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User recommendations influential in online shopping

Ali Dehghan, assistant professor of IT and business analytics, discusses the recommendation systems used by online retailers Tuesday in the Quay Davis Boardroom. Dehghan explained the differences between recommendations based on user reviews and recommendations generated by an algorithm. / photo by Nareg Agopian
Ali Dehghan, assistant professor of IT and business analytics, discusses the recommendation systems used by online retailers Tuesday in the Quay Davis Boardroom. Dehghan explained the differences between recommendations based on user reviews and recommendations generated by an algorithm. / photo by Nareg Agopian

Sheridan Lambrook
Staff Writer

Assistant Professor of IT and Business Analytics Ali Dehghan presented his latest research, “Developing a Product Network Through User-Generated Recommendations Vs. System-Generated Recommendations on Commercial Online Platform”, Tuesday afternoon in the Quay Davis Executive Boardroom before a small gathering of ULV faculty and students. 

In this technological era of holidays in honor of online shopping, how well do we know the statistics and algorithm behind it, Dehgan asked. When searching for products, is it more tempting when they are at the top, or reviewed highly by customers?

Dehghan collaborated with his colleague, Moez Farrokhia of the University of North Carolina, for the paper. 

Dehghan discussed the difference between user-generated recommendations and system-generated recommendations. 

The rise of online advertising platforms has been an intriguing development for the retail industry in recent years, he said. Companies pay groups like Google and Amazon to advertise the product as one of the first to appear in searches.

For example, Dehghan used Amazon to search for a Samsung TV, which may appear at the top of the page as “sponsored,”  or system-generated, while the “overall pick” is based on customer or user-generated recommendations. 

Unlike previous research on the subject, Dehghan’s study looks at how a network of products change over time depending on the average recommendation.

“We argue that not all products are contributing to the evolution of the networks’ equally,” Dehghan said. “But import products (with better ratings) have more impact on the establishment and formation of links or selection of existing links among products.”

For their research, the duo purchased a random selection of 50 books from Amazon. The 50 books are equivalent to “nodes,” generating 700 different links.

Thanks to features like product comparison, user-generated reviews and additional information about products that are hard to find through traditional channels, online platforms have made the process of searching and buying easier.

Dehghan said that more than 2,005 American consumers reported in 2012 that they had chosen a brand or product that they had not initially considered in more than 65% of their shopping endeavors. 

Dean of College of Business Emmeline de Pillis who attended the talk noted how the advertised products are ultimately losing money, creating a what if scenario. 

“What (you) should be doing is finding the best selling network science book and telling all (your) friends to buy that network science book and my book at the same time,” de Pillis said.

“In this preliminary study, we show that the user-generated recommendations are highly influential on raw evolution of networks,” Dehghan said. 

Dehghan added that while the impact of recommenders on product demand has been examined, the question of how these recommenders’ network-driven attributions affect the predictability of product demand remains unanswered.

Sheridan Lambrook can be reached at sheridan.grenda@laverne.edu.

Sheridan Lambrook and Nareg Agopian
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Sheridan Lambrook, a senior journalism major with a concentration in visual journalism, is photography editor and a staff writer for the Campus Times.

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Nareg Agopian is a staff photographer for the Campus Times and La Verne Magazine.

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