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BackgroundDifferent theoretical frameworks support the use of interactive websites to promote sexual health. Although several Web-based interventions have been developed to address sexual risk taking among young people, no evaluated interventions have attempted to foster behavior change through moderated interaction among a virtual network of adolescents (who remain anonymous to one another) and health professionals. ObjectiveThe objective was to conduct a summative process evaluation of TeensTalkHealth, an interactive sexual health website designed to promote condom use and other healthy decision making in the context of romantic and sexual relationships. MethodsEvaluation data were obtained from 147 adolescents who participated in a feasibility and acceptability study. Video vignettes, teen-friendly articles, and other content served as conversation catalysts between adolescents and health educators on message boards. ResultsAdolescents’ perceptions that the website encouraged condom use across a variety of relationship situations were very high. Almost 60% (54/92, 59%) of intervention participants completed two-thirds or more of requested tasks across the 4-month intervention. Adolescents reported high levels of comfort, perceived privacy, ease of website access and use, and perceived credibility of health educators. Potential strategies to enhance engagement and completion of intervention tasks during future implementations of TeensTalkHealth are discussed, including tailoring of content, periodic website chats with health educators and anonymous peers, and greater incorporation of features from popular social networking websites. ConclusionsTeensTalkHealth is a feasible, acceptable, and promising approach to complement and enhance existing services for youth.

The discovery of a person's meaningful places involves obtaining the physical locations and their labels for a person's places that matter to his daily life and routines. This problem is driven by the requirements from emerging location-aware applications, which allow a user to pose queries and obtain information in reference to places, for example, "home", "work" or "Northwest Health Club". It is a challenge to map from physical locations to personally meaningful places due to a lack of understanding of what constitutes the real users' personally meaningful places. Previous work has explored algorithms to discover personal places from location data. However, we know of no systematic empirical evaluations of these algorithms, leaving designers of location-aware applications in the dark about their choices. Our work remedies this situation. We extended a clustering algorithm to discover places. We also defined a set of essential evaluation metrics and an interactive evaluation framework. We then conducted a large-scale experiment that collected real users' location data and personally meaningful places, and illustrated the utility of our evaluation framework. Our results establish a baseline that future work can measure itself against. They also demonstrate that that our algorithm discovers places with reasonable accuracy and outperforms the well-known K-Means clustering algorithm for place discovery. Finally, we provide evidence that shapes more complex than "points" are required to represent the full range of people's everyday places. Categories and Subject Descriptors: H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval--Clustering; H.5.m [Information Interfaces and Presentation (e.g., HCI)]: Miscellaneous General Terms: Algorithms, Human Factors Additional Key Words and Phrases: Ubiquitous computing, location-aware applications, clustering algorithms, place discovery, field studies

Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated. Categories and Subject Descriptors: H.3.4 [Information Storage and Retrieval]: Systems and Software--performance Evaluation (efficiency and effectiveness) General Terms: Experimentation, Measurement, Performance Additional Key Words and Phrases: Collaborative filtering, recommender systems, metrics, evaluation

From the earliest days of the field, Recommender Systems research and practice has struggled to balance and integrate approaches that focus on recommendation as a machine learning or missing-value problem with ones that focus on machine learning as a discovery tool and perhaps persuasion platform. In this article, we review 25 years of recommender systems research from a human-centered perspective, looking at the interface and algorithm studies that advanced our understanding of how system designs can be tailored to users objectives and needs. At the same time, we show how external factors, including commercialization and technology developments, have shaped research on human-centered recommender systems. We show how several unifying frameworks have helped developers and researchers alike incorporate thinking about user experience and human decision-making into their designs. We then review the challenges, and the opportunities, in today's recommenders, looking at how deep learning and optimization techniques can integrate with both interface designs and human performance statistics to improve recommender effectiveness and usefulness. [ABSTRACT FROM AUTHOR]

But a larger question is the relationship of this definition to the initial characterizations in the essay: would a system that satisfies this definition have "goals and intentions" and be "full[y] independent"? I think - and I thought Hancock did too - that the important question is: I what actions can we take to avoid "adverse" consequences if and when "autonomous agents" do arrive? Acknowledging that they could cause harm - HAL, Skynet, or "just" unforeseen consequences like climate change. [Extracted from the article]

Objective: Prostate cancer is the most common invasive cancer in gay and bisexual men (GBM). Despite the unique sexual and urinary concerns of this group, studies of prostate cancer rehabilitation have primarily focused on heterosexual men. GBM also have high prevalence of human immunodeficiency virus (HIV), which may be associated with lower health-related quality of life (HRQOL). We examined the association between HIV status and HRQOL in a cohort of GBM with prostate cancer.Methods: Data from the Restore study, a cross-sectional online survey of GBM treated for prostate cancer, were used to examine this association. The Expanded Prostate Cancer Index Composite (EPIC) assessed function, bother, and summary measures in four domains: urinary, sexual, bowel, and hormone. Overall physical and mental HRQOL was assessed using the Short-Form Health Survey (SF-12). Multivariate analysis of variance and linear regression were used to evaluate the association between HIV status and HRQOL scores after adjustment for demographic and sexual characteristics.Results: Of 192 participants, 24 (12.4%) reported an HIV diagnosis. After adjustment for covariates, HIV-positive status was associated with lower scores on the EPIC urinary (mean difference [MD]: -13.0, 95% CI, -21.4 to -4.6), sexual (MD: -12.5, 95% CI, -21.9 to -3.2), and bowel (MD: -5.9, 95% CI, -11.7 to -0.2) domains. No significant associations were observed between HIV status and other outcomes.Conclusions: HIV status may be associated with poorer urinary, sexual, and bowel HRQOL in GBM prostate cancer survivors. [ABSTRACT FROM AUTHOR]

Despite the geographically situated nature of most sharing economy tasks, little attention has been paid to the role that geography plays in the sharing economy. In this article, we help to address this gap in the literature by examining how four key principles from human geography--distance decay, structured variation in population density, mental maps, and "the Big Sort" (spatial homophily)--manifest in sharing economy platforms. We find that these principles interact with platform design decisions to create systemic biases in which the sharing economy is significantly more effective in dense, high socioeconomic status (SES) areas than in low-SES areas and the suburbs. We further show that these results are robust across two sharing economy platforms: UberX and TaskRabbit. In addition to highlighting systemic sharing economy biases, this article more fundamentally demonstrates the importance of considering well-known geographic principles when designing and studying sharing economy platforms. [ABSTRACT FROM AUTHOR]

Social matching systems bring people together in both physical and online spaces. They have the potential to increase social interaction and foster collaboration. However, social matching systems lack a clear intellectual foundation: the nature of the design space, the key research challenges, and the roster of appropriate methods are all ill-defined. This article begins to remedy the situation. It clarifies the scope of social matching systems by distinguishing them from other recommender systems and related systems and techniques. It identifies a set of issues that characterize the design space of social matching systems and shows how existing systems explore different points within the design space. It also reviews selected social science results that can provide input into system design. Most important, the article presents a research agenda organized around a set of claims. The claims embody our understanding of what issues are most important to investigate, our beliefs about what is most likely to be true, and our suggestions of specific research directions to pursue. [ABSTRACT FROM AUTHOR]

Modem work is a highly social process, offering many cues for people to organize communication and access information. Shared physical workplaces provide natural support for tasks such as (a) social reminding about communication commitments and keeping track of collaborators and friends, and (b) social data mining of local expertise for advice and information. However, many people now collaborate remotely using tools such as email and voicemail. Our field studies show that these tools do not provide the social cues needed for group work processes. In part, this is be- cause the tools are organized around messages, rather than people. In response to this problem, we created ContactMap, a system that makes people the primary unit of interaction. ContactMap provides a structured social desktop representation of users' important contacts that directly supports social reminding and social data mining. We conducted an empirical evaluation of ContactMap, comparing it with traditional email systems, on tasks suggested by our fieldwork. Users performed better with ContactMap and preferred ContactMap for the majority of these tasks. We discuss future enhancements of our system and the implications of these results for future communication interfaces and for theories of mediated communication. [ABSTRACT FROM AUTHOR]

Reports on TopicShop, a system that mines information from the structure and content of Web pages and provides an exploratory information workspace interface. Social data mining; Web data mining ; Improvement of user task performance; Prediction of human quality judgments.

We identify a problem with the process of research in the human-computer interaction (HCI) community-an overemphasis on "radical invention" at the price of achieving a common research focus. Without such a focus, it is difficult to build on previous work, to compare different interaction techniques objectively, and to make progress in developing theory. These problems at the research level have implications for practice, too; as researchers we often are unable to give principled design advice to builders of new systems. We propose that the HCI community try to achieve a common focus around the notion of reference tasks. We offer arguments for the advantages of this approach as well as consider potential difficulties. We explain how reference tasks have been highly effective in focusing research into information retrieval and speech recognition. We discuss what factors have to be considered in selecting HCI reference tasks and present an example reference task (for searching speech archives). This example illustrates the nature of reference tasks and points to the issues and problems involved in constructing and using them. We conclude with recommendations about what steps need to be taken to execute the reference task research agenda. This involves recommendations about both the technical research that needs to be done and changes in the way that the HCI research community operates. The technical research involves identification of important user tasks by systematic requirements gathering, definition and operationalization of reference tasks and evaluation metrics, and execution of task-based evaluation, along with judicious use of field trials. Perhaps more important, we have also suggested changes in community practice that HCI must adopt to make the reference tasks idea work. We must create forums for discussion of common tasks and methods by which people can compare systems and techniques. Only by doing this can the notion of reference tasks be integrated into the process of research and development, enabling the field to achieve the focus it desperately needs. [ABSTRACT FROM AUTHOR]

Focuses on the identification of the type of software design knowledge. Development of a framework on a living design memory; Improvement on the effectiveness of software; Importance of building knowledge management systems.

Reengineering organizational processes is an imperative in today's business climate, and technology can play an important role in understanding, managing and reengineering business processes. In particular, what we call 'knowledge management tools' can be used to streamline process descriptions and deliver organizational knowledge in a timely and appropriate manner. These tools can also facilitate process reengineering by structuring previously informal work artifacts, enabling artifact reuse and promoting more effective organizational collaboration. This paper discusses two research and development collaborations that resulted in deployed knowledge management tools for a large business unit of AT&T, and discusses the importance of managing expectations, the need for existing process maturity, and the issue of knowledge maintenance in technology-driven business process reengineering. [ABSTRACT FROM AUTHOR]

Administrators of online communities face the crucial issue of understanding and developing their user communities. Will new users become committed members? What types of roles are particular individuals most likely to take on? We report on a study that investigates these questions. We administered a survey (based on standard psychological instruments) to nearly 4000 new users of the MovieLens film recommendation community from October 2009 to March 2010 and logged their usage history on MovieLens. We found that general volunteer motivations, pro-social behavioral history, and community-specific motivations predicted both the amount of use and specific types of activities users engaged in after joining the community. These findings have implications for the design and management of online communities.