An Efficient Collaborative Information Retrieval System by Incorporating the User Profile

Naderi, Hassan; Rumpler, Béatrice; Pinon, Jean-Marie

doi:10.1007/978-3-540-71545-0_19

Cited by 11 publications

(5 citation statements)

References 7 publications

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“…Finally based on our evaluation methodology, we have shown that the maximum weighted matching of a weighted bipartite graph, in which the vertexes are the querydocument compound objects, is the optimal method to calculate the similarity between two user profiles. This method can be used to dynamically construct the user's community in a collaborative information retrieval environment which will be used in PERCIRS [6].…”

Section: Resultsmentioning

confidence: 99%

“…In [6] we developed a PERsonalized Collaborative Information Retrieval System (called PERCIRS) which is able to solve the personalization problem in CIR systems by dynamically creating user communities. In our proposed system creation of user communities is based on the similarity of user profiles.…”

Section: Personalization Means That Different Users May Have Differenmentioning

confidence: 99%

See 1 more Smart Citation

Three User Profile Similarity Calculation (UPSC) Methods and Their Evaluation

Naderi

Rumpler

2007

2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System

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Collaborative Information Retrieval (CIR) is a newtechnique for resolving the current problem of information retrieval systems. A CIR system registers the previous user interactions to response to the subsequent user queries more efficiently. However well-known problem of personalization in retrieval systems is more acute in CIR systems. The goals and the characteristics of two users may be different; so when they send the same query to a CIR system, they may be interested in two different lists of documents. To solve this problem, we have developed a personalized CIR system, called PERCIRS, based on the user profiles' similarity to satisfy their queries. Thus selecting an efficient method to calculate the similarity between user profiles is a key factor for enhancing PERCIRS's efficiency. In this paper, we propose three methods for user profile similarity calculation. Finally, we introduce a mechanism for evaluating these methods.

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Section: Resultsmentioning

confidence: 99%

Section: Personalization Means That Different Users May Have Differenmentioning

confidence: 99%

Three User Profile Similarity Calculation (UPSC) Methods and Their Evaluation

Naderi

Rumpler

2007

2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System

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“…Several other works were based on the user profiles to improve collaborative retrieval: we find the works of Naderi, Rumpler and Pinon (2007) which considered that the need for a user does not only depend of the query but also on their profile, as its construction and evolution are the problems that has been solved in several researches (Achemoukh & Ahmed-Ouamer, 2012). Several other works have tried to improve the search for users operating a well-defined language.…”

Section: Related Workmentioning

confidence: 99%

Information Retrieval Model using Uncertain Confidence's Network

Naouar

Hlaoua

Omri

2017

International Journal of Information Retrieval Research

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This paper proposes a new relevance feedback approach to collaborative information retrieval based on a confidence's network, which performs propagation relevance between annotations terms. The main contribution of our approach is to extract relevant terms to reformulate the initial user query considering the annotations as an information source. The proposed model introduces the concept of necessity that allows determining the terms that have strong association relationships. The authors estimated the association relationship to a measure of a confidence. Another contribution consists on determining the relevant annotations for a given evidence source. Since the user is over whelmed by a variety of contradictory annotations on even one which are far from the original subject, the authors' model proceed filtering these annotations to determine the relevant one and then it classify them by grouping those related semantically. The experimental study conducted on different queries gives promoters results. They show very encouraging results that could reach an improvement rate.

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“…In addition, there is a semantic relationship between a query q and the selected documents for this query, which will be helpful to calculate the real similarity between two user‐profiles2. To this end we have proposed the two following formulas in (Naderi et al , 2007a) to calculate the similarity between two compounded objects, which take into account this semantic relationship. In these formulas we consider a query q and a set of selected documents D to q as a compounded object: qD .…”

Section: User Profile Similarity Calculation (Upsc)mentioning

confidence: 99%

PERCIRS: a system to combine personalized and collaborative information retrieval

Naderi

Rumpler²

2010

Journal of Documentation

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Purpose -This paper aims to discuss and test the claim that utilization of the personalization techniques can be valuable to improve the efficiency of collaborative information retrieval (CIR) systems. Design/methodology/approach -A new personalized CIR system, called PERCIRS, is presented based on the user profile similarity calculation (UPSC) formulas. To this aim, the paper proposes several UPSC formulas as well as two techniques to evaluate them. As the proposed CIR system is personalized, it could not be evaluated by Cranfield, like evaluation techniques (e.g. TREC). Hence, this paper proposes a new user-centric mechanism, which enables PERCIRS to be evaluated. This mechanism is generic and can be used to evaluate any other personalized IR system. Findings -The results show that among the proposed UPSC formulas in this paper, the (query-document)-graph based formula is the most effective. After integrating this formula into PERCIRS and comparing it with nine other IR systems, it is concluded that the results of the system are better than the other IR systems. In addition, the paper shows that the complexity of the system is less that the complexity of the other CIR systems. Research limitations/implications -This system asks the users to explicitly rank the returned documents, while explicit ranking is still not widespread enough. However it believes that the users should actively participate in the IR process in order to aptly satisfy their needs to information. Originality/value -The value of this paper lies in combining collaborative and personalized IR, as well as introducing a mechanism which enables the personalized IR system to be evaluated. The proposed evaluation mechanism is very valuable for developers of personalized IR systems. The paper also introduces some significant user profile similarity calculation formulas, and two techniques to evaluate them. These formulas can also be used to find the user's community in the social networks.

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An Efficient Collaborative Information Retrieval System by Incorporating the User Profile

Cited by 11 publications

References 7 publications

Three User Profile Similarity Calculation (UPSC) Methods and Their Evaluation

Three User Profile Similarity Calculation (UPSC) Methods and Their Evaluation

Information Retrieval Model using Uncertain Confidence's Network

PERCIRS: a system to combine personalized and collaborative information retrieval

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