optimizing web search using social annotations
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optimizing web search using social annotations

Project period

11/01/2017 - 12/17/2017

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107

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optimizing web search using social annotations
optimizing web search using social annotations

This paper explores the use of social annotations to improve web search. Nowadays, many services, e.g. del.icio.us, have been developed for web users to organize and share their favorite web pages on line by using social annotations. We observe that the social annotations can benefit web search in two aspects: 1) the annotations are usually good summaries of corresponding web pages; 2) the count of annotations indicates the popularity of web pages. Two novel algorithms are proposed to incorporate the above information into page ranking:

1) SocialSimRank (SSR) calculates the similarity between social annotations and web queries

2) SocialPageRank (SPR) captures the popularity of web pages. Preliminary experimental results show that SSR can find the latent semantic association between queries and annotations, while SPR successfully measures the quality (popularity) of a web page from the web users’ perspective. We further evaluate the proposed methods empirically with 50 manually constructed queries and 3000 auto-generated queries on a dataset crawled from del.icio.us. Experiments show that both SSR and SPR benefit web search significantly.

Why: Problem statement

There are three kinds of users related to the social search, namely web page creators, web page annotators, and search engine users. Obviously, these three user sets can overlap with each other. 

How: Solution description

In this paper, we studied the novel problem of integrating social annotations into web search. We observed that the fast-emerging annotations provided not only a multi-faceted summary but also a good indicator of the quality of web pages. Specifically, social annotations could benefit web search in both similarity ranking and static ranking. Two novel iterative algorithms have been proposed to capture the social annotations’ capability on similarity ranking and static ranking, respectively. The experimental results showed that SSR can successfully find the latent semantic relations among annotations and SPR can provide the static ranking from the web annotators’ perspective. Experiments on two query sets showed that both SPR and SSR could benefit web search significantly. The main contributions can be concluded as follows: 1) The proposal to study the problem of using social annotations to improve the quality of web search. 2) The proposal of the SocialSimRank algorithm to measure the association among various annotations. 3) The proposal of the SocialPageRank algorithm to measure a web page’s static ranking based on social annotations. It is just a beginning to integrate social annotations into web search. In the future, we would optimize the proposed algorithms and explore more sophisticated social features to improve the social search quality. 

How is it different from competition

 Social annotation, social page rank, social similarity, web search, evaluation 

Who are your customers

1) Web page creators create pages and link the pages with each other to make browsing easy for web users. They provide the basis for web search.

2) Web page annotators are web users who use annotations to organize, memorize and share their favorites online.

3) Search engine users use search engines to get information from the web. They may also become web page annotators if they save and annotate their favorites from the search results. Previous work shows that both web page creators and search engine users contribute to web search a lot. The web page creators provide not only the web pages and anchor texts for similarity ranking but also the link structure for static ranking from the web page creators’ point of view 

Project Phases and Schedule

phase 1:Completed the patient section and control section of the abbot.

phase 2:Completed the robot section and monitor section along with the programming.

Resources Required

Hardware Required:2 axis robot,PIC16F877A microcontroller,ECG sensor,GSM,GPS,RF transmitter,RF reciever,Zigbee,Relay with driver circuit,LCD

Software Required:MPlab compiler,Embedded C

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