经验、感知易用性、信息准确性、相关性、及时性正向影响感知有用性,求助自我效能和健康信息素养正向影响感知易用性。按照显著程度,直接影响求助意愿因素依次为求助经验、社会容认度、感知隐私风险、感知易用性、感知有用性、平台信任。     

A semantic Bayesian network approach to retrieving information with intelligent conversational agents

As access to information becomes more intensive in society, a great deal of that information is becoming available through diverse channels. Accordingly, users require effective methods for accessing this information. Conversational agents can act as effective and familiar user interfaces. Although conversational agents can analyze the queries of users based on a static process, they cannot manage expressions that are more complex. In this paper, we propose a system that uses semantic Bayesian networks to infer the intentions of the user based on Bayesian networks and their semantic information. Since conversation often contains ambiguous expressions, the managing of context and uncertainty is necessary to support flexible conversational agents. The proposed method uses mixed-initiative interaction (MII) to obtain missing information and clarify spurious concepts in order to understand the intention of users correctly. We applied this to an information retrieval service for websites to verify the usefulness of the proposed method.     

The effects of perceived chronic pressure and time constraint on information search behaviors and experience

In this paper, we explore the effects of individual pressure level and time constraint on searchers' behaviors and their assessment of search experience within the framework of interactive information retrieval. A user experiment was conducted in which 40 participants individually searched for information in a laboratory setting under two conditions: with time constraint (TC) and with no time constraint (NTC). Participants filled in a Perceived Stress Scale questionnaire to measure their chronic pressure value (subjective stress), and their pressure value was recorded as their individual characteristic. The results showed that the more chronic pressure the searcher has, the more search efforts they devote, including more time in searching and more time to complete the search tasks, especially when there was no time constraint. Time constraint and searchers’ pressure value had a significant effect on users’ numbers of scrolling actions per minute. The results indicate that when given a time constraint, searchers with higher-pressure values tend to lower their reading or scanning speed, while searchers with lower-pressure values tend to accelerate their reading or scanning speed. The results suggested different people would react to the time condition change in different ways, especially people with higher pressure. Therefore, it is necessary to examine users’ search behaviors in person-in-situation frameworks to analyze the effects of contextual factors on users. This study contributes to our knowledge of how contextual factors and individual characteristics affect searchers’ behaviors and have implications for the design of IIR systems.     

Group event recommendation based on graph multi-head attention network combining explicit and implicit information

In event-based social networks (EBSN), group event recommendation has become an important task for groups to quickly find events that they are interested in. Existing methods on group event recommendation either consider just one type of information, explicit or implicit, or separately model the explicit and implicit information. However, these methods often generate a problem of data sparsity or of model vector redundancy. In this paper, we present a Graph Multi-head Attention Network (GMAN) model for group event recommendation that integrates the explicit and implicit information in EBSN. Specifically, we first construct a user-explicit graph based on the user's explicit information, such as gender, age, occupation and the interactions between users and events. Then we build a user-implicit graph based on the user's implicit information, such as friend relationships. The incorporated both explicit and implicit information can effectively describe the user's interests and alleviate the data sparsity problem. Considering that there may be a correlation between the user's explicit and implicit information in EBSN, we take the user's explicit vector representation as the input of the implicit information aggregation when modeling with graph neural networks. This unified user modeling can solve the aforementioned problem of user model vector redundancy and is also suitable for event modeling. Furthermore, we utilize a multi-head attention network to learn richer implicit information vectors of users and events from multiple perspectives. Finally, in order to get a higher level of group vector representation, we use a vanilla attention mechanism to fuse different user vectors in the group. Through experimenting on two real-world Meetup datasets, we demonstrate that GMAN model consistently outperforms state-of-the-art methods on group event recommendation.     

Interpreting the web-mining results by cognitive map and association rule approach

A variety of the web-mining techniques are now being extensively utilized to extract useful knowledge about customer behaviors on the Internet. However, the naive interpretation of the web-mining results would lead to poor decision on customer behaviors, which is likely to cause waste of time and efforts on managing electronic commerce strategy. To overcome this pitfall, this study proposes using the cognitive map-based interpretation of the web-mining results. Conventional approach to obtaining the web-mining results is based on the association rule approach (ARA), while the cognitive map approach (CMA) is believed to provide more robust support in interpreting the web-mining results. Therefore, to compare the interpretation capability of the two approaches, the four constructs such as perceived usefulness, causality, information richness, users’ attitude and intention to use the approaches are adopted in the research model and tested against the questionnaire data. The test results obtained through applying the structural equation models reveal that CMA is comparable to ARA and the cognitive map has a great potential in helping enrich the interpretation of the web mining results and build more effective Internet business strategy.