Combining mitigation treatments against biases in personalized rankings: Use case on item popularity

Historical interactions leveraged by recommender systems are often non-uniformly distributed across items. Though they are of interest for consumers, certain items end up therefore being biasedly under-recommended. Existing treatments for mitigating these biases act at a single step of the pipeline (either pre-, in-, or post-processing), and it remains unanswered whether simultaneously introducing treatments throughout the pipeline leads to a better mitigation. In this paper, we analyze the impact of bias treatments along the steps of the pipeline under a use case on popularity bias. Experiments show that, with small losses in accuracy, the combination of treatments leads to better trade-offs than treatments applied separately. Our findings call for treatments rooting out bias at different steps simultaneously

How to Perform Reproducible Experiments in the ELLIOT Recommendation Framework: Data Processing, Model Selection, and Performance Evaluation

Recommender Systems have shown to be an efective way to alleviate the over-choice problem and provide accurate and tailored recommendations. However, the impressive number of proposed recommendation algorithms, splitting strategies, evaluation protocols, metrics, and tasks, has made rigorous experimental evaluation particularly challenging. ELLIOT is a comprehensive recommendation framework that aims to run and reproduce an entire experimental pipeline by processing a simple confguration fle. The framework loads, flters, and splits the data considering a vast set of strategies. Then, it optimizes hyperparameters for several recommendation algorithms, selects the best models, compares them with the baselines, computes metrics spanning from accuracy to beyond-accuracy, bias, and fairness, and conducts statistical analysis. The aim is to provide researchers a tool to ease all the experimental evaluation phases (and make them reproducible), from data reading to results collection. ELLIOT is freely available on GitHub at https://github.com/sisinflab/ellio

Popularity, novelty and relevance in point of interest recommendation: an experimental analysis

AbstractRecommender Systems (RSs) are often assessed in off-line settings by measuring the system precision in predicting the observed user's ratings or choices. But, when apreciseRS is on-line, the generated recommendations can be perceived as marginally useful because lacking novelty. The underlying problem is that it is hard to build an RS that can correctly generalise, from the analysis of user's observed behaviour, and can identify the essential characteristics of novel and yet relevant recommendations. In this paper we address the above mentioned issue by considering four RSs that try to excel on different target criteria: precision, relevance and novelty. Two state of the art RSs called and follow a classical Nearest Neighbour approach, while the other two, and are based on Inverse Reinforcement Learning. and optimise precision, tries to identify the characteristics of POIs that make them relevant, and , a novel RS here introduced, is similar to but it also tries to recommend popular POIs. In an off-line experiment we discover that the recommendations produced by and optimise precision essentially by recommending quite popular POIs. can be tuned to achieve a desired level of precision at the cost of losing part of the best capability of to generate novel and yet relevant recommendations. In the on-line study we discover that the recommendations of and are liked more than those produced by . The rationale of that was found in the large percentage of novel recommendations produced by , which are difficult to appreciate. However, excels in recommending items that are both novel and liked by the users

Système intelligent pour le suivi et l’optimisation de l’état cognitif

Les émotions des êtres humains changent régulièrement et parfois de manière brusque entrainant un changement de l’état mental c’est-à-dire de l’aptitude cérébrale à fonctionner normalement. Il en résulte une capacité cognitive (ou état cognitif) de l’individu à pouvoir raisonner, accéder à la mémoire, ou effectuer des déductions, variable selon l’état mental. Ceci affecte, en conséquence, les performances des utilisateurs qui varient en fonction de leurs état cognitifs. Cette thèse vise à optimiser l’état cognitif d’un utilisateur lors de ses interactions avec un environnement virtuel. Comme cet état dépend des émotions, l’optimisation de l’état cognitif peut être réalisée à travers l’optimisation des émotions et en particulier la réduction des émotions négatives. Une première partie concerne les moyens de mesurer en temps réel (par un Module de mesures) l’état émotionnel et mental d’un utilisateur lors de ses interactions avec un environnement virtuel. Nous avons réalisé pour cela quatre études expérimentales avec quatre environnements différents. Nous avons montré que ces mesures peuvent être réalisées en utilisant différents capteurs physiologiques. Nous avons aussi montré qu’il est possible de prédire la tendance de l’excitation (un état mental) à partir d’un traceur de regard. Dans une deuxième partie, nous présentons l’Agent Neural qui modifie les environnements virtuels afin de provoquer une modification de l’état émotionnel d’un utilisateur pour améliorer son état cognitif. Nous avons réalisé quatre études expérimentales avec quatre environnements virtuels, où l’Agent Neural intervient dans ces environnements afin de changer l’état émotionnel de l’utilisateur. Nous avons montré que l’agent est capable d’intervenir dans plusieurs types d’environnements et de modifier les émotions de l’utilisateur. Dans une troisième partie, présentons l’Agent Limbique, qui personnalise et améliore les adaptations faites par l’Agent Neural à travers l’observation et l’apprentissage des impacts des changements des environnements virtuels et des réactions émotionnelles des utilisateurs. Nous avons montré que cet agent est capable d’analyser les interventions de l’Agent Neural et de les modifier. Nous avons montré aussi que l’Agent Limbique est capable de générer une nouvelle règle d’intervention et de prédire son impact sur l’utilisateur. La combinaison du Module de mesures, de l’Agent Neural, et de l’Agent Limbique, nous a permis de créer un système de contrôle cognitif intelligent que nous avons appelé Système Limbique Digital.The human’s emotions change regularly and sometimes suddenly leading to changes in their mental state which is the brain’s ability to function normally. This mental state’s changes affect the users’ cognitive ability (or cognitive state) to reason, access memory, or make inferences, which varies depending on the mental state. Consequently, this affects the users’ performances which varies according to their cognitive states. This thesis aims to optimize the users’ cognitive state during their interactions with a virtual environment. Since this state depends on emotions, optimization of cognitive state can be achieved through the optimization of emotions and in particular the reduction of negative emotions. In a first part, we present the means of measuring in real time (using a Measuring module) the users’ emotional and mental state during their interactions with a virtual environment. We performed four experimental studies with four different environments. We have shown that these measurements can be performed using different physiological sensors. We have also shown that it is possible to predict the tendency of excitement (a mental state) using an eye tracker. In a second part, we present the Neural Agent which modifies virtual environments to provoke a modification on the users’ emotional state in order to improve their cognitive state. We performed four experimental studies with four virtual environments, in which the Neural Agent intervenes in these environments to change the users’ emotional state. We have shown that the agent is able to intervene in several types of environments and able to modify the users’ emotions. In a third part, we present the Limbic Agent, which personalizes and improves the adaptations performed by the Neural Agent through the observation and the learning from the virtual environments changes’ impacts and the users’ emotional reactions. We have shown that this agent is able to analyze the Neural Agent’s interventions and able to modify them. We have also shown that the Limbic Agent is able to generate a new intervention rule and predict its impact on the user. The combination of the Measuring Module, the Neural Agent, and the Limbic Agent, allowed us to create an intelligent cognitive control system that we called the Digital Limbic System

Movements, fishery interactions, and unusual mortalities of bottlenose dolphins

Bottlenose dolphins inhabiting coastlines and estuaries in Florida have been impacted in the past decade by development, algal blooms, catastrophic pollution, and fishery interactions (FI). Dolphins react to disturbance and environmental stressors by modifying their movements and habitat use, which may put them in jeopardy of conflict with humans. FI plays an increasing role in contributing to dolphin mortalities. I investigated dolphin movements, habitat use, residency patterns, and frequency of FI with sport fishing. Tagging studies with short-term data tags and bolt on radio-transmitters were done in several locations in Florida and the east coast providing fine-scale measurements of swimming, daily travels, and foraging activity. Transit speeds agreed with the predicted mean cost of transport as dolphins spent much of their day and night travelling and resting while swimming. Increased foraging was detected by stomach temperature changes revealing dolphins fed at night with a peak starting just after sunset. Dolphin abundance, site fidelity, ranging, stranding mortality, and community structure was characterized at Choctawhatchee and Pensacola Bays in the Florida Panhandle via surveying and photo-identification. Results showed they made frequent inshore movements, maintained site fidelity to specific areas, and comprised several distinct communities. FI was assessed at offshore reefs and coastal fishing piers near Destin, FL and Orange Beach, AL, showing that some dolphins demonstrate affinity to this activity. Harmful interactions with dolphins on reefs and at fishing piers negatively affect their resident communities. Mitigation of FI is suggested by use of avoidance strategies, gear modifications, and improved fish release practices