mlsanityData to 7 Oct 2026

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

arXiv 2107.05124 ↗First posted 11 Jul 2021cs.IR cs.LG cs.NEcited by 7

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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.

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

Builds on

24 of 45 references.

References · 24

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12 more
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Cited by year

Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 3.

7 papers.

  1. 2024NV-Retriever: Improving text embedding models with effective hard-negative miningcited by 83
  2. 2022M2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based Recommendationscited by 9
  3. 2021Beyond NDCG: behavioral testing of recommender systems with RecListcited by 8
  4. 2022Efficient model compression with Random Operation Access Specific Tile (ROAST) hashingcited by 2
  5. 2024TIMeSynC: Temporal Intent Modelling with Synchronized Context Encodings for Financial Service Applicationscited by 2
  6. 2023Hierarchical Multi-Task Learning Framework for Session-based Recommendationscited by 1
  7. 2024Modeling and Analyzing the Influence of Non-Item Pages on Sequential Next-Item Predictioncited by 0