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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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Patent
19 Dec 2012
TL;DR: In this article, a personalized news recommendation device and method based on news content and theme feature was proposed. But the authors did not reveal the model used to build the personalized user model with the theme model and a relevant named entity noun sequence.
Abstract: The invention discloses a personalized news recommendation device and method based on news content and theme feature The recommendation device is equipped with seven modules, namely a news capturing module, a pre-treatment module, a theme model training module, a theme model predicting module, a user model building module, a news recommendation module and a recommendation treatment module The recommendation method comprises the following steps: building an personalized user model with the theme model and a relevant named entity noun sequence to express the interest preference of the user reading news, and calculating weight and converting the theme feature vector of users so as to reduce the influence of hot themes and single news content on the user interest, thereby effectively overcoming the defects of concentrated user interest and insufficient diversity of recommendation results In a recommendation output stage, an initial recommendation news list is treated, a theme grouping process based on the personalized user model is added on the basis of currently repeating data deleting and redundancy filtering, and news texts are reordered again according to the aging weight so as to recommend the accurate, diversified and novel personalized news

99 citations

Proceedings ArticleDOI
01 Jun 2000
TL;DR: The proposed method observes user's reactions to the filtered documents and learns from them the profiles for the individual users and reinforcement learning is used to adapt the most significant terms that best represent user's interests.
Abstract: This paper describes a method for an information filtering agent to learn user's preferences. The proposed method observes user's reactions to the filtered documents and learns from them the profiles for the individual users. Reinforcement learning is used to adapt the most significant terms that best represent user's interests. In contrast to conventional relevance feedback methods which require explicit user feedbacks, our approach learns user preferences implicitly from direct observations of browsing behaviors during interaction. Field tests have been made which involved 10 users reading a total of 18,750 HTML documents during 45 days. The proposed method showed superior performance in personalized information filtering compared to the existing relevance feedback methods.

99 citations

Proceedings ArticleDOI
10 Aug 2015
TL;DR: It is shown how user metadata (age, gender, etc.) combined with image features derived from a convolutional neural network can be used to perform hashtag prediction and it is demonstrated that modeling the user can significantly improve the tag prediction quality over current state-of-the-art methods.
Abstract: Understanding the content of user's image posts is a particularly interesting problem in social networks and web settings. Current machine learning techniques focus mostly on curated training sets of image-label pairs, and perform image classification given the pixels within the image. In this work we instead leverage the wealth of information available from users: firstly, we employ user hashtags to capture the description of image content; and secondly, we make use of valuable contextual information about the user. We show how user metadata (age, gender, etc.) combined with image features derived from a convolutional neural network can be used to perform hashtag prediction. We explore two ways of combining these heterogeneous features into a learning framework: (i) simple concatenation; and (ii) a 3-way multiplicative gating, where the image model is conditioned on the user metadata. We apply these models to a large dataset of de-identified Facebook posts and demonstrate that modeling the user can significantly improve the tag prediction quality over current state-of-the-art methods.

98 citations

Journal ArticleDOI
TL;DR: The results show that similar patterns of user activity are observed at both the cognitive and page use levels, and activity patterns are able to distinguish between task types in similar ways and between tasks of different levels of difficulty.
Abstract: Personalization of support for information seeking depends crucially on the information retrieval system's knowledge of the task that led the person to engage in information seeking. Users work during information search sessions to satisfy their task goals, and their activity is not random. To what degree are there patterns in the user activity during information search sessionsq Do activity patterns reflect the user's situation as the user moves through the search task under the influence of his or her task goalq Do these patterns reflect aspects of different types of information-seeking tasksq Could such activity patterns identify contexts within which information seeking takes placeq To investigate these questions, we model sequences of user behaviors in two independent user studies of information search sessions (N = 32 users, 128 sessions, and N = 40 users, 160 sessions). Two representations of user activity patterns are used. One is based on the sequences of page use; the other is based on a cognitive representation of information acquisition derived from eye movement patterns in service of the reading process. One of the user studies considered journalism work tasks; the other concerned background research in genomics using search tasks taken from the TREC Genomics Track. The search tasks differed in basic dimensions of complexity, specificity, and the type of information product (intellectual or factual) needed to achieve the overall task goal. The results show that similar patterns of user activity are observed at both the cognitive and page use levels. The activity patterns at both representation layers are able to distinguish between task types in similar ways and, to some degree, between tasks of different levels of difficulty. We explore relationships between the results and task difficulty and discuss the use of activity patterns to explore events within a search session. User activity patterns can be at least partially observed in server-side search logs. A focus on patterns of user activity sequences may contribute to the development of information systems that better personalize the user's search experience.

98 citations

Patent
Jianqiang Shen1, Oliver Brdiczka1
22 Apr 2013
TL;DR: In this article, a conversation-simulating system facilitates simulating an intelligent conversation with a human user, where the system can receive a user-statement from the user during a simulated conversation, and generate a set of automatic-statements that each responds to the user's statement.
Abstract: A conversation-simulating system facilitates simulating an intelligent conversation with a human user. During operation, the system can receive a user-statement from the user during a simulated conversation, and generates a set of automatic-statements that each responds to the user-statement. The system then determines a set of behavior-characteristics for the user, and computes relevance scores for the automatic-statements based on the behavior-characteristics. Each relevance score indicates an outcome quality that the user is likely to perceive for the automatic-statement as a response to the user-statement. The system selects an automatic-statement that has a highest relevance score from the set of automatic-statements, and provides the selected automatic-statement to the user.

98 citations


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