Publication statistics

Pub. period:2003-2011
Pub. count:7
Number of co-authors:7



Co-authors

Number of publications with 3 favourite co-authors:

Panagiotis G. Ipeirotis:3
Beibei Li:2
Sha Yang:2

 

 

Productive colleagues

Anindya Ghose's 3 most productive colleagues in number of publications:

Ramayya Krishnan:19
Tridas Mukhopadhya..:16
Panagiotis G. Ipei..:12
 
 
 

Upcoming Courses

go to course
User-Centred Design - Module 3
69% booked. Starts in 26 days
 
 

Featured chapter

Marc Hassenzahl explains the fascinating concept of User Experience and Experience Design. Commentaries by Don Norman, Eric Reiss, Mark Blythe, and Whitney Hess

User Experience and Experience Design !

 
 

Our Latest Books

 
 
The Social Design of Technical Systems: Building technologies for communities. 2nd Edition
by Brian Whitworth and Adnan Ahmad
start reading
 
 
 
 
Gamification at Work: Designing Engaging Business Software
by Janaki Mythily Kumar and Mario Herger
start reading
 
 
 
 
The Social Design of Technical Systems: Building technologies for communities
by Brian Whitworth and Adnan Ahmad
start reading
 
 
 
 
The Encyclopedia of Human-Computer Interaction, 2nd Ed.
by Mads Soegaard and Rikke Friis Dam
start reading
 
 

Anindya Ghose

Add description
Rename / change spelling
Add publication
 

Publications by Anindya Ghose (bibliography)

 what's this?
2011
 
Edit | Del

Li, Beibei, Ghose, Anindya and Ipeirotis, Panagiotis G. (2011): Towards a theory model for product search. In: Proceedings of the 2011 International Conference on the World Wide Web 2011. pp. 327-336.

With the growing pervasiveness of the Internet, online search for products and services is constantly increasing. Most product search engines are based on adaptations of theoretical models devised for information retrieval. However, the decision mechanism that underlies the process of buying a product is different than the process of locating relevant documents or objects. We propose a theory model for product search based on expected utility theory from economics. Specifically, we propose a ranking technique in which we rank highest the products that generate the highest surplus, after the purchase. In a sense, the top ranked products are the "best value for money" for a specific user. Our approach builds on research on "demand estimation" from economics and presents a solid theoretical foundation on which further research can build on. We build algorithms that take into account consumer demographics, heterogeneity of consumer preferences, and also account for the varying price of the products. We show how to achieve this without knowing the demographics or purchasing histories of individual consumers but by using aggregate demand data. We evaluate our work, by applying the techniques on hotel search. Our extensive user studies, using more than 15,000 user-provided ranking comparisons, demonstrate an overwhelming preference for the rankings generated by our techniques, compared to a large number of existing strong state-of-the-art baselines.

© All rights reserved Li et al. and/or ACM Press

 
Edit | Del

Li, Beibei, Ghose, Anindya and Ipeirotis, Panagiotis G. (2011): A demo search engine for products. In: Proceedings of the 2011 International Conference on the World Wide Web 2011. pp. 233-236.

Most product search engines today build on models of relevance devised for information retrieval. However, the decision mechanism that underlies the process of buying a product is different than the process of locating relevant documents or objects. We propose a theory model for product search based on expected utility theory from economics. Specifically, we propose a ranking technique in which we rank highest the products that generate the highest surplus, after the purchase. We instantiate our research by building a demo search engine for hotels that takes into account consumer heterogeneous preferences, and also accounts for the varying hotel price. Moreover, we achieve this without explicitly asking the preferences or purchasing histories of individual consumers but by using aggregate demand data. This new ranking system is able to recommend consumers products with "best value for money" in a privacy-preserving manner. The demo is accessible at http://nyuhotels.appspot.com/

© All rights reserved Li et al. and/or ACM Press

2008
 
Edit | Del

Ghose, Anindya and Yang, Sha (2008): Analyzing search engine advertising: firm behavior and cross-selling in electronic markets. In: Proceedings of the 2008 International Conference on the World Wide Web 2008. pp. 219-226.

The phenomenon of sponsored search advertising is gaining ground as the largest source of revenues for search engines. Firms across different industries have are beginning to adopt this as the primary form of online advertising. This process works on an auction mechanism in which advertisers bid for different keywords, and final rank for a given keyword is allocated by the search engine. But how different are firm's actual bids from their optimal bids? Moreover, what are other ways in which firms can potentially benefit from sponsored search advertising? Based on the model and estimates from prior work [10], we conduct a number of policy simulations in order to investigate to what extent an advertiser can benefit from bidding optimally for its keywords. Further, we build a Hierarchical Bayesian modeling framework to explore the potential for cross-selling or spillovers effects from a given keyword advertisement across multiple product categories, and estimate the model using Markov Chain Monte Carlo (MCMC) methods. Our analysis suggests that advertisers are not bidding optimally with respect to maximizing profits. We conduct a detailed analysis with product level variables to explore the extent of cross-selling opportunities across different categories from a given keyword advertisement. We find that there exists significant potential for cross-selling through search keyword advertisements in that consumers often end up buying products from other categories in addition to the product they were searching for. Latency (the time it takes for consumer to place a purchase order after clicking on the advertisement) and the presence of a brand name in the keyword are associated with consumer spending on product categories that are different from the one they were originally searching for on the Internet.

© All rights reserved Ghose and Yang and/or ACM Press

2007
 
Edit | Del

Ghose, Anindya and Ipeirotis, Panagiotis G. (2007): Designing novel review ranking systems: predicting the usefulness and impact of reviews. In: Gini, Maria L., Kauffman, Robert J., Sarppo, Donna, Dellarocas, Chrysanthos and Dignum, Frank (eds.) Proceedings of the 9th International Conference on Electronic Commerce - ICEC 2007 August 19-22, 2007, Minneapolis, MN, USA. pp. 303-310.

 
Edit | Del

Ghose, Anindya and Yang, Sha (2007): An empirical analysis of paid placement in online advertising. In: Gini, Maria L., Kauffman, Robert J., Sarppo, Donna, Dellarocas, Chrysanthos and Dignum, Frank (eds.) Proceedings of the 9th International Conference on Electronic Commerce - ICEC 2007 August 19-22, 2007, Minneapolis, MN, USA. pp. 95-96.

2005
 
Edit | Del

Ghose, Anindya, Telang, Rahul and Krishnan, Ramayya (2005): Welfare Implications of Secondary Electronic Markets. In: HICSS 2005 - 38th Hawaii International Conference on System Sciences 3-6 January, 2005, Big Island, HI, USA. .

2003
 
Edit | Del

Ghose, Anindya, Mukhopadhyay, Tridas and Rajan, Uday (2003): Strategic benefits of Internet referral services. In: Sadeh, Norman M., Dively, Mary Jo, Kauffman, Robert J., Labrou, Yannis, Shehory, Onn, Telang, Rahul and Cranor, Lorrie Faith (eds.) Proceedings of the 5th International Conference on Electronic Commerce - ICEC 2003 September 30 - October 03, 2003, Pittsburgh, Pennsylvania, USA. pp. 240-247.

 
Add publication
Show list on your website
 

Join our community and advance:

Your
Skills

Your
Network

Your
Career

 
Join our community!
 
 
 

Changes to this page (author)

18 Apr 2011: Modified
18 Apr 2011: Modified
09 Jul 2009: Modified
12 Jun 2009: Modified
30 May 2009: Modified
30 May 2009: Modified
30 May 2009: Added

Page Information

Page maintainer: The Editorial Team
URL: http://www.interaction-design.org/references/authors/anindya_ghose.html

Publication statistics

Pub. period:2003-2011
Pub. count:7
Number of co-authors:7



Co-authors

Number of publications with 3 favourite co-authors:

Panagiotis G. Ipeirotis:3
Beibei Li:2
Sha Yang:2

 

 

Productive colleagues

Anindya Ghose's 3 most productive colleagues in number of publications:

Ramayya Krishnan:19
Tridas Mukhopadhya..:16
Panagiotis G. Ipei..:12
 
 
 

Upcoming Courses

go to course
User-Centred Design - Module 3
69% booked. Starts in 26 days
 
 

Featured chapter

Marc Hassenzahl explains the fascinating concept of User Experience and Experience Design. Commentaries by Don Norman, Eric Reiss, Mark Blythe, and Whitney Hess

User Experience and Experience Design !

 
 

Our Latest Books

 
 
The Social Design of Technical Systems: Building technologies for communities. 2nd Edition
by Brian Whitworth and Adnan Ahmad
start reading
 
 
 
 
Gamification at Work: Designing Engaging Business Software
by Janaki Mythily Kumar and Mario Herger
start reading
 
 
 
 
The Social Design of Technical Systems: Building technologies for communities
by Brian Whitworth and Adnan Ahmad
start reading
 
 
 
 
The Encyclopedia of Human-Computer Interaction, 2nd Ed.
by Mads Soegaard and Rikke Friis Dam
start reading