p Recommender systems: Past, present, future

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Wizard of Oz, a very commonly employed technique in human-robot interaction, faces the criticism of being deceptive as the humans interacting with the robot are told, if at all, only at the end of their interaction that there was in fact a human behind the ...

By offering courses and resources, learning platforms on the Web have been attracting lots of participants, and the interactions with these systems have generated a vast amount of learning-related data. Their collection, processing and analysis have promot ...

In this paper, we propose and compare personalized models for Productive Engagement (PE) recognition. PE is defined as the level of engagement that maximizes learning. Previously, in the context of robot-mediated collaborative learning, a framework of prod ...

Countless signal processing applications include the reconstruction of signals from few indirect linear measurements. The design of effective measurement operators is typically constrained by the underlying hardware and physics, posing a challenging and of ...

Educational recommender systems channel most of the research efforts on the effectiveness of the recommended items. While teachers have a central role in online platforms, the impact of recommender systems for teachers in terms of the exposure such systems ...

The goal of this tutorial is to provide the WSDM community with recent advances on the assessment and mitigation of data and algorithmic bias in recommender systems. We first introduce conceptual foundations, by presenting the state of the art and describi ...

To operate the railway system safely and efficiently, a multitude of assets need to me monitored. Railway sleepers are one of these infrastructure assets, that are safety critical. To automate the monitoring process, data-driven fault diagnostics models ha ...

Recommenders are central in many applications today. The most effective recommendation schemes, such as those based on collaborative filtering (CF), exploit similarities between user profiles to make recommendations, but potentially expose private data. Fe ...

Using personalized explanations to support recommendations has been shown to increase trust and perceived quality. However, to actually obtain better recommendations, there needs to be a means for users to modify the recommendation criteria by interacting ...

Open-domain chatbots that can engage in a conversation on any topic received significant attention in the last several years, which opened opportunities for studying user interaction with them. Drawing from reviews of chatbots posted on Google Play, we exp ...

Understanding how learners engage with learning technologies, and its relation with their learning, is crucial to the design of effective learning interventions. Assessing the learners’ state however, is non-trivial. Research suggests that performance is n ...

Considering the impact of recommendations on item providers is one of the duties of multi-sided recommender systems. Item providers are key stakeholders in online platforms, and their earnings and plans are influenced by the exposure their items receive in ...

p Recommender systems: Past, present, future

Chat with Graph Search

Questioning Wizard of Oz: Effects of Revealing the Wizard behind the Robot

L2D 2021: First International Workshop on Enabling Data-Driven Decisions from Learning on the Web

Personalized Productive Engagement Recognition in Robot-Mediated Collaborative Learning

Gradient-Based Learning of Discrete Structured Measurement Operators for Signal Recovery

The Winner Takes it All: Geographic Imbalance and Provider (Un)fairness in Educational Recommender Systems

Advances in Bias-aware Recommendation on the Web

Contrastive Feature Learning for Railway Infrastructure Fault Diagnostic

TEE-based decentralized recommender systems: The raw data sharing redemption

Interacting with Explanations through Critiquing

User Expectations of Conversational Chatbots Based on Online Reviews

Many Are The Ways to Learn: Identifying multi-modal behavioral profiles of collaborative learning in constructivist activities

Interplay between upsampling and regularization for provider fairness in recommender systems