Due to technological advancement, e-commerce and mobile commerce are popular worldwide, thus creating a rising trend of online shopping demand. The high internet penetration rate allows quick information flow, which favours people shopping online. However, the problem of overspending and huge disposal creation exists because of unclear information during shopping processes and misunderstanding of product functions. Many people duplicate buying the same products as they forget what they have just now. And some people are confused about whether the products are suitable for them as they lack clear judgment information.
Therefore, we created a recommendation-decision platform, MetaChoice, which helps people make the best decisions for unfamiliar product fields and shop smart while targeting consumers who frequently shop online. For saving comparison time, MetaChoice provides information in ratings and comments instead of exact product data for users to make comparison easier. To solve overspending issues, MetaChoice will first conduct survey questions with specific product features for understanding customers' preferences and needs. By doing so, MetaChoice will summarize users' feature preferences and highlight and recommend the most suitable product choice through machine learning to avoid unnecessary purchasing. Moreover, we may also have two screening questions to help users think about whether they buy products based on needs or wants. MetaChoice will give a warning signal to users after the recommendation is made to avoid overspending.
We will keep providing up-to-date information and expand the product categories in order to engage new customers and re-engage existing customers. The subsequence operating cost, however, is estimable and controllable. After MetaChoice has gained a sufficient customer base, we will consider taking advertisements and charge for a referral fee. In order to provide a faithful recommendation to our users, the advertising would neither affect our product ranking nor the final recommendation. Instead, we would push that advertised information to the users only for their reference. After we have picked the best choice for the users, to make things convenient, the users may wish to be directed to a purchase website as well. Therefore, in later phrases, we can include a few vendors’ websites in the recommendation result. And charge the vendor for referral commission by pay-per-click mechanism.
There are source code of our mobile app development and website development in this page !!
Found the app source code through app branch
Found the web source code through web branch
@credit_to: Jacklau1216 , Leung-Kam-Ho and KwongKaLok💕