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.

翻译：在信息密集型网页上提供个性化用户体验有助于用户更快达成最终目标。我们探索了一种自动化方法，通过利用用户会话中网页的高维轨迹信息来识别用户画像。针对神经协同过滤方法较少关注词元语义的问题，我们的方法引入了SessionBERT——一种基于Transformer的语言模型，该模型从零开始以遮蔽语言建模目标在用户轨迹（会话中的页面、元数据、计费信息）上进行训练，旨在捕获其中的语义信息。结果表明，通过学习获得的SessionBERT表示在预测页面链接和后续服务时，其F1分数始终优于BERT-base模型，相对提升分别为3%和1%。我们利用SessionBERT进行扩展，为用户可能使用的下一个最相关服务提供推荐（top-5），推荐模型的HIT@5达到了58%。