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User Modeling and User-Adapted Interaction 23

Editors:Alfred Kobsa
Standard No:ISSN 0924-1868 (print) EISSN 1573-1391 (online)
Links:link.springer.com | Table of Contents
  1. UMUAI 2013-03 Volume 23 Issue 1
  2. UMUAI 2013-04 Volume 23 Issue 2
  3. UMUAI 2013-09 Volume 23 Issue 4
  4. UMUAI 2013-11 Volume 23 Issue 5

UMUAI 2013-03 Volume 23 Issue 1

Leveraging machine-learned detectors of systematic inquiry behavior to estimate and predict transfer of inquiry skill BIBAKFull-Text 1-39
  Michael A. Sao Pedro; Ryan S. J. de Baker
We present work toward automatically assessing and estimating science inquiry skills as middle school students engage in inquiry within a physical science microworld. Towards accomplishing this goal, we generated machine-learned models that can detect when students test their articulated hypotheses, design controlled experiments, and engage in planning behaviors using two inquiry support tools. Models were trained using labels generated through a new method of manually hand-coding log files, "text replay tagging". This approach led to detectors that can automatically and accurately identify these inquiry skills under student-level cross-validation. The resulting detectors can be applied at run-time to drive scaffolding intervention. They can also be leveraged to automatically score all practice attempts, rather than hand-classifying them, and build models of latent skill proficiency. As part of this work, we also compared two approaches for doing so, Bayesian Knowledge-Tracing and an averaging approach that assumes static inquiry skill level. These approaches were compared on their efficacy at predicting skill before a student engages in an inquiry activity, predicting performance on a paper-style multiple choice test of inquiry, and predicting performance on a transfer task requiring data collection skills. Overall, we found that both approaches were effective at estimating student skills within the environment. Additionally, the models' skill estimates were significant predictors of the two types of inquiry transfer tests.
Keywords: Scientific inquiry; Exploratory learning environment assessment; Skill prediction; Machine-learned models; Microworlds; Behavior detection; Designing and conducting experiments; Bayesian Knowledge-Tracing
A PLA-based privacy-enhancing user modeling framework and its evaluation BIBAKFull-Text 41-82
  Yang Wang; Alfred Kobsa
Reconciling personalization with privacy has been a continuing interest in user modeling research. This aim has computational, legal and behavioral/attitudinal ramifications. We present a dynamic privacy-enhancing user modeling framework that supports compliance with users' individual privacy preferences and with the privacy laws and regulations that apply to each user. The framework is based on a software product line architecture. It dynamically selects personalization methods during runtime that meet the current privacy constraints. Since dynamic architectural reconfiguration is typically resource-intensive, we conducted a performance evaluation with four implementations of our system that vary two factors. The results demonstrate that at least one implementation of our approach is technically feasible with comparatively modest additional resources, even for websites with the highest traffic today. To gauge user reactions to privacy controls that our framework enables, we also conducted a controlled experiment that allowed one group of users to specify privacy preferences and view the resulting effects on employed personalization methods. We found that users in this treatment group utilized this feature, deemed it useful, and had fewer privacy concerns as measured by higher disclosure of their personal data.
Keywords: User modeling; Privacy laws; Privacy preferences; Compliance; Product line architecture; Performance evaluation; User experiment; Disclosure behavior

UMUAI 2013-04 Volume 23 Issue 2

Personalization in Social Web-Based Systems

Preface to the Special Issue on Personalization in Social Web systems BIBFull-Text 83-87
  Peter Brusilovsky; David N. Chin
The evaluation of a social adaptive website for cultural events BIBAKFull-Text 89-137
  Cristina Gena; Federica Cena; Fabiana Vernero
In this paper, we present an evaluation of a social adaptive website in the domain of cultural events, iCITY DSA, which provides information about cultural resources and events that promote the cultural heritage in the city of Turin. Using this evaluation, our objective was to investigate the actual usage of a social adaptive website, in an effort to discover the real behavior of users, the unforeseen correlations among user actions and the consequent interactive behavior, the accuracy of both system and social recommendations and their impact on the users themselves, and the role of tagging in the user modeling process. The major contributions of the paper are manifold: insights into user interactions with social adaptive systems; guidelines for future designs; evaluation of the tagging activity and tag meanings in relation to the application domain and thus their impact on the representation of the user model; and a demonstration of how a combination and interplay of evaluation methodologies (e.g., quantitative and qualitative) can enhance our comprehension of evaluation data.
Keywords: Evaluation; Social adaptive systems; Tag-based user model; Cultural events; Social recommenders
A knowledge-tracing model of learning from a social tagging system BIBAKFull-Text 139-168
  Peter Pirolli; Sanjay Kairam
We propose a user model to support personalized learning paths through online material. Our approach is a variant of student modeling using the computer tutoring concept of knowledge tracing. Knowledge tracing involves representing the knowledge required to master a domain, and, from traces of online user behavior, diagnosing user knowledge states as a profile over those elements. The user model is induced from documents tagged by an expert in a social tagging system. Tags identified with "expertise" in a domain can be used to identify a corpus of domain documents. That corpus can be fed to an automated process that distills a topic model representation characteristic of the domain. As a learner navigates and reads online material, inferences can be made about the degree to which topics in the target domain have been learned. We validate this knowledge tracing approach against data from a social tagging study. As part of this evaluation, we match the predictions of the knowledge-tracing model to individual participant responses made to individual question items used to test domain knowledge.
Keywords: Cognitive models; User models; Latent Dirichlet allocation; LDA; Topic models; SparTag.us; Social tagging; Social web
Cross-system user modeling and personalization on the Social Web BIBAKFull-Text 169-209
  Fabian Abel; Eelco Herder; Geert-Jan Houben
In order to adapt functionality to their individual users, systems need information about these users. The Social Web provides opportunities to gather user data from outside the system itself. Aggregated user data may be useful to address cold-start problems as well as sparse user profiles, but this depends on the nature of individual user profiles distributed on the Social Web. For example, does it make sense to re-use Flickr profiles to recommend bookmarks in Delicious? In this article, we study distributed form-based and tag-based user profiles, based on a large dataset aggregated from the Social Web. We analyze the completeness, consistency and replication of form-based profiles, which users explicitly create by filling out forms at Social Web systems such as Twitter, Facebook and LinkedIn. We also investigate tag-based profiles, which result from social tagging activities in systems such as Flickr, Delicious and StumbleUpon: to what extent do tag-based profiles overlap between different systems, what are the benefits of aggregating tag-based profiles. Based on these insights, we developed and evaluated the performance of several cross-system user modeling strategies in the context of recommender systems. The evaluation results show that the proposed methods solve the cold-start problem and improve recommendation quality significantly, even beyond the cold-start.
Keywords: User modeling; Personalization; Social Web; User profiles; Social tagging; Cross-system user modeling
Facebook single and cross domain data for recommendation systems BIBAKFull-Text 211-247
  Bracha Shapira; Lior Rokach
The emergence of social networks and the vast amount of data that they contain about their users make them a valuable source for personal information about users for recommender systems. In this paper we investigate the feasibility and effectiveness of utilizing existing available data from social networks for the recommendation process, specifically from Facebook. The data may replace or enrich explicit user ratings. We extract from Facebook content published by users on their personal pages about their favorite items and preferences in the domain of recommendation, and data about preferences related to other domains to allow cross-domain recommendation. We study several methods for integrating Facebook data with the recommendation process and compare the performance of these methods with that of traditional collaborative filtering that utilizes user ratings. In a field study that we conducted, recommendations obtained using Facebook data were tested and compared for 95 subjects and their crawled Facebook friends. Encouraging results show that when data is sparse or not available for a new user, recommendation results relying solely on Facebook data are at least equally as accurate as results obtained from user ratings. The experimental study also indicates that enriching sparse rating data by adding Facebook data can significantly improve results. Moreover, our findings highlight the benefits of utilizing cross domain Facebook data to achieve improvement in recommendation performance.
Keywords: Recommender systems; Facebook; Collaborative filtering; Cross-Domain recommendations; Evaluation
Exploring social tagging for personalized community recommendations BIBAKFull-Text 249-285
  Heung-Nam Kim; Abdulmotaleb El Saddik
Users of social Web sites actively create and join communities as a way to collectively share their media content and rich experience with diverse groups of people. In this study we focus on the issue of recommending social communities (or groups) to individual users. We address specifically the potential of social tagging for accentuating users' interests and characterizing communities. We also discuss some unique methods of improving several techniques that have been adapted for use in the context of community recommendations: collaborative filtering, a random walk model, a Katz influence model, a latent semantic model, and a user-centric tag model. We effectively incorporate social tagging information in each algorithm. We present empirical evaluations using real datasets from CiteULike and Last.fm. Our experimental results demonstrate that the different algorithms incorporated with social tagging offer significant advantages in improving both the recommendation quality and coverage, and demonstrate their feasibility for community recommendations in dealing with sparsity-related limitations.
Keywords: Community recommendations; Collaborative filtering; Graph-based recommendation; Latent semantic analysis; Recommender systems; Social tagging
Adaptive notifications to support knowledge sharing in close-knit virtual communities BIBAKFull-Text 287-343
  Styliani Kleanthous Loizou; Vania Dimitrova
Social web-groups where people with common interests and goals communicate, share resources, and construct knowledge, are becoming a major part of today's organisational practice. Research has shown that appropriate support for effective knowledge sharing tailored to the needs of the community is paramount. This brings a new challenge to user modelling and adaptation, which requires new techniques for gaining sufficient understanding of a virtual community (VC) and identifying areas where the community may need support. The research presented here addresses this challenge presenting a novel computational approach for community-tailored support underpinned by organisational psychology and aimed at facilitating the functioning of the community as a whole (i.e. as an entity). A framework describing how key community processes -- transactive memory (TM), shared mental models (SMMs), and cognitive centrality (CCen) -- can be utilised to derive knowledge sharing patterns from community log data is described. The framework includes two parts: (i) extraction of a community model that represents the community based on the key processes identified and (ii) identification of knowledge sharing behaviour patterns that are used to generate adaptive notifications. Although the notifications target individual members, they aim to influence individuals' behaviour in a way that can benefit the functioning of the community as a whole. A validation study has been performed to examine the effect of community-adapted notifications on individual members and on the community as a whole using a close-knit community of researchers sharing references. The study shows that notification messages can improve members' awareness and perception of how they relate to other members in the community. Interesting observations have been made about the linking between the physical and the VC, and how this may influence members' awareness and knowledge sharing behaviour. Broader implications for using log data to derive community models based on key community processes and generating community-adapted notifications are discussed.
Keywords: Community modelling; Adaptive support for knowledge sharing; Virtual communities

UMUAI 2013-09 Volume 23 Issue 4

Creating a model of the dynamics of socio-technical groups BIBAKFull-Text 345-379
  Sean P. Goggins; Giuseppe Valetto
Individuals participating in technologically mediated forms of organization often have difficulty recognizing when groups emerge, and how the groups they take part in evolve. This paper contributes an analytical framework that improves awareness of these virtual group dynamics through analysis of electronic trace data from tasks and interactions carried out by individuals in systems not explicitly designed for context adaptivity, user modeling or user personalization. We discuss two distinct cases to which we have applied our analytical framework. These two cases provide a useful contrast of two prevalent ways for analyzing social relations starting from electronic trace data: either artifact-mediated or direct person-to-person interactions. Our case study integrates electronic trace data analysis with analysis of other, triangulating data specific to each application. We show how our techniques fit in a general model of group informatics, which can serve to construct group context, and be leveraged by future tool development aimed at augmenting context adaptivity with group context and a social dimension. We describe our methods, data management strategies and technical architecture to support the analysis of individual user task context, increased awareness of group membership, and an integrated view of social, information and coordination contexts.
Keywords: Activity awareness; Group awareness; Virtual groups; Communities of practice; Networks of practice; Task context
Personalised Information Retrieval: survey and classification BIBAKFull-Text 381-443
  M. Rami Ghorab; Dong Zhou; Alexander O'Connor
Information Retrieval (IR) systems assist users in finding information from the myriad of information resources available on the Web. A traditional characteristic of IR systems is that if different users submit the same query, the system would yield the same list of results, regardless of the user. Personalised Information Retrieval (PIR) systems take a step further to better satisfy the user's specific information needs by providing search results that are not only of relevance to the query but are also of particular relevance to the user who submitted the query. PIR has thereby attracted increasing research and commercial attention as information portals aim at achieving user loyalty by improving their performance in terms of effectiveness and user satisfaction. In order to provide a personalised service, a PIR system maintains information about the users and the history of their interactions with the system. This information is then used to adapt the users' queries or the results so that information that is more relevant to the users is retrieved and presented. This survey paper features a critical review of PIR systems, with a focus on personalised search. The survey provides an insight into the stages involved in building and evaluating PIR systems, namely: information gathering, information representation, personalisation execution, and system evaluation. Moreover, the survey provides an analysis of PIR systems with respect to the scope of personalisation addressed. The survey proposes a classification of PIR systems into three scopes: individualised systems, community-based systems, and aggregate-level systems. Based on the conducted survey, the paper concludes by highlighting challenges and future research directions in the field of PIR.
Keywords: Personalisation; User modelling; User interests; Information Retrieval; Multilingual Information Retrieval; Adaptive hypermedia; Search history; Query adaptation; Result adaptation; Evaluation; Survey
James Chen Annual Award for Best Journal Article BIBFull-Text 445

UMUAI 2013-11 Volume 23 Issue 5

Recommending people to people: the nature of reciprocal recommenders with a case study in online dating BIBAKFull-Text 447-488
  Luiz Pizzato; Tomasz Rej; Joshua Akehurst
People-to-people recommenders constitute an important class of recommender systems. Examples include online dating, where people have the common goal of finding a partner, and employment websites where one group of users needs to find a job (employer) and another group needs to find an employee. People-to-people recommenders differ from the traditional items-to-people recommenders as they must satisfy both parties; we call this type of recommender reciprocal. This article is the first to present a comprehensive view of this important recommender class. We first identify the characteristics of reciprocal recommenders and compare them with traditional recommenders, which are widely used in e-commerce websites. We then present a series of studies and evaluations of a content-based reciprocal recommender in the domain of online dating. It uses a large dataset from a major online dating website. We use this case study to illustrate the distinctive requirements of reciprocal recommenders and highlight important challenges, such as the need to avoid bad recommendations since they may make users to feel rejected. Our experiments indicate that, by considering reciprocity, the rate of successful connections can be significantly improved. They also show that, despite the existence of rich explicit profiles, the use of implicit profiles provides more effective recommendations. We conclude with a discussion, linking our work in online dating to the many other domains that require reciprocal recommenders. Our key contributions are the recognition of the reciprocal recommender as an important class of recommender, the identification of its distinctive characteristics and the exploration of how these impact the recommendation process in an extensive case study in the domain of online dating.
Keywords: Recommender systems; Online dating; Reciprocity
Real-time rule-based classification of player types in computer games BIBAKFull-Text 489-526
  Ben Cowley; Darryl Charles; Michaela Black
The power of using machine learning to improve or investigate the experience of play is only beginning to be realised. For instance, the experience of play is a psychological phenomenon, yet common psychological concepts such as the typology of temperaments have not been widely utilised in game design or research. An effective player typology provides a model by which we can analyse player behaviour. We present a real-time classifier of player type, implemented in the test-bed game Pac-Man. Decision Tree algorithms CART and C5.0 were trained on labels from the DGD player typology (Bateman and Boon, 21st century game design, vol. 1, 2005). The classifier is then built by selecting rules from the Decision Trees using a rule- performance metric, and experimentally validated. We achieve 70% accuracy in this validation testing. We further analyse the concept descriptions learned by the Decision Trees. The algorithm output is examined with respect to a set of hypotheses on player behaviour. A set of open questions is then posed against the test data obtained from validation testing, to illustrate the further insights possible from extended analysis.
Keywords: Player typology; Player profiling; Computer games; Decision trees; Classification; Experimental validation