Supporting user views

Any emerging standard for object-oriented database systems must include a rich view support mechanism. A user view is a customized window into an application domain. It may be thought of as a simplifying abstraction which hides information that is not accessible to, needed or wanted by a particular user. Because they limit the information available from a given perspective, most views allow read-only access to a database. In this paper it is asserted that by generalizing object identity to include attributes and views - many view updates are made possible. An extended object structure and several categories of view transformation are also presented which allow all user views to be modeled within a single polymorphic database schema.     

Acquiring user models

Existing machine techniques for acquiring user models are characterized along five orthogonal dimensions: passive/active, user-initiated/automatic, logical/plausible, direct/indirect, and on-line/off-line. Passive techniques observe users whereas active techniques query users. User-initiated techniques require that users volunteer information; automatic techniques do not. The logical/plausible dimension measures the accuracy of derived user model data. Indirect techniques build upon data gathered by more direct methods. On-line techniques acquire user models in real-time during user interaction, while off-line techniques work after the user interaction is finished. Commonalities and differences in capabilities and features of different user model acquisition techniques are analyzed along the above dimensions, and the relationship of these techniques to similar techniques in other areas of artificial intelligence are discussed.     

Building user argumentative models

Knowing how a user builds his/her arguments during a discussion gives useful advantages if we want to assist the user or analyse his/her argumentative skills. This paper presents a novel mechanism to build user argumentative models, which captures the argumentative style to generate arguments. To this end, we observe how users generate arguments, and apply a generalised association rules algorithm to discover rules for argument generation. These rules depict the argumentative style of the user. They are composed of an antecedent, which represents the conditions to build an argument, and a consequent, which represents such argument. To evaluate this proposal, we show results obtained in the domain of meeting scheduling. We discovered interesting rules from a group of users discussing in that domain, and checked that about 60% of the arguments that users had generated in a test situation can be also generated from the rules previously learnt, at least partially. Finally, although this work focuses on modelling users’ argumentative style, we discuss how this promising approach could be applied in different knowledge domains.     

Configurable product views based on geometry user requirements

This paper describes an approach at Volvo Truck Corporation where geometry users' requirements are utilised to define configurable product views. The paper is derived from a research framework on geometry management. The objective of the research framework is to improve reuse of geometry by providing relevant geometry-based product information, so-called geometric product views, through application of configuration management. In order to provide relevant product views, specific fundamental product information must exist. This type of information will vary depending on the different types of product views. Several examples on product view are presented. The product views are realised by a developed configuration management system, an example of a configured product view is presented as proof of concept. The configuration management system is currently under industrial implementation.     

Two views of information modeling

The paper compares two views of information modeling. These are the reality mapping and the formal language development view. The former is the mainstream of information systems (IS) literature, whereas the latter has received much less attention. Our comparison focuses on the definition of the information system in both views and evaluates some of their organizational consequences. The implications are discussed in relation to sets of beliefs underlying the two views. The paper concludes that both views are needed in IS design and they complement each other. However, because the formal language development view is less articulated it is suggested that it should receive more research attention in the future.     

Exploiting user feedback to compensate for the unreliability of user models

Natural Language is a powerful medium for interacting with users, and sophisticated computer systems using natural language are becoming more prevalent. Just as human speakers show an essential, inbuilt responsiveness to their hearers, computer systems must tailor their utterances to users. Recognizing this, researchers devised user models and strategies for exploiting them in order to enable systems to produce the best answer for a particular user.Because these efforts were largely devoted to investigating how a user model could be exploited to produce better responses, systems employing them typically assumed that a detailed and correct model of the user was available a priori, and that the information needed to generate appropriate responses was included in that model. However, in practice, the completeness and accuracy of a user model cannot be guaranteed. Thus, unless systems can compensate for incorrect or incomplete user models, the impracticality of building user models will prevent much of the work on tailoring from being successfully applied in real systems. In this paper, we argue that one way for a system to compensate for an unreliable user model is to be able to react to feedback from users about the suitability of the texts it produces. We also discuss how such a capability can actually alleviate some of the burden now placed on user modeling. Finally, we present a text generation system that employs whatever information is available in its user model in an attempt to produce satisfactory texts, but is also capable of responding to the user's follow-up questions about the texts it produces.Dr. Johanna D. Moore holds interdisciplinary appointments as an Assistant Professor of Computer Science and as a Research Scientist at the Learning Research and Development Center at the University of Pittsburgh. Her research interests include natural language generation, discourse, expert system explanation, human-computer interaction, user modeling, intelligent tutoring systems, and knowledge representation. She received her MS and PhD in Computer Science from the University of California at Los Angeles, and her BS in Mathematics and Computer Science from the University of California at Los Angeles. She is a member of the Cognitive Science Society, ACL, AAAI, ACM, IEEE, and Phi Beta Kappa. Readers can reach Dr. Moore at the Department of Computer Science, University of Pittsburgh, Pittsburgh, PA 15260.Dr. Cecile Paris is the project leader of the Explainable Expert System project at USC's information Sciences Institute. She received her PhD and MS in Computer Science from Columbia University (New York) and her bachelor's degree from the University of California in Berkeley. Her research interests include natural language generation and user modeling, discourse, expert system explanation, human-computer interaction, intelligent tutoring systems, machine learning, and knowledge acquisition. At Columbia University, she developed a natural language generation system capable of producing multi-sentential texts tailored to the users level of expertise about the domain. At ISI, she has been involved in designing a flexible explanation facility that supports dialogue for an expert system shell. Dr. Paris is a member of the Association for Computational Linguistics (ACL), the American Association for Artificial Intelligence (AAAI), the Cognitive Science Society, ACM, IEEE, and Phi Kappa Phi. Readers can reach Dr. Paris at USC/ISI, 4676 Admiralty Way, Marina Del Rey, California, 90292     

Charismatic leadership and user acceptance of information technology

Although there is widespread agreement that leadership has important effects on information technology (IT) acceptance and use, relatively little empirical research to date has explored this phenomenon in detail. This paper integrates the unified theory of acceptance and use of technology (UTAUT) with charismatic leadership theory, and examines the role of project champions influencing user adoption. PLS analysis of survey data collected from 209 employees in seven organizations that had engaged in a large-scale IT implementation revealed that project champion charisma was positively associated with increased performance expectancy, effort expectancy, social influence and facilitating condition perceptions of users. Theoretical and managerial implications are discussed, and suggestions for future research in this area are provided.     

An empirical framework for user mobility models: Refining and modeling user registration patterns

In this paper, we examine user registration patterns in empirical WLAN traces, identify elusive patterns that are abused as user movements in constructing empirical mobility models, and analyze them to build up a realistic user mobility model. The examination shows that about 38–90% of transitions are irrelevant to actual user movements. In order to refine the elusive movements, we investigate the geographical relationships among APs and propose a filtering framework for removing them from the trace data. We then analyze the impact of the false-positive movements on an empirical mobility model. The numerical results indicate that the proposed framework improves the fidelity of the empirical mobility model. Finally, we devise an analytical model for characterizing realistic user movements, based on the analysis on the elusive user registration patterns, which emulates elusive user registration patterns and generates true user mobile patterns.     

结合用户背景信息的协同过滤推荐算法

针对个性化推荐系统中协同过滤算法面对的数据稀疏问题,提出了一种结合用户背景信息的推荐算法。该算法充分利用已有的用户数据和领域知识,对用户背景信息的相似度建模,在进行协同过滤前预先填充用户-项评分矩阵。实验表明该方法能够有效地提高推荐精度,并且不会带来性能上的瓶颈。     

Predicting user demographics from music listening information

Online activities such as social networking, online shopping, and consuming multi-media create digital traces, which are often analyzed and used to improve user experience and increase revenue, e. g., through better-fitting recommendations and more targeted marketing. Analyses of digital traces typically aim to find user traits such as age, gender, and nationality to derive common preferences. We investigate to which extent the music listening habits of users of the social music platform Last.fm can be used to predict their age, gender, and nationality. We propose a feature modeling approach building on Term Frequency-Inverse Document Frequency (TF-IDF) for artist listening information and artist tags combined with additionally extracted features. We show that we can substantially outperform a baseline majority voting approach and can compete with existing approaches. Further, regarding prediction accuracy vs. available listening data we show that even one single listening event per user is enough to outperform the baseline in all prediction tasks. We also compare the performance of our algorithm for different user groups and discuss possible prediction errors and how to mitigate them. We conclude that personal information can be derived from music listening information, which indeed can help better tailoring recommendations, as we illustrate with the use case of a music recommender system that can directly utilize the user attributes predicted by our algorithm to increase the quality of it’s recommendations.

     

Computer consultants' views of user participation in the system development process

The form of user participation adopted in any system development process is commonly argued to be an important determinant of its outcome. This study investigated computer consultants' experiences and views of different forms of user participation in the system development process. Thirty-four consultants from 14 Swedish companies of three different sizes filled out a questionnaire (response rate 47%). The results indicated that the consultants viewed both external factors and their own roles as guiding the degree of user participation. The consultants reported that reference groups, seminars/meetings and user testing were common forms of user participation in projects introducing existing programs into organizations and that seminars/meetings, users in project groups and reference groups, were the most common user participation forms in projects developing new programs for an organization. In both contexts, user testing was reported to be most common in the operation/administration phase of the project. The majority of consultants considered union participation to be fairly unimportant. Further, it was found that questionnaires were seldom used for data collection in projects. Finally, the consultants did not appear to categorize development projects by the type of user participation employed in the projects.     

Event detection using user interaction behavior models

In information exchange networks such as email or blog networks, most processes are carried out using exchange of messages. The behavioral analysis in such networks leads to interesting insight which would be quite valuable for organizational or social analysis. In this paper, we investigate user engagingness and responsiveness as two interaction behaviors that help us understand an email network which is one of information exchange networks. Engaging actors are those who can effectively solicit responses from other actors. Responsive actors are those who are willing to respond to other actors. By modeling such behaviors, we are able to measure them and to identify high engaging or responsive actors. We systematically propose novel behavior models to quantify the engagingness and responsiveness of actors in the Enron email network. Furthermore, as one of case studies, we study an event detection problem, based on our proposed behavior models, in the Enron emails. According to our empirical study, we found out meaningful events in Enron. For details, see Sect. 5.     

Latent grouping models for user preference prediction

We tackle the problem of new users or documents in collaborative filtering. Generalization over users by grouping them into user groups is beneficial when a rating is to be predicted for a relatively new document having only few observed ratings. Analogously, generalization over documents improves predictions in the case of new users. We show that if either users and documents or both are new, two-way generalization becomes necessary. We demonstrate the benefits of grouping of users, grouping of documents, and two-way grouping, with artificial data and in two case studies with real data. We have introduced a probabilistic latent grouping model for predicting the relevance of a document to a user. The model assumes a latent group structure for both users and items. We compare the model against a state-of-the-art method, the User Rating Profile model, where only the users have a latent group structure. We compute the posterior of both models by Gibbs sampling. The Two-Way Model predicts relevance more accurately when the target consists of both new documents and new users. The reason is that generalization over documents becomes beneficial for new documents and at the same time generalization over users is needed for new users.     

Discriminative effect of user influence and user responsibility on information system development processes and project management

Context

User participation in information system (IS) development has received much research attention. However, prior empirical research regarding the effect of user participation on IS success is inconclusive. This might be because previous studies overlook the effect of the particular components of user participation and other possible mediating factors.

Objective

The objective of this study is to empirically examine how user influence and user responsibility affect IS project performance. We inspect whether user influence and user responsibility improve the quality of the IS development process and in turn leads to project success, or if they have a direct positive influence on project success.

Method

We conducted a survey of 151 IS project managers in order to understand the impact of user influence and user responsibility on IS project performance. Regression analysis was conducted to assess the relationship among user influence, user responsibility, organizational technology learning, project control, user-developer interaction, and IS project management performance.

Results

This study shows that user responsibility and user influence have a positive effect on project performance through the promotion of IS development processes as mediators, including organizational technology learning, project control, and user-IS interaction.

Conclusion

Our results suggest that user responsibility and user influence respectively play an important role in indirectly and directly impacting project management performance. Results of the analysis imply that organizations and project managers should use both user participation and user influence to improve processes performance, and in turn, increase project success.     

Formal models for user interface design artefacts

There are many different ways of building software applications and of tackling the problems of understanding the system to be built, designing that system and finally implementing the design. One approach is to use formal methods, which we can generalise as meaning we follow a process which uses some formal language to specify the behaviour of the intended system, techniques such as theorem proving or model-checking to ensure the specification is valid (i.e., meets the requirements and has been shown, perhaps by proof or other means of inspection, to have the properties the client requires of it) and a refinement process to transform the specification into an implementation. Conversely, the approach we take may be less structured and rely on informal techniques. The design stage may involve jotting down ideas on paper, brainstorming with users etc. We may use prototyping to transform these ideas into working software and get users to test the implementation to find problems. Formal methods have been shown to be beneficial in describing the functionality of systems, what we may call application logic, and underlying system behaviour. Informal techniques, however, have also been shown to be useful in the design of the user interface to systems. Given that both styles of development are beneficial to different parts of the system we would like to be able to use both approaches in one integrated software development process. Their differences, however, make this a challenging objective. In this paper we describe models and techniques which allow us to incorporate informal design artefacts into a formal software development process.     

Partial user-supplied information and user modeling for improving QoS

The incorporation of user-supplied information has become mandatory for the improvement of QoS in network systems. There is the question about accommodation of new users of a service, given that information about former users of a service is available. In the present work, we followed two approaches to derive information about new users in the network design and control processes, where both are based on prototype generation for the answers of former users to a QoS related questionnaire. In the first approach, attempts were made to map user attributes to prototypes. The second approach used a mapping from partial answers to a prototype. As a result, the first approach appeared to be infeasible, while the second showed good results. In the resulting trade-off between number of prototypes and classification accuracy, it is possible, for example, with 8 prototypes for around 1000 users to predict the answers of new users by using only 30% of the answers of former users, while reducing accuracy by only 13% at the same time.     

Methods for defining user groups and user-adjusted information structures

A common problem in the design of information systems is how to structure the information in a way that is most useful to different groups of users. This paper describes some statistical methods for revealing the structure inherent in empirical data elicited from users. It is illustrated by the application of these methods to the design of some web pages giving information about the Universitat de Valencia. Three potential user groups were identified, administrative staff, teaching staff and students. The first analysis demonstrated that users within these three groups assign relatively homogeneous structures, but that the structures assigned by the three groups are not the same, and also, teaching and administrative staff were shown to be relatively similar and different from students. Second, the ideal information structures for each group were identified and validated against the original data. The methods described can be applied to any design situation where there is an existing user population that can be called on to provide data.     

Using dynamic user models in the recognition of the plans of the user

This paper is concerned with information-seeking dialogues in a restricted domain (we consider a consultation system for a Computer Science Department, delivering information about the various tasks that the users may want to perform: for example, how to access the library, get information about the courses of the Department, etc.) and presents a framework where a plan recognition and a user modeling component are integrated to cooperate in the task of identifying the user's plans and goals. The focus of the paper is centered on the techniques used for building the user model and exploiting it in the determination of the user's intentions. For this task, we use stereotypes and we propose some inference rules for expanding the user model by inferring the user's beliefs from both the sentences s/he utters and the information stored in the plan library of the system, that describes the actions in the domain. Moreover, we introduce some disambiguation rules that are applied to the information in the user model for restricting the set of ambiguous hypotheses on the user's plans and goals to the most plausible ones. This also simplifies a further clarification dialogue if it is necessary for a precise identification of the user's intentions.