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Simulating online recommender system performance is no- toriously difficult and the discrepancy between the online and offline behaviors is typically not accounted for in offline evaluations. Recommender systems research tends to evalu- ate model performan ...

One of the reasons sentiment lexicons do not reach human-level performance is that they lack the contexts that define the polarities of words. While obtaining this knowledge through machine learning would require huge amounts of data, context is commonsens ...

Session-based recommendation has received growing attention recently due to the increasing privacy concern. Despite the recent success of neural session-based recommenders, they are typically developed in an offline manner using a static dataset. However, ...

A computer implemented method is provided for monitoring the quality of service of a service provider, the method comprising the steps of receiving first and second reports from first and second clients, respectively, the first and second reports having in ...

Crowdsourcing marketplaces link large populations of workers to an even larger number of tasks. Thus, it is necessary to have mechanisms for matching workers with interesting and suitable tasks. Earlier work has addressed the problem of finding optimal wor ...

Increasing concerns with privacy have stimulated interests in Session-based Recommendation (SR) using no personal data other than what is observed in the current browser session. Existing methods are evaluated in static settings which rarely occur in real- ...

The object of the present invention is a method that allows a group of independent participants to coordinate decisions with respect to the allocation of interdependent resources, while maintaining certain privacy guarantees. The present invention proposes ...

Dynamic resource assignment is a common problem in multi-agent systems. We consider scenarios in which dynamic agents have preferences about assignments and the resources that can be assigned using online auctions. We study the trade-off between the follow ...

Motivating workers to provide significant effort has been recognized as an important issue in crowdsourcing. It is important not only to compensate worker effort, but also to discourage low-quality workers from participating. Several proper incentive schem ...

Automated predictions require explanations to be interpretable by humans. One type of ex- planation is a rationale, i.e., a selection of in- put features such as relevant text snippets from which the model computes the outcome. How- ever, a single overall ...

The reason for using distributed constraint satisfaction algorithms is often to allow agents to find a solution while revealing as little as possible about their variables and constraints. So far, most algorithms for DisCSP do not guarantee privacy of this ...

A recommender system for determining multimedia items to be presented in an online interface of a multimedia content provider. The recommender system continuously observes the user's behavior in order to define a context for the user which on the basis of ...

Linking facts across documents is a challenging task, as the language used to express the same information in a sentence can vary significantly, which complicates the task of multi-document summarization. Consequently, existing approaches heavily rely on h ...

We describe the selection, implementation and online evaluation of two e-commerce recommender systems developed with our partner company, Prediggo. The first one is based on the novel method of Bayesian Variable-order Markov Modeling (BVMM). The second, SS ...

As the Internet of Things grows to large scale, its components will increasingly be controlled by selfinterested agents. For example, sensor networks will evolve to community sensing where a community of agents combine their data into a single coherent str ...

Several mechanisms have been proposed for incentivizing truthful reports of a private signals owned by rational agents, among them the peer prediction method and the Bayesian truth serum. The robust Bayesian truth serum (RBTS) for small populations and bin ...

Boi Faltings

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Inferring User's Preferences using Ontologies

Modeling Online Behavior in Recommender Systems: The Importance of Temporal Context

Constructing Context-Aware Sentiment Lexicons with an Asynchronous Game with a Purpose

ADER: Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Recommendation

System and method for monitoring quality of service

Auction Based Mechanisms for Dynamic Task Assignments in Expert Crowdsourcing

Memory Augmented Neural Model for Incremental Session-based Recommendation

Optimizing the Tradeoff between Discovery, Composition, and Execution Cost in Service Composition

Method to allocate inter-dependent resources by a set of participants

Online Auctions for Dynamic Assignment: Theory and Empirical Evaluation

Learning to Scale Payments in Crowdsourcing with PropeRBoost

Rationalization through Concepts

Using an Ontological A-priori Score To Infer User's Preferences

Privacy Guarantees through Distributed Constraint Satisfaction

Recommender system for an online multimedia content provider

Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization

Context-Tree Recommendation vs Matrix-Factorization: Algorithm Selection and Live Users Evaluation

Eliciting Truthful Measurements from a Community of Sensors

A Robust Bayesian Truth Serum for Non-binary Signals

Memory Augmented Neural Model for Incremental Session-based Recommendation