The next Point of Interest (POI) recommendation task is to predict users'
immediate next POI visit given their historical data. Location-Based Social
Network (LBSN) data, which is often used for the next POI recommendation task,
comes with challenges. One frequently disregarded challenge is how to
effectively use the abundant contextual information present in LBSN data.
Previous methods are limited by their numerical nature and fail to address this
challenge. In this paper, we propose a framework that uses pretrained Large
Language Models (LLMs) to tackle this challenge. Our framework allows us to
preserve heterogeneous LBSN data in its original format, hence avoiding the
loss of contextual information. Furthermore, our framework is capable of
comprehending the inherent meaning of contextual information due to the
inclusion of commonsense knowledge. In experiments, we test our framework on
three real-world LBSN datasets. Our results show that the proposed framework
outperforms the state-of-the-art models in all three datasets. Our analysis
demonstrates the effectiveness of the proposed framework in using contextual
information as well as alleviating the commonly encountered cold-start and
short trajectory problems.

利用预训练的大型语言模型处理 POI 推荐任务中的丰富背景信息，提出的框架在三个真实世界 LBSN 数据集上表现优于现有模型，有效解决了冷启动和短轨迹问题。

大型语言模型用于下一次兴趣点推荐

Large Language Models for Next Point-of-Interest Recommendation

In this work, we predict the user lifetime within the anonymous and
location-based social network Jodel in the Kingdom of Saudi Arabia. Jodel's
location-based nature yields to the establishment of disjoint communities
country-wide and enables for the first time the study of user lifetime in the
case of a large set of disjoint communities. A user's lifetime is an important
measurement for evaluating and steering customer bases as it can be leveraged
to predict churn and possibly apply suitable methods to circumvent potential
user losses. We train and test off the shelf machine learning techniques with
5-fold crossvalidation to predict user lifetime as a regression and
classification problem; identifying the Random Forest to provide very strong
results. Discussing model complexity and quality trade-offs, we also dive deep
into a time-dependent feature subset analysis, which does not work very well;
Easing up the classification problem into a binary decision (lifetime longer
than timespan $x$) enables a practical lifetime predictor with very good
performance. We identify implicit similarities across community models
according to strong correlations in feature importance. A single countrywide
model generalizes the problem and works equally well for any tested community;
the overall model internally works similar to others also indicated by its
feature importances.

本文旨在预测沙特阿拉伯 Jodel 匿名定位社交网络内用户的生命周期，并通过机器学习技术中的随机森林模型建立二元分类实现良好的预测效果。