User Model Evolution Algorithm: Forgetting and Reenergizing User Preference

Zhou, Bo; Zhang, Bofeng; Liu, Yucong; Xing, Kun

doi:10.1109/ithings/cpscom.2011.71

Cited by 12 publications

(7 citation statements)

References 4 publications

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“…The two level model variant is preferred by multiple authors in [14,33,133,136,139], who identically divide model to long-and short-term parts. Another approach proposed in [145] added third level of medium-term preferences.…”

Section: Multiple Layer Modellingmentioning

confidence: 99%

“…Dynamic domains are challenging from the user behaviour analysis and modelling point of view. In other words, it is difficult to acquire and maintain user preferences due to the frequent changes in the content [145]. Moreover, as the Web is characterised by plenty of anonymous or occasional users, whose previous preferences are unknown [133], the short-term user behaviour modelling is actually gaining importance.…”

Section: Introductionmentioning

confidence: 99%

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Acquisition and Modelling of Short-TermUser Behaviour on theWeb: A Survey

Kassak

Kompan

Bieliková

2018

JWE

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User behaviour in data intensive applications such as the Web-based applications represents a complex set of actions influenced by plenty of factors. Thanks to this complexity, it is extremely hard for human to be able to understand all its aspects. Despite of this, by observing user actions from multiple views, we are able to extract and to model typical behaviour and its deviations on the Web. The website itself, together with transaction server logs, includes information about the site structure, content and about the actual user actions (clicks) within the site. User actions logically reflect the behaviour, while other sources indicate his/her context. Combination of these data sources allows to model the typical user behaviour and his/her preferences. The longterm behaviour describes relatively stable user preferences based on extensive user history. As the Web has become more and more dynamic, modelling user behaviour from the long-term perspective does not satisfy requirements of current Web based applications. On the other side, the short-term behaviour describes current user activity and his/her actual intent. However, this source of information is often noisy. To address these shortcomings the state-ofthe-art combines both perspectives, which allows to meaningful and timely modelling of user behaviour. In this paper, we provide a comprehensive survey

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Section: Multiple Layer Modellingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Acquisition and Modelling of Short-TermUser Behaviour on theWeb: A Survey

Kassak

Kompan

Bieliková

2018

JWE

View full text Add to dashboard Cite

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“…(5) If the sum of similarity and factor value greater than a certain threshold, then we recommend the microblogging to user, or do nothing.…”

Section: Recommendation Algorithmmentioning

confidence: 99%

“…This paper presents a method of recommend microblogging based on user model [5], which accurately describes the user's interests. This method can solve the problems mentioned above.…”

Section: Introductionmentioning

confidence: 99%

Method of Recommend Microblogging Based on User Model

Mei

Zhang

Zheng

et al. 2012

2012 IEEE 12th International Conference on Computer and Information Technology

Self Cite

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The situations of context absence and sparse feature in short texts present a challenge to effective personalized service, especially in the recommend short texts, widened semantic gap between low-level text features representation and high-level interpretation. Meanwhile, the propagation characteristics of short texts also have an effect on the results of recommend short texts. However, the traditional methods of recommend short texts rarely take the above two aspects into account when recommending short texts to users. To solve the above problems, this paper presents a microblogging recommend method based on user model. This method maps the feature of microblogging to the semantic concept by Semantic Extension Method, then calculates the similarity of user model and semantic microblogging, furthermore calculates the factor of microblogging's forwarding and comments, and lastly comprehensively considers the similarity and factor to recommend microblogging to users. Experiments show that the method of recommend microblogging based on user model is better than traditional methods. Users are more satisfied with the recommend results by user model than by traditional methods, and have a very high appraisal of this recommend method.

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“…Furthermore, a SAM (Spreading Activation Model) based evolution algorithm is proposed in [2] based on behavioral psychology for adapting to the dynamic changes of user interests. Another interesting work on the evolution of user modeling [4] benefits from the idea of forgetting and reenergizing mechanism of memory in psychology. As a result, it presented an evolution algorithm, called Forgetting and Reenergizing User Preference (FRUP), which overcomes the drawback of dynamic adaption to the user model evolution, thus it can automatically update user models and adapt to the drift of preferences over time.…”

Section: Introductionmentioning

confidence: 99%

MaaS: Model as a Service in Cloud Computing and Cyber-I Space

Zou

Zhang

Zheng

et al. 2012

2012 IEEE 12th International Conference on Computer and Information Technology

Self Cite

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In recent years, there has been an increasing interest in user modeling, because of its extremely wide usage in realworld applications, such as recommendation systems and personalized services. However, a large number of researchers have been devoted to focusing on developing a user model that can only facilitate their own applications, which has limited the rapid advances in user modeling research, so that ubiquitous modeling theory and its applications have become an emerging and open issue to solve. To handle this challenging task, this paper investigates the existing user modeling techniques and potentially applicable areas, including cloud computing and Cyber-Individual (Cyber-I), which are on the way of gradually evolving as important computing paradigms. This paper first proposes a novel user modeling conception and profile from Service-Oriented Architecture (SOA) point of view, called Model as a Service (MaaS). Then, based on the widely used three-layer abstraction of cloud computing, a new MaaS-based four-layer new architecture of cloud computing is proposed, allowing users and model developers to participate in cloud activities for the purpose of supporting personalized services. Finally, a novel MaaS-based Cyber-I framework is proposed on the basis of the architecture of Cyber-I oriented platform, where user modeling and MaaS-related operations center on the functionalities, providing the services of creation, evolution and retrieval in Cyber-I space.

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User Model Evolution Algorithm: Forgetting and Reenergizing User Preference

Cited by 12 publications

References 4 publications

Acquisition and Modelling of Short-TermUser Behaviour on theWeb: A Survey

Acquisition and Modelling of Short-TermUser Behaviour on theWeb: A Survey

Method of Recommend Microblogging Based on User Model

MaaS: Model as a Service in Cloud Computing and Cyber-I Space

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