  Most existing recommender systems leverage user behavior data of one type
only, such as the purchase behavior in E-commerce that is directly related to
the business KPI (Key Performance Indicator) of conversion rate. Besides the
key behavioral data, we argue that other forms of user behaviors also provide
valuable signal, such as views, clicks, adding a product to shop carts and so
on. They should be taken into account properly to provide quality
recommendation for users. In this work, we contribute a new solution named NMTR
(short for Neural Multi-Task Recommendation) for learning recommender systems
from user multi-behavior data. We develop a neural network model to capture the
complicated and multi-type interactions between users and items. In particular,
our model accounts for the cascading relationship among different types of
behaviors (e.g., a user must click on a product before purchasing it). To fully
exploit the signal in the data of multiple types of behaviors, we perform a
joint optimization based on the multi-task learning framework, where the
optimization on a behavior is treated as a task. Extensive experiments on two
real-world datasets demonstrate that NMTR significantly outperforms
state-of-the-art recommender systems that are designed to learn from both
single-behavior data and multi-behavior data. Further analysis shows that
modeling multiple behaviors is particularly useful for providing recommendation
for sparse users that have very few interactions.
