An automatic learning for re-ranking in social information retrieval

Author(s):  
Rihab Haddad ◽  
Lobna Hlaoua
2019 ◽  
Vol Volume 27 - 2017 - Special... ◽  
Author(s):  
Abir Gorrab ◽  
Ferihane Kboubi ◽  
Henda Ghézala

The explosion of web 2.0 and social networks has created an enormous and rewarding source of information that has motivated researchers in different fields to exploit it. Our work revolves around the issue of access and identification of social information and their use in building a user profile enriched with a social dimension, and operating in a process of personalization and recommendation. We study several approaches of Social IR (Information Retrieval), distinguished by the type of incorporated social information. We also study various social recommendation approaches classified by the type of recommendation. We then present a study of techniques for modeling the social user profile dimension, followed by a critical discussion. Thus, we propose our social recommendation approach integrating an advanced social user profile model. L’explosion du web 2.0 et des réseaux sociaux a crée une source d’information énorme et enrichissante qui a motivé les chercheurs dans différents domaines à l’exploiter. Notre travail s’articule autour de la problématique d’accès et d’identification des informations sociales et leur exploitation dans la construction d’un profil utilisateur enrichi d’une dimension sociale, et son exploitation dans un processus de personnalisation et de recommandation. Nous étudions différentes approches sociales de RI (Recherche d’Information), distinguées par le type d’informations sociales incorporées. Nous étudions également diverses approches de recommandation sociale classées par le type de recommandation. Nous exposons ensuite une étude des techniques de modélisation de la dimension sociale du profil utilisateur, suivie par une discussion critique. Ainsi, nous présentons notre approche de recommandation sociale proposée intégrant un modèle avancé de profil utilisateur social.


2012 ◽  
Vol 2 (4) ◽  
pp. 12-30
Author(s):  
Fethi Fkih ◽  
Mohamed Nazih Omri

Collocation is defined as a sequence of lexical tokens which habitually co-occur. This type of information is widely used in various applications such as Information Retrieval, document indexing, machine translation, lexicography, etc. Therefore, many techniques are developed for the automatic retrieval of collocations from textual documents. These techniques use statistical measures based on a joint frequency calculation to quantify the connection strength between the tokens of a candidate collocation. The discrimination between relevant and irrelevant collocations is performed using a priori fixed threshold. Generally, the discrimination threshold estimation is performed manually by a domain expert. This supervised estimation is considered as an additional cost which reduces system performance. In this paper, the authors propose a new technique for the threshold automatic learning to retrieve information from web text document. This technique is mainly based on the usual performance evaluation measures (such as ROC and Precision-Recall curves). The results show the ability to automatically estimate a statistical threshold independently of the treated corpus.


Author(s):  
Brendan Luyt ◽  
Chu Keong Lee

In this chapter we discuss some of the social and ethical issues associated with social information retrieval. Using the work of Habermas we argue that social networking is likely to exacerbate already disturbing trends towards the fragmentation of society and a corresponding decline reduction in social diversity. Such a situation is not conducive to developing a healthy, democratic society. Following the tradition of critical theorists of technology, we conclude with a call for responsible and aware technological design with more attention paid to the values embedded in new technological systems.


Author(s):  
Hans Hjelm ◽  
Martin Volk

A formal ontology does not contain lexical knowledge; it is by nature language-independent. Mappings can be added between the ontology and, arbitrarily, many lexica in any number of languages. The result of this operation is what is here referred to as a cross-language ontology. A cross-language ontology can be a useful resource for machine translation or cross-language information retrieval. This chapter focuses on ways of automatically building an ontology by exploiting cross-language information from parallel corpora. The goal is to improve the automatic learning results compared to learning an ontology from resources in a single language. The authors present a framework for cross-language ontology learning, providing a setting in which cross-language evidence (data) can be integrated and quantified. The aim is to investigate the following question: Can cross-language data teach us more than data from a single language for the ontology learning task?


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