Tour itinerary recommendation involves planning a sequence of relevant Point-of-Interest (POIs), which combines challenges from the fields of both Operations Research (OR) and Recommendation Systems (RS). As an OR problem, there is the need to maximize a certain utility (e.g., popularity of POIs in the tour) while adhering to some constraints (e.g., maximum time for the tour). As a RS problem, it is heavily related to problem or filtering or ranking a subset of POIs that are relevant to a user and recommending it as part of an itinerary. In this paper, we explore the use of language models for the task of tour itinerary recommendation and planning. This task has the unique requirement of recommending personalized POIs relevant to users and planning these POIs as an itinerary that satisfies various constraints. We discuss some approaches in this area, such as using word embedding techniques like Word2Vec and GloVe for learning POI embeddings and transformer-based techniques like BERT for generating itineraries.

翻译：旅游行程推荐涉及规划一系列相关兴趣点（POIs）的序列，该任务融合了运筹学（OR）和推荐系统（RS）两大领域的挑战。作为运筹学问题，需要在遵守某些约束条件（如行程最大时间）的同时最大化特定效用（如旅游行程中POI的受欢迎程度）。作为推荐系统问题，该任务与从兴趣点集合中筛选或排序用户相关子集并将其作为行程组成部分进行推荐密切相关。本文探讨了语言模型在旅游行程推荐与规划任务中的应用。该任务的独特要求在于推荐与用户相关的个性化POI，并将这些POI规划为满足多种约束条件的行程。我们讨论了该领域的若干方法，包括利用Word2Vec和GloVe等词嵌入技术学习POI向量表示，以及采用基于Transformer的BERT等技术生成行程。