Intelligent User Profiling

Schiaffino, Silvia; Amandi, Analía

doi:10.1007/978-3-642-03226-4_11

Cited by 104 publications

(54 citation statements)

References 48 publications

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“…user can modify the interface according to his/her preferences or a combination of both. Silvia Schiafino and Anala Amandi [1] discussed the main issues concerning user profiles: how a user profile is composed; how a user profile can be acquired; and how a user profile can be used. The Adaptive Web Applications are the user models that adapt the contents of a web page or provide navigation support to the user.…”

Section: Recommendationmentioning

confidence: 99%

“…A fuzzy set expresses the degree of membership of an element in that set. When compared to traditional binary or multi-valued logic, in which the degree of truth takes values from a discrete finite set, in fuzzy logic the degree of truth can take continuous values between [0,1]. This characteristic allows capturing the uncertainty inherent to real data.…”

Section: Fuzzy Logicmentioning

confidence: 99%

“…The main goal of user modeling is customization and adaptation of systems to the user specific needs. User model is a set of information structures designed to represent one or more of the following elements: (1) Representation of assumptions about the goals, plans, preferences, tasks and abilities and the knowledge about one or more types of users; (2) Representation of relevant common characteristics of users pertaining to specific user subgroups (stereotypes); (3) The classification of a user in one or more of these subgroups; (4) The recording of user behavior; (5) The formation of assumptions about the user based on the interaction history and/or (6) The generalization of the interaction histories of many users into stereotypes. [12] The information a user model has is directly proportional to the personalization content and presentation that is to be personalized.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Review and Analysis of Machine Learning and Soft Computing Approaches for User Modeling

Potey¹,

Sinha²

2015

IJWesT

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Section: Recommendationmentioning

confidence: 99%

Section: Fuzzy Logicmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Review and Analysis of Machine Learning and Soft Computing Approaches for User Modeling

Potey¹,

Sinha²

2015

IJWesT

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“…Our study combines tools that enable classification of the texts [7] and automatic recovery methods of text information monitored applicable pragmalinguistics studies that is used in journalistic language in order to identify features of journalistic profiles [15], [22]. Of major importance are some researches based on the collection of new media texts, used in identifying characteristics underlying implementation of text classifiers [11], [17], [13].…”

Section: State Of the Artmentioning

confidence: 99%

Monitoring and Predicting Journalistic Profiles

G®fu

Cristea

2013

Computational Collective Intelligence. Technologies and Applications

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Abstract. The paper develops a pilot study aiming at finding a methodology for identifying features of journalistic writing profiles. The study is based on capturing dominant discursive tonalities, knowing that journalistic discourse entails public legitimacy. We made use of a series of natural language processing tools as preliminary steps in revealing three egocentric journalistic identities. Based on quantitative analysis, such as syntactic and lexicalsemantic, we put in evidence qualitative pragmatic aspects. The final goal of this study is to configure a tool for automatic analysis of journalistic profiles. We have concentrated on three top Romanian journalists, whose newspaper publications have been monitored over a period of three months and semiautomatically analyzed. As a result, a database with their profiles is collected and interpreted. Such a tool could be of interest to mass-media, but also to specialists in communication and public relations, to political parties and to the public opinion, in general.

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“…On the other hand, current mobile advertising is still using traditional approach: to send an advertisement to as many people as possible, in hope that some of them will be interested in the advertisement and in turn buying the promoted product or service. This method has neglected the fact that each people have their own preferences, background and goal in using application, so they won't accept just any kind of advertisement [4].…”

Section: Introductionmentioning

confidence: 99%

Profiling-based mobile advertisement as a marketing strategy for GPS- based online traffic map

Pradipta

Endarnoto

Purnama

et al. 2011

Proceedings of the 2011 International Conference on Electrical Engineering and Informatics

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Abstract-The emergence of mobile computing is inevitably followed by mobile advertising: advertising that target mobile device, such as feature phones, smartphones and tablets. However, the majority of mobile advertising is still relying on the traditional approach: to send an advertisement to as many people as possible, in hope that some of them will be interested in the advertisement and in turn buying the promoted product or service. The problem with this method is that each people have their own preferences, so that kind of strategy won't get an optimum result. Furthermore, with traditional strategy there is very low chance of people getting the right advertisement in the right place.This research proposed a new advertisement system that combined a location based service with user profiling system. In this study, the advertisement system is integrated to an online traffic map mobile application, which also has an RSS feed reader feature. The system will learn user interest by using web mining to analyse browsing history that is taken from the RSS reader. When the user tries to generate a route in the map, the system will automatically fetch advertisements that are located along the route, which suited user interest.With this proposed system, we have successfully created a mobile advertisement that is highly efficient and effective. Moreover, it has gained positive feedback from user by being accurate, beneficial, and non-obtrusive. Better reception of advertisement from user will lead to the increasing rate of advertisement success.

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Intelligent User Profiling

Cited by 104 publications

References 48 publications

Review and Analysis of Machine Learning and Soft Computing Approaches for User Modeling

Review and Analysis of Machine Learning and Soft Computing Approaches for User Modeling

Monitoring and Predicting Journalistic Profiles

Profiling-based mobile advertisement as a marketing strategy for GPS- based online traffic map

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