  Most of the existing recommender systems assume that user's visiting history
can be constantly recorded. However, in recent online services, the user
identification may be usually unknown and only limited online user behaviors
can be used. It is of great importance to model the temporal online user
behaviors and conduct recommendation for the anonymous users. In this paper, we
propose a list-wise deep neural network based architecture to model the limited
user behaviors within each session. To train the model efficiently, we first
design a session embedding method to pre-train a session representation, which
incorporates different kinds of user search behaviors such as clicks and views.
Based on the learnt session representation, we further propose a list-wise
ranking model to generate the recommendation result for each anonymous user
session. We conduct quantitative experiments on a recently published dataset
from an e-commerce company. The evaluation results validate the effectiveness
of the proposed method, which can outperform the state-of-the-art
significantly.
