Information Retrieval and Graph Analysis Approaches for Book Recommendation

The Scientific World JOURNAL ◽

10.1155/2015/926418 ◽

2015 ◽

Vol 2015 ◽

pp. 1-8 ◽

Cited By ~ 4

Author(s):

Chahinez Benkoussas ◽

Patrice Bellot

Keyword(s):

Information Retrieval ◽

Directed Graph ◽

Language Model ◽

Social Information ◽

Link Analysis ◽

Graph Analysis ◽

Retrieval Models ◽

Xml Retrieval ◽

Ranked Retrieval

A combination of multiple information retrieval approaches is proposed for the purpose of book recommendation. In this paper, book recommendation is based on complex user's query. We used different theoretical retrieval models: probabilistic as InL2 (Divergence from Randomness model) and language model and tested their interpolated combination. Graph analysis algorithms such as PageRank have been successful in Web environments. We consider the application of this algorithm in a new retrieval approach to related document network comprised of social links. We called Directed Graph of Documents (DGD) a network constructed with documents and social information provided from each one of them. Specifically, this work tackles the problem of book recommendation in the context of INEX (Initiative for the Evaluation of XML retrieval) Social Book Search track. A series of reranking experiments demonstrate that combining retrieval models yields significant improvements in terms of standard ranked retrieval metrics. These results extend the applicability of link analysis algorithms to different environments.

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Determining subject headings of documents using information retrieval models

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v23.i2.pp1049-1058 ◽

2021 ◽

Vol 23 (2) ◽

pp. 1049

Author(s):

Evi Yulianti ◽

Laksmita Rahadianti

Keyword(s):

Public Health ◽

Information Retrieval ◽

Research Collaboration ◽

Language Model ◽

Document Retrieval ◽

Controlled Vocabulary ◽

Retrieval Models ◽

Subject Headings ◽

Library Resources ◽

The Subject

<p>Subject heading is a controlled vocabulary that describes the topic of adocument, which is important to find and organize library resources. Assigning appropriate subject headings to a document, however, is a time-consuming process. We therefore conduct a novel study on the effectiveness of information retrieval models, i.e.,language model (LM) andvector spacemodel (VSM), to automatically generate a ranked list of relevant subject headings, with the aim to give a recommendation for librarians to determine the subject headings effectively and efficiently. Our results show that there are a high number of our queries (up to 61%) that have relevant subject headings in the ten top-ranked recommendations and on average, the first relevant subject heading is found at the early position (3rd rank). This indicates that document retrieval methods can help the subject heading assignment process. LM and VSM are shown to have comparable performance, except when the search unit is title, VSM is superior to LM by8-22%. Our further analysis exhibits three faculty pairs that are potential to have research collaboration as their students’ thesis often have overlap subject headings: i) economy and business-social and political sciences, ii) nursing-public health and iii) medicine-public health.</p>

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A Survey on Information Retrieval Models, Techniques and Applications

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i7.90 ◽

2017 ◽

Vol 7 (7) ◽

pp. 16 ◽

Cited By ~ 1

Author(s):

Ndengabaganizi Tonny James ◽

Rajkumar Kannan

Keyword(s):

Information Retrieval ◽

Retrieval Models ◽

Knowledge Based ◽

Long Time

It has been long time many people have realized the importance of archiving and finding information. With the advent of computers, it became possible to store large amounts of information; and finding useful information from such collections became a necessity. Over the last forty years, Information Retrieval (IR) has matured considerably. Several IR systems are used on an everyday basis by a wide variety of users. Information retrieval (IR) is generally concerned with the searching and retrieving of knowledge-based information from database. In this paper, we will discuss about the various models and techniques and for information retrieval. We are also providing the overview of traditional IR models.

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