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.

基于高维度轨迹信息，我们通过使用 SessionBERT 方法来识别用户画像，提供个性化的网页用户体验和推荐服务。

用户画像识别和新服务适配推荐

User Persona Identification and New Service Adaptation Recommendation

This draft paper presents a workflow for creating User Personas with Large
Language Models, using the results of a Thematic Analysis of qualitative
interviews. The proposed workflow uses improved prompting and a larger pool of
Themes, compared to previous work conducted by the author for the same task.
This is possible due to the capabilities of a recently released LLM which
allows the processing of 16 thousand tokens (GPT3.5-Turbo-16k) and also due to
the possibility to offer a refined prompting for the creation of Personas. The
paper offers details of performing Phase 2 and 3 of Thematic Analysis, and then
discusses the improved workflow for creating Personas. The paper also offers
some reflections on the relationship between the proposed process and existing
approaches to Personas such as the data-driven and qualitative Personas.
Moreover, the paper offers reflections on the capacity of LLMs to capture user
behaviours and personality traits, from the underlying dataset of qualitative
interviews used for the analysis.

使用大型语言模型创建用户人物画像的工作流程，利用定性访谈的主题分析结果，提出了一个改进的提示和更大的主题池，以及对用户人物创建的工作流程进行了改进和反思，同时讨论了 LMM 捕捉用户行为和个性特征的能力。

使用定性访谈改进 LML 编写用户画像的指引和流程：捕捉用户的行为和人格特征

Improved prompting and process for writing user personas with LLMs,  using qualitative interviews: Capturing behaviour and personality traits of  users

The goal of this paper is establishing if we can satisfactorily perform a
Thematic Analysis (TA) of semi-structured interviews using a Large Language
Model (more precisely GPT3.5-Turbo). Building on previous work by the author,
which established an embryonal process for conducting a TA with the model, this
paper will perform a further analysis and then cover the last phase of a TA
(phase 6), which entails the writing up of the result. This phase was not
covered by the previous work. In particular, the focus will be on using the
results of a TA done with the LLM on a dataset of user interviews, for writing
user personas, with the model building on the TA to produce the personas
narratives. User personas are models of real users, usually built from a data
analysis like interviews with a sample of users. User personas are tools often
used in User Centered Design processes. The paper shows that the model can
build basic user personas with an acceptable quality deriving them from themes,
and that the model can serve for the generation of ideas for user personas.

本文探讨利用大型语言模型（GPT3.5-Turbo）是否能够对半结构化面试进行主题分析（TA），并重点关注 LLM 结果如何用于用户画像的生成。结果表明，该模型可以从主题中生成基本的用户画像，并可为用户画像的生成提供想法。