Google Search in India: Unveiling the Geo-Personalized Web
Authors: 
Pranav Chatur, Naman Jain, Varun Trivedi, Ayush Kumar Dhoot, Sakthi Balan MuthiahAuthors Info & Claims
Pranav Chatur, Naman Jain, Varun Trivedi, Ayush Kumar Dhoot, Sakthi Balan MuthiahAuthors Info & ClaimsPages 403 - 411
Abstract
The ubiquitous presence of search engines has revolutionized the way people access information. Google, as the dominant search engine worldwide, plays a pivotal role in shaping information retrieval experiences. It employs personalized search algorithms to deliver tailored search results based on each user’s preferences. Despite numerous studies on general personalization in search engines, there is limited research on geolocation-driven personalization in search engine results, particularly in India. This research paper aims to quantitatively analyze and assess the impact of geolocation on personalized search results within the context of India. To conduct this study, we have selected an extensive set of search queries across various domains. Multiple geolocations within India were chosen to represent different regions, cities, and rural areas. Using a systemic methodology, we collected and analyzed search results for each query, keeping the user’s geolocation as a variable. The study focuses on the extent of personalization introduced by Google’s search algorithms in search result rankings based on geolocation. The findings indicate that personalization influences search results, though the degree of variation depends on the specific search query category and result ranking. Queries regarding popular or local items show higher personalization, while within-state personalization is more elevated in larger states or cities with cosmopolitan populations. This research paves the way for fostering a deeper understanding of the implications of geolocation-driven search result personalization.
References
[1]
2009. WWW ’09: Proceedings of the 18th International Conference on World Wide Web (Madrid, Spain). Association for Computing Machinery, New York, NY, USA.
[2]
Leonardo Andrade and Mário Silva. 2006. Relevance Ranking for Geographic IR.
[3]
Pranav Chatur. 2022. GitHub - DarkMenacer/Google-Geolocation-Bias: Tool to scrape web links for different queries from different cities across the world, all from your home — github.com. https://github.com/DarkMenacer/Google-Geolocation-Bias. [Accessed 13-Jul-2023].
[4]
Changyao Chen. 2021. rbo 0.1.3. Retrieved Jan 31, 2023 from https://pypi.org/project/rbo
[5]
Zhicheng Dou, Ruihua Song, and Ji-Rong Wen. 2007. A large-scale evaluation and analysis of personalized search strategies. 16th International World Wide Web Conference, WWW2007, 581–590. https://doi.org/10.1145/1242572.1242651
[6]
Sharad Goel, Jake Hofman, and M. Sirer. 2021. Who Does What on the Web: A Large-Scale Study of Browsing Behavior. Proceedings of the International AAAI Conference on Web and Social Media 6, 1 (Aug. 2021), 130–137. https://doi.org/10.1609/icwsm.v6i1.14266
[7]
Google Ads API 2023. Geo targets. Retrieved Nov 27, 2023 from https://developers.google.com/google-ads/api/data/geotargets
[8]
Government of India, Ministry of Finance, Department of Expenditure 2015. Re-classification/Upgradation of Cities/Towns on the basis of Census-2011 for the purpose of grant of House Rent. Allowance(HRA) to Central Government employees.Retrieved May 27, 2017 from https://doe.gov.in/sites/default/files/21-07-2015.pdf
[9]
Aniko Hannak, Piotr Sapiezynski, Arash Molavi Kakhki, Balachander Krishnamurthy, David Lazer, Alan Mislove, and Christo Wilson. 2013. Measuring Personalization of Web Search. In Proceedings of the 22nd International Conference on World Wide Web (Rio de Janeiro, Brazil) (WWW ’13). Association for Computing Machinery, New York, NY, USA, 527–538. https://doi.org/10.1145/2488388.2488435
[10]
Jian Hu, Hua-Jun Zeng, Hua Li, Cheng Niu, and Zheng Chen. 2007. Demographic prediction based on user’s browsing behavior. 16th International World Wide Web Conference, WWW2007, 151–160. https://doi.org/10.1145/1242572.1242594
[11]
IAMAI. 2022. KANTAR, Internet in India 2022. Retrieved Sept 2, 2022 from https://www.iamai.in/sites/default/files/research/Internet%20in%20India%202022_Print%20version.pdf
[12]
indiacode. 2022. Legal Driving Age. Retrieved Nov 2, 2022 from https://www.indiacode.nic.in/show-data?actid=AC_CEN_30_42_00009_198859_1517807326286&orderno=6#: :text=(1)%20No%20person%20under%20the,the%20age%20of%20sixteen%20years
[13]
Chloe Kliman-Silver, Aniko Hannak, David Lazer, Christo Wilson, and Alan Mislove. 2015. Location, Location, Location: The Impact of Geolocation on Web Search Personalization. In Proceedings of the 2015 Internet Measurement Conference (Tokyo, Japan) (IMC ’15). Association for Computing Machinery, New York, NY, USA, 121–127. https://doi.org/10.1145/2815675.2815714
[14]
Fang Liu, Clement Yu, and Weiyi Meng. 2002. Personalized Web search by mapping user queries to categories. International Conference on Information and Knowledge Management, Proceedings, 558–565. https://doi.org/10.1145/584792.584884
[15]
Maryam Mohsin. 2022. GOOGLE SEARCH STATISTICS YOU NEED TO KNOW IN 2023. Retrieved Dec 31, 2022 from https://www.oberlo.com/blog/google-search-statistics
[16]
Eli Pariser. 2011. The Filter Bubble: What the Internet Is Hiding from You. Penguin Group, The.
[17]
James Pitkow, Hinrich Schütze, Todd Cass, Rob Cooley, Don Turnbull, Andy Edmonds, Eytan Adar, and Thomas Breuel. 2002. Personalized Search. Commun. ACM 45, 9 (sep 2002), 50–55. https://doi.org/10.1145/567498.567526
[18]
Population density 2022. Population Density. Retrieved Sept 27, 2022 from https://www.indiacensus.net/density.php
[19]
Alexander Pretschner and Susan Gauch. 1999. Ontology based personalized search. 391 – 398. https://doi.org/10.1109/TAI.1999.809829
[20]
Princenihith. 2020. Maps with Python. https://github.com/Princenihith/Maps_with_python. Accessed on 28 May 2023.
[21]
Feng Qiu and Junghoo Cho. 2006. Automatic identification of user interest for personalized search. Proceedings of the 15th international conference on World Wide Web, 727–736. https://doi.org/10.1145/1135777.1135883
[22]
Kevin Richard. 2020. Geolocation: The Ultimate Tip to Emulate Local Search. Retrieved Nov 16, 2022 from https://moz.com/blog/geolocation-the-ultimate-tip-to-emulate-local-search
[23]
Xuehua Shen, Bin Tan, and ChengXiang Zhai. 2005. Implicit User Modeling for Personalized Search. International Conference on Information and Knowledge Management, Proceedings (10 2005). https://doi.org/10.1145/1099554.1099747
[24]
Jian-Tao Sun, Hua-Jun Zeng, Huan Liu, Yuchang Lu, and Zheng Chen. 2005. CubeSVD: A novel approach to personalized Web search. Proceedings of the 14th International Conference on World Wide Web, 382–390. https://doi.org/10.1145/1060745.1060803
[25]
Bin Tan, Xuehua Shen, and ChengXiang Zhai. 2006. Mining long-term search history to improve search accuracy. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2006, 718–723. https://doi.org/10.1145/1150402.1150493
[26]
Jaime Teevan, Susan Dumais, and Eric Horvitz. 2005. Personalizing search via automated analysis of interests and activities. 449–456. https://doi.org/10.1145/1076034.1076111
[27]
vikaspedia. 2022. National Famers day. Retrieved Nov 2, 2022 from https://vikaspedia.in/agriculture/agri-directory/important-days/national-farmers-day#: :text=National%20Farmers’%20Day%2C%20also%20known,in%20India%20on%2023%20December.&text=Your%20browser%20can’t%20play%20this%20video
[28]
Dr.N.Srinivasan V.Raju. 2021. An Efficient Analysis of Web Search Personalization Using Fuzzy Based Approach. Turkish Journal of Computer and Mathematics Education 12, 12, Article 5 (March 2021), 3753-3759 pages. https://www.turcomat.org/index.php/turkbilmat/article/view/8153/6365
[29]
William Webber, Alistair Moffat, and Justin Zobel. 2010. A Similarity Measure for Indefinite Rankings. ACM Trans. Inf. Syst. 28, 4, Article 20 (nov 2010), 38 pages. https://doi.org/10.1145/1852102.1852106
[30]
Bo Yu and Guoray Cai. 2007. A query-aware document ranking method for geographic information retrieval. 49–54. https://doi.org/10.1145/1316948.1316962
Index Terms
- Google Search in India: Unveiling the Geo-Personalized Web
Recommendations
Location, Location, Location: The Impact of Geolocation on Web Search Personalization
IMC '15: Proceedings of the 2015 Internet Measurement ConferenceTo cope with the immense amount of content on the web, search engines often use complex algorithms to personalize search results for individual users. However, personalization of search results has led to worries about the Filter Bubble Effect, where ...
Personalised Search in E-Comm Groceries
WWW '23 Companion: Companion Proceedings of the ACM Web Conference 2023Personalized Search(henceforth called P10d Search) focuses to deliver user-specific search results based on the previous purchases. Search engine retrieves the result based on the defined relevancy algorithm. When a user searches a keyword, search ...
Comments
Information & Contributors
Information
Published In
January 2024
627 pages
ISBN:9798400716348
DOI:10.1145/3632410
Copyright © 2024 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 04 January 2024
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
CODS-COMAD 2024
CODS-COMAD 2024: 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)
January 4 - 7, 2024
Bangalore, India
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 34Total Downloads
- Downloads (Last 12 months)34
- Downloads (Last 6 weeks)1
Reflects downloads up to 14 Jan 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format