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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.


Papers
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Journal ArticleDOI
TL;DR: It is shown how Semantic Web technologies can be deployed to (partially) solve three important challenges for recommender systems applied in an open Web context to deal with the complexity of various types of relationships for recommendation inferencing.

112 citations

Proceedings ArticleDOI
10 Aug 2019
TL;DR: An attention-based framework to combine users' long-term and short-term preferences, thus users' representation can be generated adaptively according to the specific context is proposed and outperforms several state-of-art methods consistently.
Abstract: User modeling is an essential task for online recommender systems. In the past few decades, collaborative filtering (CF) techniques have been well studied to model users' long term preferences. Recently, recurrent neural networks (RNN) have shown a great advantage in modeling users' short term preference. A natural way to improve the recommender is to combine both long-term and short-term modeling. Previous approaches neglect the importance of dynamically integrating these two user modeling paradigms. Moreover, users' behaviors are much more complex than sentences in language modeling or images in visual computing, thus the classical structures of RNN such as Long Short-Term Memory (LSTM) need to be upgraded for better user modeling. In this paper, we improve the traditional RNN structure by proposing a time-aware controller and a content-aware controller, so that contextual information can be well considered to control the state transition. We further propose an attention-based framework to combine users' long-term and short-term preferences, thus users' representation can be generated adaptively according to the specific context. We conduct extensive experiments on both public and industrial datasets. The results demonstrate that our proposed method outperforms several state-of-art methods consistently.

112 citations

Book ChapterDOI
22 Jun 2003
TL;DR: The markup language UserML is presented, which tries to contribute a platform for the communication about partial user models in a ubiquitous computing environment, where all different kinds of systems work together to satisfy the user's needs.
Abstract: Ubiquitous computing offers new chances and challenges to the field of user modeling. With the markup language UserML, we try to contribute a platform for the communication about partial user models in a ubiquitous computing environment, where all different kinds of systems work together to satisfy the user's needs. We also present an implementation architecture of a general user model editor which is based on UserML. The keywords are ubiquitous computing, distributed user modeling and markup languages.

112 citations

Journal ArticleDOI
TL;DR: A novel scheme to represent a user's interest categories, and an adaptive algorithm to learn the dynamics of the user's interests through positive and negative relevance feedback are described.
Abstract: Learning users' interest categories is challenging in a dynamic environment like the Web because they change over time. This article describes a novel scheme to represent a user's interest categories, and an adaptive algorithm to learn the dynamics of the user's interests through positive and negative relevance feedback. We propose a three-descriptor model to represent a user's interests. The proposed model maintains a long-term interest descriptor to capture the user's general interests and a short-term interest descriptor to keep track of the user's more recent, faster-changing interests. An algorithm based on the three-descriptor representation is developed to acquire high accuracy of recognition for long-term interests, and to adapt quickly to changing interests in the short-term. The model is also extended to multiple three-descriptor representations to capture a broader range of interests. Empirical studies confirm the effectiveness of this scheme to accurately model a user's interests and to adapt appropriately to various levels of changes in the user's interests.

112 citations

Journal ArticleDOI
TL;DR: Experimental results show that a set of approximate dynamic programming algorithms combined to a method for learning a sparse representation of the value function can learn good dialogue policies directly from data, avoiding user modeling errors.
Abstract: Spoken Dialogue Systems (SDS) are systems which have the ability to interact with human beings using natural language as the medium of interaction. A dialogue policy plays a crucial role in determining the functioning of the dialogue management module. Handcrafting the dialogue policy is not always an option, considering the complexity of the dialogue task and the stochastic behavior of users. In recent years approaches based on Reinforcement Learning (RL) for policy optimization in dialogue management have been proved to be an efficient approach for dialogue policy optimization. Yet most of the conventional RL algorithms are data intensive and demand techniques such as user simulation. Doing so, additional modeling errors are likely to occur. This paper explores the possibility of using a set of approximate dynamic programming algorithms for policy optimization in SDS. Moreover, these algorithms are combined to a method for learning a sparse representation of the value function. Experimental results show that these algorithms when applied to dialogue management optimization are particularly sample efficient, since they learn from few hundreds of dialogue examples. These algorithms learn in an off-policy manner, meaning that they can learn optimal policies with dialogue examples generated with a quite simple strategy. Thus they can learn good dialogue policies directly from data, avoiding user modeling errors.

112 citations


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