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User modeling

About: User modeling is a research topic. Over the lifetime, 10701 publications have been published within this topic receiving 278012 citations.


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Journal ArticleDOI
TL;DR: This work proposes an intuitive formalism that captures assistance as policy blending, illustrates how some of the existing techniques for shared control instantiate it, and provides a principled analysis of its main components: prediction of user intent and its arbitration with the user input.
Abstract: In shared control teleoperation, the robot assists the user in accomplishing the desired task, making teleoperation easier and more seamless. Rather than simply executing the user's input, which is hindered by the inadequacies of the interface, the robot attempts to predict the user's intent, and assists in accomplishing it. In this work, we are interested in the scientific underpinnings of assistance: we propose an intuitive formalism that captures assistance as policy blending, illustrate how some of the existing techniques for shared control instantiate it, and provide a principled analysis of its main components: prediction of user intent and its arbitration with the user input. We define the prediction problem, with foundations in inverse reinforcement learning, discuss simplifying assumptions that make it tractable, and test these on data from users teleoperating a robotic manipulator. We define the arbitration problem from a control-theoretic perspective, and turn our attention to what users consider good arbitration. We conduct a user study that analyzes the effect of different factors on the performance of assistance, indicating that arbitration should be contextual: it should depend on the robot's confidence in itself and in the user, and even the particulars of the user. Based on the study, we discuss challenges and opportunities that a robot sharing the control with the user might face: adaptation to the context and the user, legibility of behavior, and the closed loop between prediction and user behavior.

331 citations

Book ChapterDOI
01 Jan 1997
TL;DR: ELM-ART II is introduced, an intelligent interactive textbook to support learning programming in LISP and demonstrates how interactivity and adaptivity can be implemented in WWW-based tutoring systems.
Abstract: Most learning systems and electronic textbooks accessible via the WWW up to now lack the capabilities of individualized help and adapted learning support that are the emergent features of on-site intelligent tutoring systems. This paper discusses the problems of developing interactive and adaptive learning systems on the WWW. We introduce ELM-ART II, an intelligent interactive textbook to support learning programming in LISP. ELM-ART II demonstrates how interactivity and adaptivity can be implemented in WWW-based tutoring systems. The knowledge-based component of the system uses a combination of an overlay model and an episodic user model. It also supports adaptive navigation as individualized diagnosis and help on problem solving tasks. Adaptive navigation support is achieved by annotating links. Additionally, the system selects the next best step in the curriculum on demand. Results of an empirical study show different effects of these techniques on different types of users during the first lessons of the programming course.

329 citations

Proceedings ArticleDOI
11 Apr 2016
TL;DR: This paper proposes a new probabilistic approach that directly incorporates user exposure to items into collaborative filtering, and recovers one of the most successful state-of-the-art approaches as a special case of the model.
Abstract: Collaborative filtering analyzes user preferences for items (e.g., books, movies, restaurants, academic papers) by exploiting the similarity patterns across users. In implicit feedback settings, all the items, including the ones that a user did not consume, are taken into consideration. But this assumption does not accord with the common sense understanding that users have a limited scope and awareness of items. For example, a user might not have heard of a certain paper, or might live too far away from a restaurant to experience it. In the language of causal analysis (Imbens & Rubin, 2015), the assignment mechanism (i.e., the items that a user is exposed to) is a latent variable that may change for various user/item combinations. In this paper, we propose a new probabilistic approach that directly incorporates user exposure to items into collaborative filtering. The exposure is modeled as a latent variable and the model infers its value from data. In doing so, we recover one of the most successful state-of-the-art approaches as a special case of our model (Hu et al. 2008), and provide a plug-in method for conditioning exposure on various forms of exposure covariates (e.g., topics in text, venue locations). We show that our scalable inference algorithm outperforms existing benchmarks in four different domains both with and without exposure covariates.

329 citations

Patent
28 Jun 1999
TL;DR: In this article, an automated recommendation system keeps track of the needs and preferences of the user through a user preference vector, which represents the user's preference for a specific item attribute.
Abstract: An automated recommendation system keeps track of the needs and preferences of the user through a user preference vector. Each field of the user preference vector represents the user's preference for a specific item attribute. Item attributes are defined by a systems programmer. The systems programmer also creates product vectors of items in the recommendation database. A user preference vector is compared against a product vector to determine if the product is suitable for recommendation. A recommended item may be purchased by the user by submitting a purchase request over a network connection. The user preference vector is constantly refined through feedback from the user about the recommended items.

329 citations

Journal Article
TL;DR: This paper aims to compute on-line automatic recommendations to an active learner based on his/her recent navigation history, as well as exploiting similarities and dissimilarities among user preferences and among the contents of the learning resources.
Abstract: Introduction Up to the very recent years, most e-learning systems have not been personalized Several works have addressed the need for personalization in the e-learning domain However, even today, personalization systems are still mostly confined to research labs, and most of the current e-learning platforms are still delivering the same educational resources in the same way to learners with different profiles In general, to enable personalization, existing systems used one or more types of knowledge (learners' knowledge, learning material knowledge, learning process knowledge, etc) Generally, personalization in e-learning systems concerns: adaptive interaction, adaptive course delivery, content discovery and assembly, and adaptive collaboration support The category of adaptive course delivery represents the most common and widely used collection of adaptation techniques applied in e-learning systems today Typical examples include dynamic course re-structuring and adaptive selection of learning objects, as well as adaptive navigation support, which have all benefited from the rise of using recommendation strategies to generate new and relevant links and items In fact, one of the new forms of personalization in e-learning environment is to give recommendations to learners in order to support and help them through the e-learning process A number of personalized systems have relied on explicit information given by a learner (demographic, questionnaire, etc) and have applied known methods and techniques of adapting the presentation and navigation (Chorfi et al, 2004) As explained in (Brusilovsky, 1996), two different classes of adaptation can be considered: adaptive presentation and adaptive navigation support Later, in (Brusilovsky, 2001), the taxonomy of adaptive hypermedia technologies was updated to add some extensions in relation with new technologies Then, the distinction between two modes of adaptive navigation support became a necessity, especially with the growth of recommender systems Automatic recommendation implies that the user profiles are created and eventually maintained dynamically by the system without explicit user information Examples include amazoncom's personalized recommendations and music recommenders like Mystrandcom in commercial systems (Mobasher 2006), smart recommenders in e-learning (Zaiane, 2002), etc In general, such systems differ in the input data, in user modeling strategies, and in prediction techniques Several approaches for automatic personalization have been reported in the literature, such as content-based or item-based filtering, collaborative filtering, rule-based filtering, and techniques relying on Web usage mining, etc (Nasraoui, 2005) Web recommender systems can be categorized depending on these approaches Content-based filtering (or item-based filtering) systems recommend items to a given user based on the correlation between the content of these items and the preferences of the user (Meteren et al, 2000) This means that the recommended items are considered to be similar to those seen and liked by the same user in the past Thus, there is no notion of a community of users, rather only one user profile is considered while making recommendations Classical examples of systems applying content based filtering approach include among other Personal webwatcher (Mladenic , 1996), syskill and webert (Pazzani et al, 1997), etc Collaborative filtering system recommends items that are liked by other users with similar interests Thus, the exploration of new items is assured by the fact that other similar user profiles are also considered Examples of such systems include GroupLens (Konstan et al, 1997) and (Sarwar et al, 1998) Hybrid recommender systems combine several recommendation strategies to provide better performance than either strategy alone Most hybrids work by combining several input data sources or several recommendation strategies …

324 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202327
202269
2021150
2020167
2019194
2018216