Deciphering trends in mobile search kamvar

Essential Phone Unboxing - Is This Your Next Phone?

Some of the changes being:Users types fasterMore users are clickingMore exploration with a sessionLess Homogeneous queriesMore High-end devicesTrends in User types faster:Time from requesting the google front page to submitting a query has decreased from More Users are clickingIn , less than 10 percent of queries with atleast one click on a search result. In , the percent of queries with atleast one click, was more than 50 percent. Request for more search results increased from 8. Factors for increase:Drastic improvements in transcoder technology. Secondly, reduction in time to retrieve the search results.

Trends in continuedLess static queries:Diversity of queries increasedIn , top query accounted for 0. Measuring the cumulative frequency of the top 1, queries from a random set of more than 50, mobile queries in and , a decrease from 22 to 17 percent. Trends in continuedMore High end devices. Trends in and Conclusion. Post on Dec Views. Category: Documents 0 Downloads. Introduction Only wireless mobile requests was taken for consideration. Sample Data for study Average cell mobile query was 2. Top five categories in mobile search Wireless search is a more recent boom in than desktop search, it would also follow the same trend as Desktop search.

[FULL] computer graphics by g s baluja

Authors speculated that people feel more comfortable querying adult term on private devices. Trends in continued More Users are clickingIn , less than 10 percent of queries with atleast one click on a search result. Trends in continuedMore High end devices More adult queries:Better transcoder improvements.

It may reverse, as it did in wired networks. There is a need for fast, personal and effective information retrieval strategies. This study proposes a semantic ontology based approach for the information retrieval.

  • Author: Shumeet Baluja!
  • Let us contact you!.
  • Maryam Kamvar Garrett.

The essential aspect of this study is to have a semantic fast snippet based re-ranking approach based on a powerful and vivid mobile ontology. The re-ranking process is carried out by the event control access system thus taking a step towards making the information retrieval system understand the information it processes.

Experimental results validate the premise of this study. The overall design of the ontological system, re-ranking system and the reasoning mechanism is described in this study. Improving the dimensions of search operations has been a focus of much research study like that of Bedi and Chawla This is even more relevant in a mobile phone context. All the information in the world is available in our fingertips literally through a mobile phone. Amershi and Morris have stated that large populations of the developed world have access to a mobile phone. There is also an explosive growth in the developing countries.

But, the usage of the device for the search process is often frustrating to users Arias et al. When a user seeks information from the Web, the process starts with a keyword being typed in the search field of the search engine. The search engine's software program then utilizes algorithmic functions and criteria to find keyword matches in the information stored in the databases.

The vast amount of information leads to information overload according to Tsai Now, software applications such as by Banu and Khader have been developed that use the results of the web contents and further provide a prioritized results to the user based on relevancy of the Web page in terms of various criteria. Developing search applications for the mobile phones is exacerbated by the nature of the domain with its small size screen, limited processing power and communication speeds.

Also, methods for handling the content in various file formats of the web in mobile phones are severely limited. The need of the hour is for methods that do not rely on complex data manipulation as the overheads of the data manipulation will add to the bad user experiences.

Hence, fast and effective and personalized information access methods are the need of the hour. Personalized information access methods provide an alternative to the one size fits all approach of the web.

Refine list

In a truly personal device like the mobile phone with a single person using the system always, this is Bouidghaghen et al. The various Information Retrieval models have been given by Prasannakumari In general, Kastrinakis and Tzitzikas state that web search can be improved through the improvement of the query, use of ontologies for query processing Corby et al.

In a mobile phone based context, the web usage is personal in nature, Kamvar and Baluja state that the searching consists of short queries and demands results that are most relevant to the user There is a need for methods that are relevant and also understandable to the users. In a mobile context, various methods have been proposed to improve the quality of the web search. This study proposes to use an ontology based model for the re-ranking process. An event condition action system is proposed for the reasoning process. The solutions for improving the quality of results in the mobile phones are constrained by the limitations of the domain.

There have been many approaches in the semantic aspects. A semantic web based local search system was proposed by Jeon and Lee This study was implemented in data in a simulator based local setting. A methodology for personalizing the search results using ontologies where an intermediate server was placed between the mobile client and the web was discussed by Goenka et al. A framework for web data management using ontologies was described by Huo et al. The use of event condition action languages using semantic web has been described in detail Papamarkos et al.

A variation of the rules for the mobile context in an ontological form has been applied in this study. Knowledge representation and reasoning on this knowledge are the two main components of Semantic web Ilyas et al. A genetic algorithm using ontologies has been explained by Bergstrom et al.

Deciphering Trends in Mobile Search

This study uses a reasoning mechanism that uses a complex reasoning system and is based on nested term document pairs. The ontology follows the properties of inheritance. The awareness of a specific situation is called context awareness as per Liao and Tu This can be used in mobile phones to restrict the granularity of the information served.

  1. CN1474314A - 一种利用关键词筛选题目的系统及方法 - Google Patents.
  2. Understanding portal-based mobile search: a case study;
  3. Deciphering trends in mobile search - Search results - Pascal and Francis Bibliographic Databases;
  4. Recognition and correction of voice web search queries!
  5. Publications?
  6. Deciphering Trends in Mobile Search.
  7. Lane et al. In this study, the personal context of the usage is maintained in the personalized ontological model. The intermediate server model has also be used Carpineto et al.

    Deciphering Trends in Mobile Search | Sciweavers

    At this stage, our study does not involve the use of any intermediate server and instead focuses on re-ranking algorithms. The visualization of search interface queries has been explored by Church and Smyth A complex and comprehensive method incorporating summarization, visualization and clustering is explained by Machado et al.

    • find me at t phone number.
    • how to conduct a background investigation;
    • birth certificates of the 1940s;
    • USB2 - Dynamic community-based cache for mobile search - Google Patents.
    • Mobile Searcher Behavior Should Drive Design & SEO.
    • ip bus rca interconnector owners manual.

    This feature is what is of primary importance over other aspects. A method for transforming web pages is explained in Xiao et al. The method is important in the search and display of the contents and will be a part of our future study. A model that adapts itself based on the trends in search of the users has been described Ruvini, In our method the adaptation strategy is in the updates of the ontologies. The log based manipulation can be applied in the future. The relevance feedback method outlined in Vinay et al. The important difference is that only the results at the website level are considered.

    While the above approaches show the depth of the study in the domain, our study is unique in its expressive nature of the ontology, intuitive and feedback based re-ranking algorithm and support for clustering. The study also uses the database centric approach for ontologies thus making it generalizable and extensible. This study uses the snippet clustering approach for ranking the results. The content based clustering for the documents will need a Document object model for the content extraction thus proving to be time consuming.