Collaborative personalized Twitter search with topic-language models

Vosecky, Jan; Leung, Kenneth Wai-Ting; Ng, Wilfred

doi:10.1145/2600428.2609584

Cited by 41 publications

(24 citation statements)

References 23 publications

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“…Their contributions lie in a method to characterize this temporal density function using kernel density estimation, with and without human relevance judgments, and an approach to integrating this information into a standard retrieval model. Finally, Vosecky et al [69] propose a framework for collaborative personalized Twitter Search, which exploits the user's social connections in order to obtain a comprehensive account of her preferences. This framework includes a novel user model structure to manage the topical diversity in Twitter and to enable query disambiguation.…”

Section: Social Content Searchmentioning

confidence: 99%

Social networks and information retrieval, how are they converging? A survey, a taxonomy and an analysis of social information retrieval approaches and platforms

Bouadjenek

Hacid

Bouzeghoub

2016

Information Systems

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a b s t r a c tThere is currently a number of research work performed in the area of bridging the gap between Information Retrieval (IR) and Online Social Networks (OSN). This is mainly done by enhancing the IR process with information coming from social networks, a process called Social Information Retrieval (SIR). The main question one might ask is What would be the benefits of using social information (no matter whether it is content or structure) into the information retrieval process and how is this currently done?With the growing number of efforts towards the combination of IR and social networks, it is necessary to build a clearer picture of the domain and synthesize the efforts in a structured and meaningful way. This paper reviews different efforts in this domain. It intends to provide a clear understanding of the issues as well as a clear structure of the contributions. More precisely, we propose (i) to review some of the most important contributions in this domain to understand the principles of SIR, (ii) a taxonomy to categorize these contributions, and finally, (iii) an analysis of some of these contributions and tools with respect to several criteria, which we believe are crucial to design an effective SIR approach. This paper is expected to serve researchers and practitioners as a reference to help them structuring the domain, position themselves and, ultimately, help them to propose new contributions or improve existing ones.

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Section: Social Content Searchmentioning

confidence: 99%

Social networks and information retrieval, how are they converging? A survey, a taxonomy and an analysis of social information retrieval approaches and platforms

Bouadjenek

Hacid

Bouzeghoub

2016

Information Systems

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“…Using a threshold of 0.1 when assigning a topic to a user, we find out that 77% of the users have more than one center of interest. If we assume that a query deals with one topic, as in [11], it is then clear that we have to filter terms of the profile to expand the query. All these elements reinforce our initial idea that focusing on an adequate subset of the user profile may help to focus on relevant documents.…”

Section: Empirical Studymentioning

confidence: 99%

“…For example, selecting users that have an explicit [11,9] or implicit [3,12] relationships with the query issuer. [11] proposes a collaborative personalized search model based on topic models to disambiguate the query. [3] integrates other users from the social network that have annotated the document.…”

Section: Introductionmentioning

confidence: 99%

Axiomatic Term-Based Personalized Query Expansion Using Bookmarking System

Mulhem

Amer

Géry

2016

Lecture Notes in Computer Science

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Abstract. This paper tackles the problem of pinpointing relevant information in a social network for Personalized Information Retrieval (PIR). We start from the premise that user profiles must be filtered so that they outperform non profile based queries. The formal Profile Query Expansion Constraint is then defined. We fix a specific integration of profile and a probabilistic matching framework that fits into the constraint defined. Experiments are conducted on the Bibsonomy corpus. Our findings show that even simple profile adaptation using query is effective for Personalized Information Retrieval.

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“…People share their current status in the form of tweets (140 character short messages), which are often short and noisy in nature due to size limit [1]. Web search methods are not sufficient for Twitter's content search, it happens because of the diversity of topics, sparseness and its extreme social nature [2]. Many researchers focused on tweets content analysis by applying data mining techniques on tweets 3. www.facebook.com contents to find that Twitter users' information leaks.…”

Section: Related Workmentioning

confidence: 99%

Textual content based information retrieval from Twitter

Ahmad

Ali

2016

2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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Now a day's micro-blogging site Twitter 1 is rapidly gaining popularity among politician, celebrities, businessmen, academician and even ordinary people. Many users want to collect useful information from Twitter for possible future use. In this regard, the user requires a system that facilitates user to restore tweets and find them again with higher degree of relevance with user's query. In this paper, we propose a framework for tweets retrieval from Twitter. The system acquires information from Twitter by using Twitter search API and develops a corpus of user's contents by removing noisy and ambiguous elements from the retrieved collection of tweets. Further, we pose queries to obtain the results of the system. We find that the system return useful documents to the user in order of their decreasing relevance scores.

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Collaborative personalized Twitter search with topic-language models

Cited by 41 publications

References 23 publications

Social networks and information retrieval, how are they converging? A survey, a taxonomy and an analysis of social information retrieval approaches and platforms

Social networks and information retrieval, how are they converging? A survey, a taxonomy and an analysis of social information retrieval approaches and platforms

Axiomatic Term-Based Personalized Query Expansion Using Bookmarking System

Textual content based information retrieval from Twitter

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