Aggregating Product Reviews for the Chinese Market (Computer Project)

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As of December 2007, the number of Internet users in China had increased to 210 million people. The annual growth rate reached 53.3 percent in 2008, with the average number of Internet users increasing every day by 200,000 people. Currently, China’s Internet population is slightly lower than the 215 million internet users in the United States.

Despite the rapid growth of the Chinese economy in the global Internet market, China’s e-commerce is not following the traditional pattern of commerce, but instead has developed based on user demand. This growth has extended into every area of the Internet.

In the west, expert product reviews have been shown to be an important element in a user’s purchase decision. The higher the quality of product reviews that customers received, the more products they buy from on-line shops. As the number of products and options increase, Chinese customers need impersonal, impartial, and detailed products reviews. This thesis focuses on on-line product reviews and how they affect Chinese customer’s purchase decisions.

E-commerce is a complex system. As a typical model of e-commerce, we examine a Business to Consumer (B2C) on-line retail site and consider a number of factors; including some seemingly subtitle factors that may influence a customer’s eventually decision to shop on website. Specifically this thesis project will examine aggregated product reviews from different on-line sources by analyzing some existing western companies. Following this the thesis demonstrates how to aggregate product reviews for an e-business website.

During this thesis project we found that existing data mining techniques made it straight forward to collect reviews. These reviews were stored in a database and web applications can query this database to provide a user with a set of relevant product reviews. One of the important issues, just as with search engines is providing the relevant product reviews and determining what order they should be presented in. In our work we selected the reviews based upon matching the product (although in some cases there are ambiguities concerning if two products are actually identical or not) and ordering the matching reviews by date – with the most recent reviews present first.

Some of the open questions that remain for the future are: (1) improving the matching – to avoid the ambiguity concerning if the reviews are about the same product or not and (2) determining if the availability of product reviews actually affect a Chinese user’s decision to purchase a product.
Source: KTH
Author: Wu, Yongliang

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