# Session-aware Information Embedding for E-commerce Product Recommendation

arXiv: https://arxiv.org/abs/1707.05955 · HTML: https://mlsanity.com/paper/1707.05955/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2017.

## Built on by (most cited first, 9 of 9)

- [A Closer Look at GAN Priors: Exploiting Intermediate Features for Enhanced Model Inversion Attacks](https://mlsanity.com/paper/2407.13863/) (2024)
- [Model Inversion Attack via Dynamic Memory Learning](https://mlsanity.com/paper/2309.00013/) (2023)
- [On the Consistency of Average Embeddings for Item Recommendation](https://mlsanity.com/paper/2308.12767/) (2023)
- [Exploiting Session Information in BERT-based Session-aware Sequential Recommendation](https://mlsanity.com/paper/2204.10851/) (2022)
- [Inferring Substitutable and Complementary Products with Knowledge-Aware Path Reasoning based on Dynamic Policy Network](https://mlsanity.com/paper/2110.03276/) (2021)
- [Sequential Recommendation in Online Games with Multiple Sequences, Tasks and User Levels](https://mlsanity.com/paper/2102.06950/) (2021)
- [Robust Recommendation with Implicit Feedback via Eliminating the Effects of Unexpected Behaviors](https://mlsanity.com/paper/2112.11023/) (2021)
- [ExCalibR: Expected Calibration of Recommendations](https://mlsanity.com/paper/2304.12311/) (2023)
- [Revisiting the Privacy Risks of Split Inference: A GAN-Based Data Reconstruction Attack via Progressive Feature Optimization](https://mlsanity.com/paper/2508.20613/) (2025)

## Abstract (arXiv)

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.