603 research outputs found
Layered evaluation of interactive adaptive systems : framework and formative methods
Peer reviewedPostprin
Evaluating the effectiveness of explanations for recommender systems : Methodological issues and empirical studies on the impact of personalization
Peer reviewedPostprin
A Recommender System for Adaptive Examination Preparation using Pearson Correlation Collaborative Filtering
Distance learning is any type of far-off instruction where the understudy isn't actually present for the exercise. It is blasting gratitude to the force of the Internet. Distance learning plays a vital role for examination preparation where multiple choice questions can be utilized to evaluate the performance of students. Multiple Choice Question (MCQ) is a type of question used in the examination to evaluate the performance of students accordingly where usually four options are given along with the question, and one has to choose the correct answer. This research includes a simulation model that has been built to keep the learners continue to learn the subjects they might be weak in. We have developed a methodology that may guide a student to update his/her area of weakness by using a recommender system based on Pearson Correlation Collaborative Filtering approach. The paper describes a recommender system that will keep track of a learner's profile and create an adaptive training mechanism using the performance matrix
Visualization for Recommendation Explainability: A Survey and New Perspectives
Providing system-generated explanations for recommendations represents an
important step towards transparent and trustworthy recommender systems.
Explainable recommender systems provide a human-understandable rationale for
their outputs. Over the last two decades, explainable recommendation has
attracted much attention in the recommender systems research community. This
paper aims to provide a comprehensive review of research efforts on visual
explanation in recommender systems. More concretely, we systematically review
the literature on explanations in recommender systems based on four dimensions,
namely explanation goal, explanation scope, explanation style, and explanation
format. Recognizing the importance of visualization, we approach the
recommender system literature from the angle of explanatory visualizations,
that is using visualizations as a display style of explanation. As a result, we
derive a set of guidelines that might be constructive for designing explanatory
visualizations in recommender systems and identify perspectives for future work
in this field. The aim of this review is to help recommendation researchers and
practitioners better understand the potential of visually explainable
recommendation research and to support them in the systematic design of visual
explanations in current and future recommender systems.Comment: Updated version Nov. 2023, 36 page
Personalized Memory Transfer for Conversational Recommendation Systems
Dialogue systems are becoming an increasingly common part of many users\u27 daily routines. Natural language serves as a convenient interface to express our preferences with the underlying systems. In this work, we implement a full-fledged Conversational Recommendation System, mainly focusing on learning user preferences through online conversations. Compared to the traditional collaborative filtering setting where feedback is provided quantitatively, conversational users may only indicate their preferences at a high level with inexact item mentions in the form of natural language chit-chat. This makes it harder for the system to correctly interpret user intent and in turn provide useful recommendations to the user. To tackle the ambiguities in natural language conversations, we propose Personalized Memory Transfer (PMT) which learns a personalized model in an online manner by leveraging a key-value memory structure to distill user feedback directly from conversations. This memory structure enables the integration of prior knowledge to transfer existing item representations/preferences and natural language representations. We also implement a retrieval based response generation module, where the system in addition to recommending items to the user, also responds to the user, either to elicit more information regarding the user intent or just for a casual chit-chat. The experiments were conducted on two public datasets and the results demonstrate the effectiveness of the proposed approach
Recommandation conversationnelle : écoutez avant de parlez
In a world of globalization, where offers continues to grow, the ability to direct people to their specific need is essential. After being key differentiating factors for Netflix and Amazon, Recommender Systems in general are no where near a downfall. Still, one downside of the basic recommender systems is that they are mainly based on indirect feedback (our behaviour, mainly form the past) as opposed to explicit demand at a specific time.
Recent development in machine learning brings us closer to the possibility for a user to express it’s specific needs in natural language and get a machine generated reply. This is what Conversational Recommendation is about. Conversational recommendation encapsulates several machine learning sub-tasks. In this work, we focus our study on methods for the task of item (in our case, movie) recommendation from conversation.
To explore this setting, we use, adapt and extend state of the art transformer based neural language modeling techniques to the task of recommendation from dialogue. We study the performance of different methods using the ReDial dataset [24], a conversational- recommendation dataset for movies. We also make use of a knowledge base of movies and measure their ability to improve performance for cold-start users, items, and/or both.
This master thesis is divided as follows. First, we review all the basics concepts and the previous work necessary to to this lecture. When then dive deep into the specifics our data management, the different models we tested, the set-up of our experiments and the results we got. Follows the original a paper we submitted at RecSys 2020 Conference. Note that their is a minor inconsistency since throughout the thesis, we use v to represent items but in the paper, we used i.
Overall, we find that pre-trained transformer models outperform baselines even if the baselines have access to the user preferences manually extracted from their utterances.Dans un monde de mondialisation, où les offres continuent de croître, la capacité de référer les gens vers leurs besoins spécifiques est essentiel. Après avoir été un facteur de différenciation clé pour Netflix et Amazon, les systèmes de recommandation en général ne sont pas près de disparaître. Néanmoins, l’un des leurs inconvénients est qu’ils sont principalement basés sur des informations indirects (notre comportement, principalement du passé) par opposition à une demande explicite à un moment donné.
Le développement récent de l’apprentissage automatique nous rapproche de la possibilité d’exprimer nos besoins spécifiques en langage naturel et d’obtenir une réponse générée par la machine. C’est ce en quoi consiste la recommandation conversationnelle. La recommandation conversationnelle englobe plusieurs sous-tâches d’apprentissage automatique. Dans ce travail, nous concentrons notre étude sur les méthodes entourant la tâche de recommandation d’item (dans notre cas, un film) à partir d’un dialogue.
Pour explorer cette avenue, nous adaptons et étendons les techniques de modélisation du langage basées sur les transformeurs à la tâche de recommandation à partir du dialogue. Nous étudions les performances de différentes méthodes à l’aide de l’ensemble de données ReDial [24], un ensemble de données de recommandation conversationnelle pour les films. Nous utilisons également une base de connaissances de films et mesurons sa capacité à améliorer les performances lorsque peu d’information sur les utilisateurs/éléments est disponible.
Ce mémoire par article est divisé comme suit. Tout d’abord, nous passons en revue tous les concepts de base et les travaux antérieurs nécessaires à cette lecture. Ensuite, nous élaborons les spécificités de notre gestion des données, les différents modèles que nous avons testés, la mise en place de nos expériences et les résultats que nous avons obtenus. Suit l’article original que nous avons soumis à la conférence RecSys 2020. Notez qu’il y a une incohérence mineure puisque tout au long du mémoire, nous utilisons v pour représenter les éléments mais dans l’article, nous avons utilisé i.
Dans l’ensemble, nous constatons que les modèles de transformeurs pré-entraînés surpassent les modèles de bases même si les modèles de base ont accès aux préférences utilisateur extraites manuellement des dialogues
Advances and Challenges of Multi-task Learning Method in Recommender System: A Survey
Multi-task learning has been widely applied in computational vision, natural
language processing and other fields, which has achieved well performance. In
recent years, a lot of work about multi-task learning recommender system has
been yielded, but there is no previous literature to summarize these works. To
bridge this gap, we provide a systematic literature survey about multi-task
recommender systems, aiming to help researchers and practitioners quickly
understand the current progress in this direction. In this survey, we first
introduce the background and the motivation of the multi-task learning-based
recommender systems. Then we provide a taxonomy of multi-task learning-based
recommendation methods according to the different stages of multi-task learning
techniques, which including task relationship discovery, model architecture and
optimization strategy. Finally, we raise discussions on the application and
promising future directions in this area
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