Ex2Vec: Characterizing Users and Items from the Mere Exposure Effect

The traditional recommendation framework seeks to connect user and content, by finding the best match possible based on users past interaction. However, a good content recommendation is not necessarily similar to what the user has chosen in the past. As humans, users naturally evolve, learn, forget, get bored, they change their perspective of the world and in consequence, of the recommendable content. One well known mechanism that affects user interest is the Mere Exposure Effect: when repeatedly exposed to stimuli, users' interest tends to rise with the initial exposures, reaching a peak, and gradually decreasing thereafter, resulting in an inverted-U shape. Since previous research has shown that the magnitude of the effect depends on a number of interesting factors such as stimulus complexity and familiarity, leveraging this effect is a way to not only improve repeated recommendation but to gain a more in-depth understanding of both users and stimuli. In this work we present (Mere) Exposure2Vec (Ex2Vec) our model that leverages the Mere Exposure Effect in repeat consumption to derive user and item characterization and track user interest evolution. We validate our model through predicting future music consumption based on repetition and discuss its implications for recommendation scenarios where repetition is common

Adaptation dynamique des interfaces homme-machine en utilisant du suivi de charge cognitive

Working Memory (WM) is the part of human cognition responsible for the short-term storage and processing of information; it is also a bottleneck in information processing. In this work, we develop strategies for providing computer systems with awareness of the user’s WM limitations. We introduce two frameworks for the cognitive adaptation of task-related user interfaces. The first one, MATCHS (Memory Adaptation Through Cognitive Handling Simulations), is a closed-loop control system capable of tracking the user’s estimated cognitive capacity by adjusting its value according to performance. The second one, AUHWM (An Unscented Hound for Working Memory), is developed upon MATCHS's ideas, employing an Unscented Kalman filter for the real time tracking of human cognitive capacity. We test and discuss the performance of both frameworks. Lastly, we lay out prospects of how the ideas developed here can be extended to provide better and more general assessment and adaptation to human cognitive capacities.La mémoire de travail est la partie de la cognition humaine responsable du stockage et du traitement de l'information à court terme. Elle constitue également un goulot d'étranglement majeur dans le traitement de l'information. Dans ce travail, nous présentons deux cadres d’adaptation cognitive des interfaces utilisateur liées à des tâches. Le premier, appelé MATCHS (Memory Adaptation Through Cognitive Handling Simulations), est un système de contrôle en boucle fermée capable de suivre la capacité cognitive estimée de l’utilisateur en l’ajustant en fonction de ses performances. Le deuxième, AUHWM (An Unscented Hound for Working Memory), est développé sur les idées de MATCHS, en utilisant un filtre de Kalman "Unscented" pour le suivi, en temps réel, de la capacité cognitive humaine. Nos testons et validons les deux approches. Enfin, nous exposons les perspectives futures que les idées développées dans ce travail laissent entrevoir pour fournir une évaluation et une adaptation meilleures et plus générales des capacités cognitives humaines

Adaptation dynamique des interfaces homme-machine en utilisant du suivi de charge cognitive

Working Memory (WM) is the part of human cognition responsible for the short-term storage and processing of information; it is also a bottleneck in information processing. In this work, we develop strategies for providing computer systems with awareness of the user’s WM limitations. We introduce two frameworks for the cognitive adaptation of task-related user interfaces. The first one, MATCHS (Memory Adaptation Through Cognitive Handling Simulations), is a closed-loop control system capable of tracking the user’s estimated cognitive capacity by adjusting its value according to performance. The second one, AUHWM (An Unscented Hound for Working Memory), is developed upon MATCHS's ideas, employing an Unscented Kalman filter for the real time tracking of human cognitive capacity. We test and discuss the performance of both frameworks. Lastly, we lay out prospects of how the ideas developed here can be extended to provide better and more general assessment and adaptation to human cognitive capacities.La mémoire de travail est la partie de la cognition humaine responsable du stockage et du traitement de l'information à court terme. Elle constitue également un goulot d'étranglement majeur dans le traitement de l'information. Dans ce travail, nous présentons deux cadres d’adaptation cognitive des interfaces utilisateur liées à des tâches. Le premier, appelé MATCHS (Memory Adaptation Through Cognitive Handling Simulations), est un système de contrôle en boucle fermée capable de suivre la capacité cognitive estimée de l’utilisateur en l’ajustant en fonction de ses performances. Le deuxième, AUHWM (An Unscented Hound for Working Memory), est développé sur les idées de MATCHS, en utilisant un filtre de Kalman "Unscented" pour le suivi, en temps réel, de la capacité cognitive humaine. Nos testons et validons les deux approches. Enfin, nous exposons les perspectives futures que les idées développées dans ce travail laissent entrevoir pour fournir une évaluation et une adaptation meilleures et plus générales des capacités cognitives humaines

Adaptation dynamique des interfaces homme-machine en utilisant du suivi de charge cognitive

La mémoire de travail est la partie de la cognition humaine responsable du stockage et du traitement de l'information à court terme. Elle constitue également un goulot d'étranglement majeur dans le traitement de l'information. Dans ce travail, nous présentons deux cadres d’adaptation cognitive des interfaces utilisateur liées à des tâches. Le premier, appelé MATCHS (Memory Adaptation Through Cognitive Handling Simulations), est un système de contrôle en boucle fermée capable de suivre la capacité cognitive estimée de l’utilisateur en l’ajustant en fonction de ses performances. Le deuxième, AUHWM (An Unscented Hound for Working Memory), est développé sur les idées de MATCHS, en utilisant un filtre de Kalman "Unscented" pour le suivi, en temps réel, de la capacité cognitive humaine. Nos testons et validons les deux approches. Enfin, nous exposons les perspectives futures que les idées développées dans ce travail laissent entrevoir pour fournir une évaluation et une adaptation meilleures et plus générales des capacités cognitives humaines.Working Memory (WM) is the part of human cognition responsible for the short-term storage and processing of information; it is also a bottleneck in information processing. In this work, we develop strategies for providing computer systems with awareness of the user’s WM limitations. We introduce two frameworks for the cognitive adaptation of task-related user interfaces. The first one, MATCHS (Memory Adaptation Through Cognitive Handling Simulations), is a closed-loop control system capable of tracking the user’s estimated cognitive capacity by adjusting its value according to performance. The second one, AUHWM (An Unscented Hound for Working Memory), is developed upon MATCHS's ideas, employing an Unscented Kalman filter for the real time tracking of human cognitive capacity. We test and discuss the performance of both frameworks. Lastly, we lay out prospects of how the ideas developed here can be extended to provide better and more general assessment and adaptation to human cognitive capacities

Adaptation dynamique des interfaces homme-machine en utilisant du suivi de charge cognitive

Working Memory (WM) is the part of human cognition responsible for the short-term storage and processing of information; it is also a bottleneck in information processing. In this work, we develop strategies for providing computer systems with awareness of the user’s WM limitations. We introduce two frameworks for the cognitive adaptation of task-related user interfaces. The first one, MATCHS (Memory Adaptation Through Cognitive Handling Simulations), is a closed-loop control system capable of tracking the user’s estimated cognitive capacity by adjusting its value according to performance. The second one, AUHWM (An Unscented Hound for Working Memory), is developed upon MATCHS's ideas, employing an Unscented Kalman filter for the real time tracking of human cognitive capacity. We test and discuss the performance of both frameworks. Lastly, we lay out prospects of how the ideas developed here can be extended to provide better and more general assessment and adaptation to human cognitive capacities.La mémoire de travail est la partie de la cognition humaine responsable du stockage et du traitement de l'information à court terme. Elle constitue également un goulot d'étranglement majeur dans le traitement de l'information. Dans ce travail, nous présentons deux cadres d’adaptation cognitive des interfaces utilisateur liées à des tâches. Le premier, appelé MATCHS (Memory Adaptation Through Cognitive Handling Simulations), est un système de contrôle en boucle fermée capable de suivre la capacité cognitive estimée de l’utilisateur en l’ajustant en fonction de ses performances. Le deuxième, AUHWM (An Unscented Hound for Working Memory), est développé sur les idées de MATCHS, en utilisant un filtre de Kalman "Unscented" pour le suivi, en temps réel, de la capacité cognitive humaine. Nos testons et validons les deux approches. Enfin, nous exposons les perspectives futures que les idées développées dans ce travail laissent entrevoir pour fournir une évaluation et une adaptation meilleures et plus générales des capacités cognitives humaines

Oblivion Tracking: Towards a Probabilistic Working Memory Model for the Adaptation of Systems to Alzheimer Patients

International audienceWe introduce a new probabilistic working memory (WM) model that we intend to use to automatically personalize user interfaces with respect to Alzheimer patients' declining WM capacity. WM is the part of the human memory responsible for the conscious short-term storing and manipulation of information. It is known to be extremely limited and to be one of the strongest factors that impact individual diierences in cognitive abilities. In particular, individuals suuering from Alzheimer's disease have signiicantly impaired WM capacities that worsen as the disease progresses. As a use case for our model, we describe a system that is designed to help patients with Alzheimer's disease choose the music track they would like to listen to from a given playlist. We discuss how our WM model could be used to adapt this system to each patient's disease progression in time and the consequent deterioration of her WM capacity. CCS CONCEPTS • Human-centered computing →User models; • Mathematics of computing →Bayesian networks

Adapting Human-Computer Interfaces to Working Memory Limitations Using MATCHS

International audienceWe introduce MATCHS (Memory Adaptation Through Cognitive Handling Simulation), a new feature forhuman-computer interaction (HCI) that enables tasks to be more efficiently performed when they heavily depend on a user’sworking memory (WM) capacity. WM is the part of human cognition responsible for short-term information storage; it iskey to the proper completion of one’s current task(s). Known to have very limited capacity and a fast decaying time, inparticular with age or when Alzheimer-like diseases set in, WM is one of the strongest factors that explain individual differences in cognitive abilities, thus motivating the introduction of WMspecific parameters in personalized user interface design. Our MATCHS framework for HCI design builds upon a lowlevel model of the user’s WM to predict how much, and forhow long, an information can be expected to be stored there. Thus, the task at hand can, accordingly, be dynamically adjusted. We implemented our approach in a new memory game named Match2s, and tested our framework, including with a small set ofusers. Our results suggest that MATCHS is able to properly assess someone’s current WM characteristics and track its evolutionas users’ motivation or focus evolve over time, providing useful information to adapt simple interaction scenarii such as the one inMatch2s. Future work will include more testing and applications, in particular with users for whom such interfaces could be amajor asset, i.e., patients suffering from dementia