Extraction of Interesting Adaptation Rules

Nasfi, Rim; Soui, Makram

doi:10.1016/j.procs.2014.07.081

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…In order to offer the user the personalized content adapted to his/her expectations, it is necessary to identify his/her needs and preferences . A personalized system has to satisfy preferences of the user based on his profile .…”

Section: Introductionmentioning

confidence: 99%

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Social recommendation: A user profile clustering‐based approach

Ouaftouh

Zellou

Idri

2019

Concurrency and Computation

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The recommendation in information systems is a specific form of information filtering that aims to present the relevant information interesting the user. This technique is used in different contexts such as social networking, e-commerce and information retrieval. Generally, existing recommender system techniques implement collaborative filtering by deducing a part of user interests from the preferences of other users with similar profiles. Many techniques can be used to implement Collaborative Filtering such as Bayesian Networks, latent semantic, and clustering.We present in this work a novel clustering approach using a modified partitional algorithm. We propose a user model that integrates the relevant user information and a clustering algorithm that generates groups of similar user profiles by implementing a profile similarity function. The proposed approach is then evaluated based on a set of user profiles data corresponding to the context of an e-commerce website. KEYWORDSclustering algorithm, recommender systems, similarity measures, user profile INTRODUCTIONRecent technologies offer access to large masses of information. In order to help the user get relevant information that specifically concerns him/her, personalization is the appropriate solution. The concept of personalization, also called adaptation, is an extension of a research trend that has been actively used since the beginning of the 1980s in human-machine interaction. 1 Personalization can be as much about the content (data, information, document, etc) as about the container (IHM, interaction platform, communication mode, communication channel, etc). This concept is often used in a variety of domains, ie, Information Retrieval, e-commerce, tutoring systems, etc.In order to offer the user the personalized content adapted to his/her expectations, it is necessary to identify his/her needs and preferences. 2-4A personalized system has to satisfy preferences of the user based on his profile. 5 The user profile is built using information gathered about the user and his/her use of the system. Offering personalized content adapted to user's expectations implies identifying his/her needs and preferences. [2][3][4] The implementation of personalization can be performed through different techniques, ie, personalizing the appearance of the interface, filtering the incoming information or recommending content to the user. In this paper, we focus on the last option that is recommending the content to the user.It has been shown recently 6 that a normal user can lose interest after 60 to 90 seconds of choosing through reviewing 10 to 20 items on one or two screens. 6 That points to the risks of the user abandoning the system if he/she doesn't find what interests him/her. Recommendation aims to ensure that each user will find something corresponding to his/her preferences.In this work, a novel approach is proposed for a user profile clustering based recommendation. This approach considers a global user model represented with a keyword vector model structure usin...

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Section: Introductionmentioning

confidence: 99%

“…The user profile is built using information gathered about the user and his/her use of the system. Offering personalized content adapted to user's expectations implies identifying his/her needs and preferences …”

Section: Introductionmentioning

confidence: 99%

Social recommendation: A user profile clustering‐based approach

Ouaftouh

Zellou

Idri

2019

Concurrency and Computation

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A novel feature selection method using generalized inverted Dirichlet-based HMMs for image categorization

Nasfi

Bouguila

2022

Int. J. Mach. Learn. & Cyber.

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A Case of De Garengeot Hernia and Perforated Appendicitis Diagnosed by Ultrasonography

Imataki

Miyake

Nagai

et al. 2021

Nihon Rinsho Geka Gakkai Zasshi (J Jpn Surg Assoc)

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A 71-year-old man was admitted to our hospital with a hard, irreducible, and enlarged right inguinal mass with a diameter of 1-cm. Ultrasonography (US) revealed a saccular structure 2.5-cm in diameter that stemmed from the abdominal cavity containing a tubular organ with a blind tip, net-like area, and anechoic area ; these features were identified as the appendix, greater omentum, and ascites, respectively. US also revealed a wall deficit in the appendix, while color Doppler imaging showed an absence of blood flow at the distal appendix. Perforated appendicitis was suspected, and surgery was performed 25-hours after clinical presentation. Surgery revealed an appendiceal perforation and abscess in the hernial sac, which were treated via appendectomy followed by McVay repair. Our case findings reveal that US is a useful tool for diagnosing De Garengeot hernia and identifying perforated appendicitis.

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Extraction of Interesting Adaptation Rules

Cited by 3 publications

References 8 publications

Social recommendation: A user profile clustering‐based approach

Social recommendation: A user profile clustering‐based approach

A novel feature selection method using generalized inverted Dirichlet-based HMMs for image categorization

A Case of De Garengeot Hernia and Perforated Appendicitis Diagnosed by Ultrasonography

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