Hybrid deep modelling with human knowledge in practical e-commerce search

Abstract

In recent years, recommender systems have become increasingly important in the E-commerce marketplaces (e.g., Alibaba, Amazon). According to public information, recommender systems have contributed 30% gross merchandise value (GMV) for Amazon. Significant effort has been devoted into this research field by the industry and the academic community. Research works making key advances in recommendation systems can be divided into matrix factorization (MF), collaborative filtering (CF) and click-through rate (CTR) prediction. Currently, in industrial e-commerce recommendation systems, the main adopted approach is to use CF methods for performing recall tasks and deep learning-based CTR prediction methods for performing ranking tasks. However, significant challenges remain when these algorithms are to be deployed into practical e-commerce environments. In the system of search, most of the ctr modeling use big data, with a large number of user behavior data. The amount of data is a large number, which is very helpful to build the model. At the same time, these data also contains a lot of noise. This can lead to inaccurate modeling. In many cases, these behaviors are not consistent with business needs, we need to make manual guidance and intervention to the model.