Real-time personalized twitter search based on semantic expansion and quality model

Xiang, Zhu; Huang, Jiuming; Zhou, Bin; Li, Aiping; Jia, Yan

doi:10.1016/j.neucom.2016.10.082

Cited by 16 publications

(7 citation statements)

References 9 publications

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“…Provide interesting content to user by [1,2] 1. Push notification on a mobile phone: Interest content might be shown to the user through mobile phone notification.…”

Section: Microblog Real Time Filteringmentioning

confidence: 99%

“…Periodic email digest: user interested profile might be aggregated into an email form that periodically sent to a user [1]. In that case a user could read a longer story about content.…”

Section: Microblog Real Time Filteringmentioning

confidence: 99%

“…Which calculates the similarity between a tweet and a profile in vector space model with TFIDF weight of terms [1]. Tweets and profiles can be expressed as vectors.…”

Section: Tf-idf Scorementioning

confidence: 99%

“…BM25 is a bag of words ranking function [16] that ranks a information based on the user interest profile or query words appearing in each document information [1,2].…”

Section: Bm25mentioning

confidence: 99%

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A Survey of Information Retrieval on Microblog

Patel¹,

Bhatt²,

Shah³

2017

IJCA

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Twitter is most popular microblogging site. It provide us with real time data. This article Provide survey of techniques for retrieving information from twitter stream. This techniques aim is finding real world and most relevant information with respect to the query. For retrieve most relevant information used query expansion techniques. Twitter data contain large amount of information. Information rank retrieval techniques find important data and gives the final score to that information with respect to user interest profile

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“…Provide interesting content to user by [1,2] 1. Push notification on a mobile phone: Interest content might be shown to the user through mobile phone notification.…”

Section: Microblog Real Time Filteringmentioning

confidence: 99%

“…Periodic email digest: user interested profile might be aggregated into an email form that periodically sent to a user [1]. In that case a user could read a longer story about content.…”

Section: Microblog Real Time Filteringmentioning

confidence: 99%

“…Which calculates the similarity between a tweet and a profile in vector space model with TFIDF weight of terms [1]. Tweets and profiles can be expressed as vectors.…”

Section: Tf-idf Scorementioning

confidence: 99%

“…BM25 is a bag of words ranking function [16] that ranks a information based on the user interest profile or query words appearing in each document information [1,2].…”

Section: Bm25mentioning

confidence: 99%

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A Survey of Information Retrieval on Microblog

Patel¹,

Bhatt²,

Shah³

2017

IJCA

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“…The present challenge in the field of Information Retrieval (IR) is to improve the recommendation of relevant resources to users [1,2]. The user query is essential in retrieving documents, but most users find it difficult to formulate queries that can adequately represent their intentions.…”

Section: Introductionmentioning

confidence: 99%

Factors Affecting Users‟ Measure of Interest: A Study of the Effect of Task, Document Difficulty and Document Familiarity

Akuma¹,

Jayne²

2019

IJITCS

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Data on the web is constantly growing which may affect users" ability to find relevant information within a reasonable time limit. Some of the factors previously studied that affect users searching behaviour are task difficulty and topic familiarity. In this paper, we consider a set of implicit feedback parameters to investigate how document difficulty and document familiarity affects users searching behaviour in a taskspecific context. An experiment was conducted and data was collected from 77 undergraduate students of Computer science. Users" implicit features and explicit ratings of document difficulty and familiarity were captured and logged through a plugin in Firefox browser. Implicit feedback parameters were correlated with user ratings for document difficulty and familiarity. The result showed no correlation between implicit feedback parameters and the rating for document familiarity. There was, however, a negative correlation between user mouse activities and document difficulty ratings.Also, the dataset of all the participants in the experiment was grouped according to task type and analysed. The result showed that their behaviour varies according to task type. Our findings provide more insight into studying the moderating factors that affect user searching behaviour.

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