Tianchi-Brick and Mortar Store Recommendation with Budget Constraints
Ranked 5th – Tianchi Competition (2016) “Brick and Mortar Store Recommendation with Budget Constraints” (IJCAI Socinf 2016-New York,USA)(Alibaba Group) Among 1122 teams from 267 universities, research institutions and companies in 21 countries.
Brick-and-Mortar Store Recommendation with Budget Constraints
IJCAI (http://ijcai-16.org/) is a leading international conference in AI area, and often organizes various activities to promote AI innovations, e.g. hosting a sequence of competitions. In this year, Alibaba Group and Ant Financial Service Group will hold a data mining competition of location base services, in cooperation with IJCAI workshop SocInf 2016 (http://socinf2016.isistan.unicen.edu.ar/).
Background
As mobile devices become ubiquitous in our daily life, location based services (LBS) have become increasingly more important. People are getting more comfortable sharing their real-time locations with various location-based services, such as navigation, car ride hailing, restaurant/hotel booking, etc. As a result, huge amount of user data has been accumulated, which ignites the excitement from the machine learning/data mining community to join in force revealing the magic in the Pandora’s box of our daily life, where the high dimension time-space complexity needs to be explored.
In this contest, we will focus on nearby store recommendation when users enter new areas they rarely visited in the past. The contest has two novelties:
First, you are supposed to investigate whether the correlation between online and on-site preference helps in recommending nearby stores. Alibaba Group owns Taobao.com and Tmall.com, the largest online retail platforms in China, serves for more than 10 million merchants and over 300 million customers. Meanwhile, the Ant financial’s Alipay offers restaurant and retail store recommendation and payment services, named Koubei, for a number of customers. A user enjoying services provided by these two groups often has a unified online account. While Taobao and Tmall have run for many years and accumulate vast consumers’ behavior data, the nearby recommendation/payment services provided by Alipay are relatively new thus with less data.
Second, a set of budget constraints is imposed on the recommender system, for example, due to service capacity or number of coupons available at the stores.
As far as we know, such contest setting is novel to the research community, though it is critically important to the blooming location based business
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