Cross-systems Personalisierung

The World Wide Web provides access to a wealth of information and services to a huge and heterogeneous user population on a global scale. One important and successful design mechanism in dealing with this diversity of users is to personalize Web sites and services, i.e. to customize system content, characteristics, or appearance with respect to a specific user. Each system independently builds up user profiles and uses this information to personalize the service offering. Such isolated approaches have two major drawbacks: firstly, investments of users in personalizing a system either through explicit provision of information or through long and regular use are not transferable to other systems. Secondly, users have little or no control over the information that defines their profile, since user data are deeply buried in personalization engines running on the server side. Cross system personalization (CSP) (Mehta, Niederee, & Stewart, 2005) allows for sharing information across different information systems in a user-centric way and can overcome the aforementioned problems. Information about users, which is originally scattered across multiple systems, is combined to obtain maximum leverage and reuse of information. Our initial approaches to cross system personalization relied on each user having a unified profile which different systems can understand. The unified profile contains facets modeling aspects of a multidimensional user which is stored inside a "Context Passport" that the user carries along in his/her journey across information space. The user’s Context Passport is presented to a system, which can then understand the context in which the user wants to use the system. The basis of ’understanding’ in this approach is of a semantic nature, i.e. the semantics of the facets and dimensions of the unified profile are known, so that the latter can be aligned with the profiles maintained internally at a specific site. The results of the personalization process are then transfered back to the user’s Context Passport via a protocol understood by both parties. The main challenge in this approach is to establish some common and globally accepted vocabulary and to create a standard every system will comply with. Machine Learning techniques provide an alternative approach to enable CSP without the need of accepted semantic standards or ontologies. The key idea is that one can try to learn dependencies between profiles maintained within one system and profiles maintained within a second system based on data provided by users who use both systems and who are willing to share their profiles across systems – which we assume is in the interest of the user. Here, instead of requiring a common semantic framework, it is only required that a sufficient number of users cross between systems and that there is enough regularity among users that one can learn within a user population, a fact that is commonly exploited in collaborative filtering. In this thesis, we aim to provide a principled approach towards achieving cross system personalization. We describe both semantic and learning approaches, with a stronger emphasis on the learning approach. We also investigate the privacy and scalability aspects of CSP and provide solutions to these problems. Finally, we also explore in detail the aspect of robustness in recommender systems. We motivate several approaches for robustifying collaborative filtering and provide the best performing algorithm for detecting malicious attacks reported so far.Die Personalisierung von Software Systemen ist von stetig zunehmender Bedeutung, insbesondere im Zusammenhang mit Web-Applikationen wie Suchmaschinen, Community-Portalen oder Electronic Commerce Sites, die große, stark diversifizierte Nutzergruppen ansprechen. Da explizite Personalisierung typischerweise mit einem erheblichen zeitlichem Aufwand für den Nutzer verbunden ist, greift man in vielen Applikationen auf implizite Techniken zur automatischen Personalisierung zurück, insbesondere auf Empfehlungssysteme (Recommender Systems), die typischerweise Methoden wie das Collaborative oder Social Filtering verwenden. Während diese Verfahren keine explizite Erzeugung von Benutzerprofilen mittels Beantwortung von Fragen und explizitem Feedback erfordern, ist die Qualität der impliziten Personalisierung jedoch stark vom verfügbaren Datenvolumen, etwa Transaktions-, Query- oder Click-Logs, abhängig. Ist in diesem Sinne von einem Nutzer wenig bekannt, so können auch keine zuverlässigen persönlichen Anpassungen oder Empfehlungen vorgenommen werden. Die vorgelegte Dissertation behandelt die Frage, wie Personalisierung über Systemgrenzen hinweg („cross system“) ermöglicht und unterstützt werden kann, wobei hauptsächlich implizite Personalisierungstechniken, aber eingeschränkt auch explizite Methodiken wie der semantische Context Passport diskutiert werden. Damit behandelt die Dissertation eine wichtige Forschungs-frage von hoher praktischer Relevanz, die in der neueren wissenschaftlichen Literatur zu diesem Thema nur recht unvollständig und unbefriedigend gelöst wurde. Automatische Empfehlungssysteme unter Verwendung von Techniken des Social Filtering sind etwas seit Mitte der 90er Jahre mit dem Aufkommen der ersten E-Commerce Welle popularisiert orden, insbesondere durch Projekte wie Information Tapistery, Grouplens und Firefly. In den späten 90er Jahren und Anfang dieses Jahrzehnts lag der Hauptfokus der Forschungsliteratur dann auf verbesserten statistischen Verfahren und fortgeschrittenen Inferenz-Methodiken, mit deren Hilfe die impliziten Beobachtungen auf konkrete Anpassungs- oder Empfehlungsaktionen abgebildet werden können. In den letzten Jahren sind vor allem Fragen in den Vordergrund gerückt, wie Personalisierungssysteme besser auf die praktischen Anforderungen bestimmter Applikationen angepasst werden können, wobei es insbesondere um eine geeignete Anpassung und Erweiterung existierender Techniken geht. In diesem Rahmen stellt sich die vorgelegte Arbeit

Man vs machine – Detecting deception in online reviews

This study focused on three main research objectives: analyzing the methods used to identify deceptive online consumer reviews, evaluating insights provided by multi-method automated approaches based on individual and aggregated review data, and formulating a review interpretation framework for identifying deception. The theoretical framework is based on two critical deception-related models, information manipulation theory and self-presentation theory. The findings confirm the interchangeable characteristics of the various automated text analysis methods in drawing insights about review characteristics and underline their significant complementary aspects. An integrative multi-method model that approaches the data at the individual and aggregate level provides more complex insights regarding the quantity and quality of review information, sentiment, cues about its relevance and contextual information, perceptual aspects, and cognitive material

Trust-Based Rating Prediction and Malicious Profile Detection in Online Social Recommender Systems

Online social networks and recommender systems have become an effective channel for influencing millions of users by facilitating exchange and spread of information. This dissertation addresses multiple challenges that are faced by online social recommender systems such as: i) finding the extent of information spread; ii) predicting the rating of a product; and iii) detecting malicious profiles. Most of the research in this area do not capture the social interactions and rely on empirical or statistical approaches without considering the temporal aspects. We capture the temporal spread of information using a probabilistic model and use non-linear differential equations to model the diffusion process. To predict the rating of a product, we propose a social trust model and use the matrix factorization method to estimate user\u27s taste by incorporating user-item rating matrix. The effect of tastes of friends of a user is captured using a trust model which is based on similarities between users and their centralities. Similarity is modeled using Vector Space Similarity and Pearson Correlation Coefficient algorithms, whereas degree, eigen-vector, Katz, and PageRank are used to model centrality. As rating of a product has tremendous influence on its saleability, social recommender systems are vulnerable to profile injection attacks that affect user\u27s opinion towards favorable or unfavorable recommendations for a product. We propose a classification approach for detecting attackers based on attributes that provide the likelihood of a user profile of that of an attacker. To evaluate the performance, we inject push and nuke attacks, and use precision and recall to identify the attackers. All proposed models have been validated using datasets from Facebook, Epinions, and Digg. Results exhibit that the proposed models are able to better predict the information spread, rating of a product, and identify malicious user profiles with high accuracy and low false positives

Proceedings of the 18th Irish Conference on Artificial Intelligence and Cognitive Science

These proceedings contain the papers that were accepted for publication at AICS-2007, the 18th Annual Conference on Artificial Intelligence and Cognitive Science, which was held in the Technological University Dublin; Dublin, Ireland; on the 29th to the 31st August 2007. AICS is the annual conference of the Artificial Intelligence Association of Ireland (AIAI)

On Pecuniary Resiliency, Early Warning, and Market Imitation Under Unrestricted Warfare

This study extends established financial market approaches to account for key econophysical attributes, low probability/high impact events, and a market\u27s potential use as early warning for threats. Any disparity between established financial practices and true market conditions may provide incentive for exploitation and may harm national security objectives and interests through cascading effects. These national security concerns may include, in particular, the health of a reserve currency for those countries whose currency serves as one. This is a preferred approach with Unrestricted Warfare-type operations as these techniques may not enable repudiation of the antagonist. Since this approach may remain a strong incentive for such tactics for the foreseeable future, it is imperative to develop techniques that hedge against financial miscalculations and subversive efforts. This research relaxes key assumptions of standard finance theory and applies these approaches to currency dynamics and portfolio selection which provides insight on areas of vulnerability. Early warning measures of threats are developed and compared to critical world events. Vulnerabilities to capital markets are studied, and their effects on reserve currencies are also analyzed. Lastly, a mathematical framework is developed that enables imitation of the aforementioned econophysical attributes in a simulated environment thereby bridging the divide between certain aspects of standard finance theory and econophysics for future study

Discovering and Mitigating Social Data Bias

abstract: Exabytes of data are created online every day. This deluge of data is no more apparent than it is on social media. Naturally, finding ways to leverage this unprecedented source of human information is an active area of research. Social media platforms have become laboratories for conducting experiments about people at scales thought unimaginable only a few years ago. Researchers and practitioners use social media to extract actionable patterns such as where aid should be distributed in a crisis. However, the validity of these patterns relies on having a representative dataset. As this dissertation shows, the data collected from social media is seldom representative of the activity of the site itself, and less so of human activity. This means that the results of many studies are limited by the quality of data they collect. The finding that social media data is biased inspires the main challenge addressed by this thesis. I introduce three sets of methodologies to correct for bias. First, I design methods to deal with data collection bias. I offer a methodology which can find bias within a social media dataset. This methodology works by comparing the collected data with other sources to find bias in a stream. The dissertation also outlines a data collection strategy which minimizes the amount of bias that will appear in a given dataset. It introduces a crawling strategy which mitigates the amount of bias in the resulting dataset. Second, I introduce a methodology to identify bots and shills within a social media dataset. This directly addresses the concern that the users of a social media site are not representative. Applying these methodologies allows the population under study on a social media site to better match that of the real world. Finally, the dissertation discusses perceptual biases, explains how they affect analysis, and introduces computational approaches to mitigate them. The results of the dissertation allow for the discovery and removal of different levels of bias within a social media dataset. This has important implications for social media mining, namely that the behavioral patterns and insights extracted from social media will be more representative of the populations under study.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Addressing the Issues of Coalitions and Collusion in Multiagent Systems

In the field of multiagent systems, trust and reputation systems are intended to assist agents in finding trustworthy partners with whom to interact. Earlier work of ours identified in theory a number of security vulnerabilities in trust and reputation systems, weaknesses that might be exploited by malicious agents to bypass the protections offered by such systems. In this work, we begin by developing the TREET testbed, a simulation platform that allows for extensive evaluation and flexible experimentation with trust and reputation technologies. We use this testbed to experimentally validate the practicality and gravity of attacks against vulnerabilities. Of particular interest are attacks that are collusive in nature: groups of agents (coalitions) working together to improve their expected rewards. But the issue of coalitions is not unique to trust and reputation; rather, it cuts across a range of fields in multiagent systems and beyond. In some scenarios, coalitions may be unwanted or forbidden; in others they may be benign or even desirable. In this document, we propose a method for detecting coalitions and identifying coalition members, a capability that is likely to be valuable in many of the diverse fields where coalitions may be of interest. Our method makes use of clustering in benefit space (a high-dimensional space reflecting how agents benefit others in the system) in order to identify groups of agents who benefit similar sets of agents. A statistical technique is then used to identify which clusters contain coalitions. Experimentation using the TREET platform verifies the effectiveness of this approach. A series of enhancements to our method are also introduced, which improve the accuracy and robustness of the algorithm. To demonstrate how this broadly-applicable tool can be used to address domain-specific problems, we focus again on trust and reputation systems. We show how, by incorporating our work into one such system (the existing Beta Reputation System), we can provide resistance to collusion. We conclude with a detailed discussion of the value of our work for a wide range of environments, including a variety of multiagent systems and real-world settings

Using Data Mining for Facilitating User Contributions in the Social Semantic Web

This thesis utilizes recommender systems to aid the user in contributing to the Social Semantic Web. In this work, we propose a framework that maps domain properties to recommendation technologies. Next, we develop novel recommendation algorithms for improving personalized tag recommendation and for recommendation of semantic relations. Finally, we introduce a framework to analyze different types of potential attacks against social tagging systems and evaluate their impact on those systems