# FinTRec: Transformer Based Unified Contextual Ads Targeting and Personalization for Financial Applications

arXiv: https://arxiv.org/abs/2511.14865 · HTML: https://mlsanity.com/paper/2511.14865/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 49 references that are in the map.
- First posted on arXiv 18 Nov 2025.

## Builds on (most cited first, 27 of 27)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [Captum: A unified and generic model interpretability library for PyTorch](https://mlsanity.com/paper/2009.07896/) (2020)
- [TabTransformer: Tabular Data Modeling Using Contextual Embeddings](https://mlsanity.com/paper/2012.06678/) (2020)
- [Deep Interest Evolution Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1809.03672/) (2018)
- [Recurrent Neural Networks with Top-k Gains for Session-based Recommendations](https://mlsanity.com/paper/1706.03847/) (2017)
- [Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction](https://mlsanity.com/paper/1905.09248/) (2019)
- [Sequential Recommendation with Graph Neural Networks](https://mlsanity.com/paper/2106.14226/) (2021)
- [PinnerFormer: Sequence Modeling for User Representation at Pinterest](https://mlsanity.com/paper/2205.04507/) (2022)
- [Grad-SAM: Explaining Transformers via Gradient Self-Attention Maps](https://mlsanity.com/paper/2204.11073/) (2022)
- [Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactions](https://mlsanity.com/paper/2007.12986/) (2020)
- [Towards Personalized and Semantic Retrieval: An End-to-End Solution for E-commerce Search via Embedding Learning](https://mlsanity.com/paper/2006.02282/) (2020)
- [Scaling User Modeling: Large-scale Online User Representations for Ads Personalization in Meta](https://mlsanity.com/paper/2311.09544/) (2023)
- [KuaiSAR: A Unified Search And Recommendation Dataset](https://mlsanity.com/paper/2306.07705/) (2023)
- [FATA-Trans: Field And Time-Aware Transformer for Sequential Tabular Data](https://mlsanity.com/paper/2310.13818/) (2023)
- [One Transformer for All Time Series: Representing and Training with Time-Dependent Heterogeneous Tabular Data](https://mlsanity.com/paper/2302.06375/) (2023)
- [Learning Multi-touch Conversion Attribution with Dual-attention Mechanisms for Online Advertising](https://mlsanity.com/paper/1808.03737/) (2018)
- [Structured Self-Attention Weights Encode Semantics in Sentiment Analysis](https://mlsanity.com/paper/2010.04922/) (2020)
- [USER: A Unified Information Search and Recommendation Model based on Integrated Behavior Sequence](https://mlsanity.com/paper/2109.15012/) (2021)
- [Modeling Users' Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search](https://mlsanity.com/paper/2203.15542/) (2022)
- [Graph and Sequential Neural Networks in Session-based Recommendation: A Survey](https://mlsanity.com/paper/2408.14851/) (2024)
- [UnifiedSSR: A Unified Framework of Sequential Search and Recommendation](https://mlsanity.com/paper/2310.13921/) (2023)
- [Unified Generative Search and Recommendation](https://mlsanity.com/paper/2504.05730/) (2025)
- [Dynamic Customer Embeddings for Financial Service Applications](https://mlsanity.com/paper/2106.11880/) (2021)
- [Time-aligned Exposure-enhanced Model for Click-Through Rate Prediction](https://mlsanity.com/paper/2308.09966/) (2023)
- [Joint Modeling of Search and Recommendations Via an Unified Contextual Recommender (UniCoRn)](https://mlsanity.com/paper/2408.10394/) (2024)

## Built on by (most cited first, 4 of 4)

- [REPREC: Representation Driven Parameter-Efficient Recommendation System](https://mlsanity.com/paper/2607.24845/) (2026)
- [SAGA: Structure-Attended Generative Action Embedding Model that encodes Multi-Surface User Action Sequences](https://mlsanity.com/paper/2608.15429/) (2026)
- [A Systematic Benchmark of Explainable Methods for Temporal Attribution in Sequential Recommendation Systems](https://mlsanity.com/paper/2609.27201/) (2026)
- [DP-Rec: Towards Dynamic Patching for Efficient Long-Sequence Recommendation](https://mlsanity.com/paper/2609.32215/) (2026)

## Abstract (arXiv)

Transformer-based architectures are widely adopted in sequential recommendation systems, yet their application in Financial Services (FS) presents distinct practical and modeling challenges for real-time recommendation. These include:a) long-range user interactions (implicit and explicit) spanning both digital and physical channels generating temporally heterogeneous context, b) the presence of multiple interrelated products require coordinated models to support varied ad placements and personalized feeds, while balancing competing business goals. We propose FinTRec, a transformer-based framework that addresses these challenges and its operational objectives in FS. While tree-based models have traditionally been preferred in FS due to their explainability and alignment with regulatory requirements, our study demonstrate that FinTRec offers a viable and effective shift toward transformer-based architectures. Through historic simulation and live A/B test correlations, we show FinTRec consistently outperforms the production-grade tree-based baseline. The unified architecture, when fine-tuned for product adaptation, enables cross-product signal sharing, reduces training cost and technical debt, while improving offline performance across all products. To our knowledge, this is the first comprehensive study of unified sequential recommendation modeling in FS that addresses both technical and business considerations.