# User Persona Identification and New Service Adaptation Recommendation

arXiv: https://arxiv.org/abs/2311.10773 · HTML: https://mlsanity.com/paper/2311.10773/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 15 Nov 2023.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [TabTransformer: Tabular Data Modeling Using Contextual Embeddings](https://mlsanity.com/paper/2012.06678/) (2020)
- [Personalized Dialogue Generation with Diversified Traits](https://mlsanity.com/paper/1901.09672/) (2019)
- [Neural Personalized Response Generation as Domain Adaptation](https://mlsanity.com/paper/1701.02073/) (2017)

## Built on by (most cited first, 2 of 2)

- [MaRVL-QA: A Benchmark for Mathematical Reasoning over Visual Landscapes](https://mlsanity.com/paper/2508.17180/) (2025)
- [Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy](https://mlsanity.com/paper/2505.21907/) (2025)

## Abstract (arXiv)

Providing a personalized user experience on information dense webpages helps users in reaching their end-goals sooner. We explore an automated approach to identifying user personas by leveraging high dimensional trajectory information from user sessions on webpages. While neural collaborative filtering (NCF) approaches pay little attention to token semantics, our method introduces SessionBERT, a Transformer-backed language model trained from scratch on the masked language modeling (mlm) objective for user trajectories (pages, metadata, billing in a session) aiming to capture semantics within them. Our results show that representations learned through SessionBERT are able to consistently outperform a BERT-base model providing a 3% and 1% relative improvement in F1-score for predicting page links and next services. We leverage SessionBERT and extend it to provide recommendations (top-5) for the next most-relevant services that a user would be likely to use. We achieve a HIT@5 of 58% from our recommendation model.