Jill Freyne

Exploiting Query Repetition and Regularity in an Adaptive Community-Based Web Search Engine

Search engines continue to struggle with the challenges presented by Web search: vague queries, impatient users and an enormous and rapidly expanding collection of unmoderated, heterogeneous documents all make for an extremely hostile search environment. In this paper we argue that conventional approaches to Web search -- those that adopt a traditional, document-centric, information retrieval perspective -- are limited by their refusal to consider the past search behaviour of users during future search sessions. In particular, we argue that in many circumstances the search behaviour of users is repetitive and regular; the same sort of queries tend to recur and the same type of results are often selected. We describe how this observation can lead to a novel approach to a more adaptive form of search, one that leverages past search behaviours as a means to re-rank future search results in a way that recognises the implicit preferences of communities of searchers. We describe and evaluate the I-SPY search engine, which implements this approach to collaborative, community-based search. We show that it offers potential improvements in search performance, especially in certain situations where communities of searchers share similar information needs and use similar queries to express these needs. We also show that I-SPY benefits from important advantages when it comes to user privacy. In short, we argue that I-SPY strikes a useful balance between search personalization and user privacy, by offering a unique form of anonymous personalization, and in doing so may very well provide privacy-conscious Web users with an acceptable approach to personalized search.

Further Experiments on Collaborative Ranking in Community-Based Web Search

As the search engine arms-race continues, search engines are constantly looking for ways to improve the manner in which they respond to user queries. Given the vagueness of Web search queries, recent research has focused on ways to introduce context into the search process as a means of clarifying vague, under-specified or ambiguous query terms. In this paper we describe a novel approach to using context in Web search that seeks to personalize the results of a generic search engine for the needs of a specialist community of users. In particular we describe two separate evaluations in detail that demonstrate how the collaborative search method has the potential to deliver significant search performance benefits to end-users while avoiding many of the privacy and security concerns that are commonly associated with related personalization research.

Intelligent food planning: personalized recipe recommendation

As the obesity epidemic takes hold across the world many medical professionals are referring users to online systems aimed at educating and persuading users to alter their lifestyle. The challenge for many of these systems is to increase initial adoption and sustain participation for sufficient time to have real impact on the life of its users. In this work we present some preliminary investigation into the design of a recipe recommender, aimed at educating and sustaining user participation, which makes tailored recommendations of healthy recipes. We concentrate on the two initial dimensions of food recommendations: data capture and food-recipe relationships and present a study into the suitability of varying recommender algorithms for the recommendation of recipes.

Physical activity motivating games: virtual rewards for real activity

Contemporary lifestyle has become increasingly sedentary: little physical (sports, exercises) and much sedentary (TV, computers) activity. The nature of sedentary activity is self-reinforcing, such that increasing physical and decreasing sedentary activity is difficult. We present a novel approach aimed at combating this problem in the context of computer games. Rather than explicitly changing the amount of physical and sedentary activity a person sets out to perform, we propose a new game design that leverages user engagement to generate out of game motivation to perform physical activity while playing. In our design, players gain virtual game rewards in return for real physical activity performed. Here we present and evaluate an application of our design to the game Neverball. We adapted Neverball by reducing the time allocated to accomplish the game tasks and motivated players to perform physical activity by offering time based rewards. An empirical evaluation involving 180 participants shows that the participants performed more physical activity, decreased the amount of sedentary playing time, and did not report a decrease in perceived enjoyment of playing the activity motivating version of Neverball.

Increasing engagement through early recommender intervention

Social network sites rely on the contributions of their members to create a lively and enjoyable space. Recent research has focused on using personalization and recommender technologies to encourage participation of existing members. In this work we present an early-intervention approach to encouraging participation and engagement, which makes recommendations to new users during their sign-up process. Our recommender system exploits external social media to produce people and profile entry recommendations for new users. We present results of a live user study, showing that users who received recommendations at sign-up created more social connections, contributed more content, and were on the whole more engaged with the system, contributing more without prompt and returning more often. We further show that recommendations for multiple content types yield significantly better results, in terms of user contribution and consumption; and that recommendations of more active users yield a higher return rate.

Relative status of journal and conference publications in computer science

Though computer scientists agree that conference publications enjoy greater status in computer science than in other disciplines, there is little quantitative evidence to support this view. The importance of journal publication in academic promotion makes it a highly personal issue, since focusing exclusively on journal papers misses many significant papers published by CS conferences. Here, we aim to quantify the relative importance of CS journal and conference papers, showing that CS papers in leading conferences match the impact of papers in mid-ranking journals and surpass the impact of papers in journals in the bottom half of the Thompson Reuters rankings (http://www.isiknowledge.com) for impact measured in terms of citations in Google Scholar. We also show that poor correlation between this measure and conference acceptance rates indicates conference publication is an inefficient market where venues equally challenging in terms of rejection rates offer quite different returns in terms of citations. How to measure the quality of academic research and performance of particular researchers has always involved debate. Many CS researchers feel that performance assessment is an exercise in futility, in part because academic research cannot be boiled down to a set of simple performance metrics, and any attempt to introduce them would expose the entire research enterprise to manipulation and gaming. On the other hand, many researchers want some reasonable way to evaluate academic performance, arguing that even an imperfect system sheds light on research quality, helping funding agencies and tenure committees make more informed decisions. One long-standing way of evaluating academic performance is through publication output. Best practice for academics is to write key research contributions as scholarly articles for submission to relevant journals and conferences; the peer-review model has stood the test of time in determining the quality of accepted articles. However, todays culture of academic publication accommodates a range of publication opportunities yielding a continuum of quality, with a significant gap between the lower and upper reaches of the continuum; for example, journal papers are routinely viewed as superior to conference papers, which are generally considered superior to papers at workshops and local symposia. Several techniques are used for evaluating publications and publication outlets, mostly targeting journals. For example, Thompson Reuters (the Institute for Scientific Information) and other such organizations record and assess the number of citations accumulated by leading journals (and some high-ranking conferences) in the ISI Web of Knowledge (http://www.isiknowledge.com) to compute the impact factor of a journal as a measure of its ability to attract citations. Less-reliable indicators of publication quality are also available for judging conference quality; for example, a conferences rejection rate is often cited as a quality indicator on the grounds that a high rejection rate means a more selective review process able to generate higher-quality papers. However, as the devil is in the details, the details in this case vary among academic disciplines and subdisciplines. Here, we examine the issue of publication quality from a CS/engineering perspective, describing how related publication practices differ from those of other disciplines, in that CS/engineering research is mainly published in conferences rather than in journals. This culture presents an important challenge when evaluating CS research because traditional impact metrics are better suited to evaluating journal rather than conference publications. In order to legitimize the role of conference papers to the wider scientific community, we offer an impact measure based on an analysis of Google Scholar citation data suited to CS conferences. We validate this new measure with a large-scale experiment covering 8,764 conference and journal papers to demonstrate a strong correlation between traditional journal impact and our new citation score. The results highlight how leading conferences compare favorably to mid-ranking journals, surpassing the impact of journals in the bottom half of the traditional ISI Web of Knowledge ranking. We also discuss a number of interesting anomalies in the CS conference circuit, highlighting how conferences with similar rejection rates (the traditional way of evaluating conferences) can attract quite different citation counts. We also note interesting geographical distinctions in this regard, particularly with respect to European and U.S. conferences.

Influencing Individually: Fusing Personalization and Persuasion

Personalized technologies aim to enhance user experience by taking into account users’ interests, preferences, and other relevant information. Persuasive technologies aim to modify user attitudes, intentions, or behavior through computer-human dialogue and social influence. While both personalized and persuasive technologies influence user interaction and behavior, we posit that this influence could be significantly increased if the two technologies were combined to create personalized and persuasive systems. For example, the persuasive power of a one-size-fits-all persuasive intervention could be enhanced by considering the users being influenced and their susceptibility to the persuasion being offered. Likewise, personalized technologies could cash in on increased success, in terms of user satisfaction, revenue, and user experience, if their services used persuasive techniques. Hence, the coupling of personalization and persuasion has the potential to enhance the impact of both technologies. This new, developing area clearly offers mutual benefits to both research areas, as we illustrate in this special issue.

Features Predicting Weight Loss in Overweight or Obese Participants in a Web-Based Intervention: Randomized Trial

Background Obesity remains a serious issue in many countries. Web-based programs offer good potential for delivery of weight loss programs. Yet, many Internet-delivered weight loss studies include support from medical or nutritional experts, and relatively little is known about purely web-based weight loss programs. Objective To determine whether supportive features and personalization in a 12-week web-based lifestyle intervention with no in-person professional contact affect retention and weight loss. Methods We assessed the effect of different features of a web-based weight loss intervention using a 12-week repeated-measures randomized parallel design. We developed 7 sites representing 3 functional groups. A national mass media promotion was used to attract overweight/obese Australian adults (based on body mass index [BMI] calculated from self-reported heights and weights). Eligible respondents (n = 8112) were randomly allocated to one of 3 functional groups: information-based (n = 183), supportive (n = 3994), or personalized-supportive (n = 3935). Both supportive sites included tools, such as a weight tracker, meal planner, and social networking platform. The personalized-supportive site included a meal planner that offered recommendations that were personalized using an algorithm based on a user’s preferences for certain foods. Dietary and activity information were constant across sites, based on an existing and tested 12-week weight loss program (the Total Wellbeing Diet). Before and/or after the intervention, participants completed demographic (including self-reported weight), behavioral, and evaluation questionnaires online. Usage of the website and features was objectively recorded. All screening and data collection procedures were performed online with no face-to-face contact. Results Across all 3 groups, attrition was high at around 40% in the first week and 20% of the remaining participants each week. Retention was higher for the supportive sites compared to the information-based site only at week 12 (P = .01). The average number of days that each site was used varied significantly (P = .02) and was higher for the supportive site at 5.96 (SD 11.36) and personalized-supportive site at 5.50 (SD 10.35), relative to the information-based site at 3.43 (SD 4.28). In total, 435 participants provided a valid final weight at the 12-week follow-up. Intention-to-treat analyses (using multiple imputations) revealed that there were no statistically significant differences in weight loss between sites (P = .42). On average, participants lost 2.76% (SE 0.32%) of their initial body weight, with 23.7% (SE 3.7%) losing 5% or more of their initial weight. Within supportive conditions, the level of use of the online weight tracker was predictive of weight loss (model estimate = 0.34, P < .001). Age (model estimate = 0.04, P < .001) and initial BMI (model estimate = -0.03, P < .002) were associated with frequency of use of the weight tracker. Conclusions Relative to a static control, inclusion of social networking features and personalized meal planning recommendations in a web-based weight loss program did not demonstrate additive effects for user weight loss or retention. These features did, however, increase the average number of days that a user engaged with the system. For users of the supportive websites, greater use of the weight tracker tool was associated with greater weight loss.

Social networking feeds: recommending items of interest

The success of social media has resulted in an information overload problem, where users are faced with hundreds of new contributions, edits and communications at every visit. A prime example of this in social networks is the news or activity feeds, where the actions (friending, commenting, photo sharing, etc) of friends on the network are presented to users in order to inform them of the network activity. In this work we endeavour to reduce the burden on individuals of identifying interesting updates in social network news feeds by automatically identifying and recommending relevant items to individuals where item relevance is based on the observed interactions of the individual with the social network. The results of our offline study show that combining short term interest models, exploiting previous viewing behavior of users, and long-term models, exploiting previous viewing of network actions, was the best predictor of feed item relevance.

Physical Activity Motivating Games: Be Active and Get Your Own Reward

People’s daily lives have become increasingly sedentary, with extended periods of time being spent in front of a host of electronic screens for learning, work, and entertainment. We present research into the use of an adaptive persuasive technology, which introduces bursts of physical activity into a traditionally sedentary activity: computer game playing. Our game design approach leverages the playfulness and addictive nature of computer games to motivate players to engage in mild physical activity. The design allows players to gain virtual in-game rewards in return for performing real physical activity captured by sensory devices. This article presents a two-stage analysis of the activity-motivating game design approach applied to a prototype game. Initially, we detail the overall acceptance of active games discovered when trialing the technology with 135 young players. Results showed that players performed more activity without negatively affecting their perceived enjoyment of the playing experience. The analysis did discover, however, a lack of balance between the amounts of physical activity carried out by players with various gaming skills, which prompted a subsequent investigation into adaptive techniques for balancing the amount of physical activity performed by players. An evaluation of additional 90 players showed that adaptive techniques successfully overcame the gaming skills dependence and achieved more balanced activity levels. Overall, this work positions activity-motivating games as an approach that can potentially change the way players interact with computer games and lead to healthier lifestyles.