# Transformers with multi-modal features and post-fusion context for e-commerce session-based recommendation

arXiv: https://arxiv.org/abs/2107.05124 · HTML: https://mlsanity.com/paper/2107.05124/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 45 references that are in the map.
- First posted on arXiv 11 Jul 2021.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding](https://mlsanity.com/paper/1809.07426/) (2018)
- [Recurrent Neural Networks with Top-k Gains for Session-based Recommendations](https://mlsanity.com/paper/1706.03847/) (2017)
- [A Simple Convolutional Generative Network for Next Item Recommendation](https://mlsanity.com/paper/1808.05163/) (2018)
- [Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling](https://mlsanity.com/paper/1611.01462/) (2016)
- [Evaluation of Session-based Recommendation Algorithms](https://mlsanity.com/paper/1803.09587/) (2018)
- [Empirical Analysis of Session-Based Recommendation Algorithms](https://mlsanity.com/paper/1910.12781/) (2019)
- [News Session-Based Recommendations using Deep Neural Networks](https://mlsanity.com/paper/1808.00076/) (2018)
- [Improving End-to-End Sequential Recommendations with Intent-aware Diversification](https://mlsanity.com/paper/1908.10171/) (2019)
- [Personalized News Recommendation with Context Trees](https://mlsanity.com/paper/1303.0665/) (2013)
- [Memory Augmented Neural Model for Incremental Session-based Recommendation](https://mlsanity.com/paper/2005.01573/) (2020)
- [Contextual Hybrid Session-based News Recommendation with Recurrent Neural Networks](https://mlsanity.com/paper/1904.10367/) (2019)
- [Rethinking Item Importance in Session-based Recommendation](https://mlsanity.com/paper/2005.04456/) (2020)
- [BERT Goes Shopping: Comparing Distributional Models for Product Representations](https://mlsanity.com/paper/2012.09807/) (2020)
- [Shopping in the Multiverse: A Counterfactual Approach to In-Session Attribution](https://mlsanity.com/paper/2007.10087/) (2020)
- [Session-based Recommendation with Self-Attention Networks](https://mlsanity.com/paper/2102.01922/) (2021)
- [CHAMELEON: A Deep Learning Meta-Architecture for News Recommender Systems [Phd. Thesis]](https://mlsanity.com/paper/2001.04831/) (2019)

## Built on by (most cited first, 7 of 7)

- [NV-Retriever: Improving text embedding models with effective hard-negative mining](https://mlsanity.com/paper/2407.15831/) (2024)
- [M2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based Recommendations](https://mlsanity.com/paper/2209.11824/) (2022)
- [Beyond NDCG: behavioral testing of recommender systems with RecList](https://mlsanity.com/paper/2111.09963/) (2021)
- [Efficient model compression with Random Operation Access Specific Tile (ROAST) hashing](https://mlsanity.com/paper/2207.10702/) (2022)
- [TIMeSynC: Temporal Intent Modelling with Synchronized Context Encodings for Financial Service Applications](https://mlsanity.com/paper/2410.12825/) (2024)
- [Hierarchical Multi-Task Learning Framework for Session-based Recommendations](https://mlsanity.com/paper/2309.06533/) (2023)
- [Modeling and Analyzing the Influence of Non-Item Pages on Sequential Next-Item Prediction](https://mlsanity.com/paper/2408.15953/) (2024)

## Abstract (arXiv)

Session-based recommendation is an important task for e-commerce services, where a large number of users browse anonymously or may have very distinct interests for different sessions. In this paper we present one of the winning solutions for the Recommendation task of the SIGIR 2021 Workshop on E-commerce Data Challenge. Our solution was inspired by NLP techniques and consists of an ensemble of two Transformer architectures - Transformer-XL and XLNet - trained with autoregressive and autoencoding approaches. To leverage most of the rich dataset made available for the competition, we describe how we prepared multi-model features by combining tabular events with textual and image vectors. We also present a model prediction analysis to better understand the effectiveness of our architectures for the session-based recommendation.