Personalised Information Retrieval: survey and classification

Ghorab, M. Rami; Zhou, Dong; O’Connor, Alexander; Wade, Vincent

doi:10.1007/s11257-012-9124-1

Cited by 101 publications

(76 citation statements)

References 107 publications

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“…There exist sufficient researches in personalized search [1,2]. These can be roughly allocated into two categories.…”

Section: Related Workmentioning

confidence: 99%

“…The number of topics and the number of terms selected from user models are chosen from [5,50]. The dimension of numbers and the number of documents retrieved from an exterior corpus are chosen from [10,100] and [1,10] respectively. When there are K = 15 latent topics and E = 50 dimensions for WE, γ = 5 documents retrieved from an exterior corpus, δ = 50 terms selected from user models to expand the original query, we obtain the highest performance.…”

Section: Eai Endorsed Transactions Onmentioning

confidence: 99%

“…The use of personalized Web search systems has become crucial in retrieving relevant information. Such system fetches relevant information that are most correlated to an individual user rather than only to the issued query [1,2]. Recording of the individual's interests and past behaviors in user models has been widely adopted.…”

Section: Introductionmentioning

confidence: 99%

“…Collaborative social tagging systems like del.icio.us 1 and BibSonomy 2 , etc., have become more and more popular. The tags and documents added by different users to the platforms are closely linked to that individual and their interests, providing abundant information for constructing more rigid and characteristic user models.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Augmented User Model for Personalized Search in Collaborative Social Tagging Systems

Zhao¹,

Zhou²,

Wu³

et al. 2017

EAI Endorsed Transactions on Collaborative Computing

Self Cite

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Alongside the enormous volume of user-generated content posted to World Wide Web, there exists a thriving demand for search personalization services. To provide personalized services, a user model is usually required. We address the setting adopted by the majority of previous work, where a user model consists solely of the user's past information. We construct an augmented user model from a number of tags and documents. These resources are further processed according to the user's past information by exploring external knowledge base. A novel generative model is proposed for user model generation. This model utilizes recent advances in neural language models such as Word Embeddings with latent semantic models such as Latent Dirichlet Allocation. We further present a new query expansion method to facilitate the desired personalized retrieval. Experiments conducted on two real-world collaborative social tagging datasets show that our proposed methods outperform state-of-the-art methods.

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“…There exist sufficient researches in personalized search [1,2]. These can be roughly allocated into two categories.…”

Section: Related Workmentioning

confidence: 99%

Section: Eai Endorsed Transactions Onmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Augmented User Model for Personalized Search in Collaborative Social Tagging Systems

Zhao¹,

Zhou²,

Wu³

et al. 2017

EAI Endorsed Transactions on Collaborative Computing

Self Cite

View full text Add to dashboard Cite

show abstract

“…The conventional method of Information Retrieval system, presented in Personalized Information Retrieval (PIR) [28] is that, even if the query submitted by different users at different context it will retrieve the same list of result. This can be overcome by applying user feedback mechanism or web personalization.…”

Section: International Journal Of Computer Applications (0975 -8887)mentioning

confidence: 99%

Intelligent Web Information Retrieval based on User Navigational Patterns

Prasanth¹,

Hemalatha²

2015

IJCA

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The foremost mission of any information retrieval algorithm is the efficient extraction of user interests. The rapid growth of web data, intense competition and user's option to choose from several alternatives increases this issue. In this context Web usage mining can provide valuable contributions in terms of ideas and methods, as it fissures useful knowledge from the pattern of user interactions with the Web. The user interest can be identified by analyzing the access pattern of user browse, the web pages they save, collect, or print. These valuable information's are available in server logs, which can be exploited to satisfy user needs by optimizing the document-retrieval task. This article is a review conducted in the field of web usage mining and its latest works for supporting the research on efficient information retrieval based on user access pattern. This survey analyzes 25 released information retrieval models to find out the major mining techniques applied in them and also to analyze the effect of diverse parameters like feedbacks, time, content, frequency etc in information retrieval. The goal of this survey is to find the best composition of features to be included in an efficient information retrieval model. Using those features a new retrieval model is then proposed. General TermsInformation Retrieval, Web Usage Mining.

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Personalization in text information retrieval: A survey

Liu

Belkin

2019

Asso for Info Science & Tech

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Personalization of information retrieval (PIR) is aimed at tailoring a search toward individual users and user groups by taking account of additional information about users besides their queries. In the past two decades or so, PIR has received extensive attention in both academia and industry. This article surveys the literature of personalization in text retrieval, following a framework for aspects or factors that can be used for personalization. The framework consists of additional information about users that can be explicitly obtained by asking users for their preferences, or implicitly inferred from users' search behaviors. Users' characteristics and contextual factors such as tasks, time, location, etc., can be helpful for personalization. This article also addresses various issues including when to personalize, the evaluation of PIR, privacy, usability, etc. Based on the extensive review, challenges are discussed and directions for future effort are suggested.

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Personalised Information Retrieval: survey and classification

Cited by 101 publications

References 107 publications

An Augmented User Model for Personalized Search in Collaborative Social Tagging Systems

An Augmented User Model for Personalized Search in Collaborative Social Tagging Systems

Intelligent Web Information Retrieval based on User Navigational Patterns

Personalization in text information retrieval: A survey

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