Personalized social search based on the user's social network

Carmel, David; Zwerdling, Naama; Guy, Ido; Ofek-Koifman, Shila; Har'El, Nadav; Ronen, Inbal; Uziel, Erel; Yogev, Sivan; Chernov, Sergey

doi:10.1145/1645953.1646109

Cited by 174 publications

(116 citation statements)

References 35 publications

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“…Moreover a comparison with other temporal models [17] [30] would be required to evaluate the effectiveness of our proposed measure.…”

Section: Resultsmentioning

confidence: 99%

“…They applied the topic matching between the user's interests and the document's topic in order to rerank the web pages rather than using only the term matching between the query and the document's content. Carmel et al [17] explored the user's connections in social networks. They re-ranked search results based on their connection strength with the user's related persons and topics.…”

Section: Personalized Searchmentioning

confidence: 99%

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Time-Sensitive User Profile for Optimizing Search Personlization

Kacem

Faiz

2014

User Modeling, Adaptation, and Personalization

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 15229The contribution was presented at UMAP 2014 : http://www.um.org/umap2014/umap2014/index.html Any correspondence concerning this service should be sent to the repository administrator: staff-oatao@listes-diff.inp-toulouse.fr Abstract. Thanks to social Web services, Web search engines have the opportunity to afford personalized search results that better fit the user's information needs and interests. To achieve this goal, many personalized search approaches explore user's social Web interactions to extract his preferences and interests, and use them to model his profile. In our approach, the user profile is implicitly represented as a vector of weighted terms which correspond to the user's interests extracted from his online social activities. As the user interests may change over time, we propose to weight profiles terms not only according to the content of these activities but also by considering the freshness. More precisely, the weights are adjusted with a temporal feature. In order to evaluate our approach, we model the user profile according to data collected from Twitter. Then, we rerank initial search results accurately to the user profile. Moreover, we proved the significance of adding a temporal feature by comparing our method with baselines models that does not consider the user profile dynamics.

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“…Moreover a comparison with other temporal models [17] [30] would be required to evaluate the effectiveness of our proposed measure.…”

Section: Resultsmentioning

confidence: 99%

Section: Personalized Searchmentioning

confidence: 99%

Time-Sensitive User Profile for Optimizing Search Personlization

Kacem

Faiz

2014

User Modeling, Adaptation, and Personalization

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“…They used social annotations for this purpose. Some have also analyzed the impact of users social networks on personalization [18]. There are works that have evaluated some specific online social network and evaluated them for social search.…”

Section: Related Workmentioning

confidence: 99%

Empirical Evaluation of Social and Traditional Search Tools for Adhoc Information Retrieval

Hussain¹,

Missen²,

Akhtar³

et al. 2017

ijacsa

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Abstract-The nature of World Wide Web (www) has evolved over the passage of time. Easier and faster availability of Internet has given rise to huge volumes of data available online. Another cause of huge volumes of data is the emergence of online social networks (like Facebook, Twitter, etc.) which has actually changed the role of data consumers to data generators. Increasing popularity of these online social networks has also changed the way different web services used to be used. For example, Facebook messaging has some impact on usage of emails; twitter usage affects (positively or negatively) online newspaper readings. Both of these platforms are heavily used for information searching. In this paper, we evaluate the role of Facebook and Twitter for academic queries and compare the findings with Google search engines to find out if there is a chance that these online social networks will replace Google sooner. A query set selected from the standard AOL dataset is used for experimentation. Academic related queries are selected and classified by expert users. Findings of Google, Facebook and Twitter are compared against these queries using Mean Average Precision (MAP), as a metrics for evaluation. Results conclude that Google has the dominating factor with a better MAP than Facebook and Twitter.

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“… Title: Personalized social search based on the user's social network [16]. Author: Z. Dou, R. Song, and J.-R. Wen.…”

Section: Literature Survey On Movie Miningmentioning

confidence: 99%

A Survey on Personalized Multimedia Content Search

Vanashri¹

2015

IJRITCC

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Abstract-Millions of users share opinions on different aspects of life every day. User can give opinions through their comments related to multimedia contents. For analyzing user's demands we can evaluate user comments as a common online resource. Comments given b y user provide clues about them & also their interest area. In this paper, we done survey on different techniques used for personalized search in relevant information retrieval. Recommender systems or recommendation systems are a subclass of information filtering system that seek to predict 'rating' or 'preference' that are given by users. We propose a personalized search system, called MovieMine, enhance this proposed system to provide more relevant data summarized according to past & present comments left by user. Large movie data center provide movie review which can be utilized for more specific output. Experimental evaluations show that our proposed techniques are efficient and perform better than previously proposed methods. As in research work we can study more types of sentiment analysis with different recommender systems approach.

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Personalized social search based on the user's social network

Cited by 174 publications

References 35 publications

Time-Sensitive User Profile for Optimizing Search Personlization

Time-Sensitive User Profile for Optimizing Search Personlization

Empirical Evaluation of Social and Traditional Search Tools for Adhoc Information Retrieval

A Survey on Personalized Multimedia Content Search

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