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

Revisiting Adversarially Learned Injection Attacks Against Recommender Systems

Recommender systems play an important role in modern information and e-commerce applications. While increasing research is dedicated to improving the relevance and diversity of the recommendations, the potential risks of state-of-the-art recommendation models are under-explored, that is, these models could be subject to attacks from malicious third parties, through injecting fake user interactions to achieve their purposes. This paper revisits the adversarially-learned injection attack problem, where the injected fake user `behaviors' are learned locally by the attackers with their own model -- one that is potentially different from the model under attack, but shares similar properties to allow attack transfer. We found that most existing works in literature suffer from two major limitations: (1) they do not solve the optimization problem precisely, making the attack less harmful than it could be, (2) they assume perfect knowledge for the attack, causing the lack of understanding for realistic attack capabilities. We demonstrate that the exact solution for generating fake users as an optimization problem could lead to a much larger impact. Our experiments on a real-world dataset reveal important properties of the attack, including attack transferability and its limitations. These findings can inspire useful defensive methods against this possible existing attack.Comment: Accepted at Recsys 2

Cholate-interspersed porphyrin-anthraquinone conjugates: photonuclease activity of large sized, 'tweezer-like' molecules

In a new approach towards the development of a 'dual-wavelength dual-mechanism' type of photosensitizer for use in photodynamic therapy (PDT), covalently linked bichromophoric systems comprising of porphyrin (P) and anthraquinone (AnQ) subunits have been synthesized and fully characterized by FAB-MS, IR, UV-Visible and 1H NMR methods. The porphyrin donor and the anthraquinone acceptor subunits of these mono- or bis-intercalating hybrid molecules are interspersed with either cholate or polymethylene spacers. There exists minimal ground- and singlet-state interaction between the porphyrin and anthraquinone subunits in the giant-sized, cholate-interspersed P-AnQ systems as revealed by a comparison of their spectroscopic and electrochemical properties with those of the corresponding individual reference compounds. On the other hand, quenching of fluorescence observed for the P-AnQ systems endowed with polymethylene spacers has been interpreted in terms of a possible intramolecular electron transfer between the singlet porphyrin and the anthraquinone acceptor. When excited into their porphyrin absorption band maxima, each new P-AnQ system could generate singlet molecular oxygen in good-to-moderate yield. Wavelength-dependent photonuclease activity of these new bis-intercalating species has been examined

On the Existence of Competitive Equilibrium with Chores

We study the chore division problem in the classic Arrow-Debreu exchange setting, where a set of agents want to divide their divisible chores (bads) to minimize their disutilities (costs). We assume that agents have linear disutility functions. Like the setting with goods, a division based on competitive equilibrium is regarded as one of the best mechanisms for bads. Equilibrium existence for goods has been extensively studied, resulting in a simple, polynomial-time verifiable, necessary and sufficient condition. However, dividing bads has not received a similar extensive study even though it is as relevant as dividing goods in day-to-day life. In this paper, we show that the problem of checking whether an equilibrium exists in chore division is NP-complete, which is in sharp contrast to the case of goods. Further, we derive a simple, polynomial-time verifiable, sufficient condition for existence. Our fixed-point formulation to show existence makes novel use of both Kakutani and Brouwer fixed-point theorems, the latter nested inside the former, to avoid the undefined demand issue specific to bads

Fairness in Federated Learning via Core-Stability

Federated learning provides an effective paradigm to jointly optimize a model benefited from rich distributed data while protecting data privacy. Nonetheless, the heterogeneity nature of distributed data makes it challenging to define and ensure fairness among local agents. For instance, it is intuitively "unfair" for agents with data of high quality to sacrifice their performance due to other agents with low quality data. Currently popular egalitarian and weighted equity-based fairness measures suffer from the aforementioned pitfall. In this work, we aim to formally represent this problem and address these fairness issues using concepts from co-operative game theory and social choice theory. We model the task of learning a shared predictor in the federated setting as a fair public decision making problem, and then define the notion of core-stable fairness: Given $N$ agents, there is no subset of agents $S$ that can benefit significantly by forming a coalition among themselves based on their utilities $U_N$ and $U_S$ (i.e., $\frac{|S|}{N} U_S \geq U_N$). Core-stable predictors are robust to low quality local data from some agents, and additionally they satisfy Proportionality and Pareto-optimality, two well sought-after fairness and efficiency notions within social choice. We then propose an efficient federated learning protocol CoreFed to optimize a core stable predictor. CoreFed determines a core-stable predictor when the loss functions of the agents are convex. CoreFed also determines approximate core-stable predictors when the loss functions are not convex, like smooth neural networks. We further show the existence of core-stable predictors in more general settings using Kakutani's fixed point theorem. Finally, we empirically validate our analysis on two real-world datasets, and we show that CoreFed achieves higher core-stability fairness than FedAvg while having similar accuracy.Comment: NeurIPS 2022; code: https://openreview.net/attachment?id=lKULHf7oFDo&name=supplementary_materia