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Online and Offline integrated recommendation system

IP.com Disclosure Number: IPCOM000234822D
Publication Date: 2014-Feb-10
Document File: 5 page(s) / 129K

Publishing Venue

The IP.com Prior Art Database

Abstract

An online and offline integrated recommendation system has been proposed, which combines online and offline user buying data for user behavior analysis. It also consider the location data, trajectory data for generating the final recommendation list by best choice for specific person and where to buy.

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Online and Offline integrated recommendation system

Many customers often try some commodities such as clothes and shoes and cell phone, in stores but buy them on web store. Why buy on web stores: Cheaper and convenience: send home without going to store. But customers need to ask two question: 1) whether this commodity need to try in stores but buy on web; 2) where to try this commodity.

Our method basically consists of three steps: 1.Collecting data (not static, it is active): store place, product properties, customer preference; 2. If input something: No, show him/her on-line store discount and his/her favor store discount Yes, differentation product: product*customer*place, same product: product*customer*place. 3. Machine learning& Data mining method deal with the three aspect information: product*customer*place. The final output is a recommand list and order by best choice for specific person and where to buy.

Usage Scenarios: output is a recommand list and order by best choice for specific person -For differentiation product, guide customer buy it on suitable place.
-For the normal same product, guide specific customer buy it on suitable place.
-For the specific same product, guide customer buy it on suiteable place
-For the specific customer and product, show him/her suiteable place to buy.
-Solving crowding on special festival day(11, Hongkong shopping festival)

Benefit for sellers:
-For O&O company
-Better shopping experence, better profit
-No matter online or...