Utilizing Language Models for Tour Itinerary Recommendation
Ngai Lam Ho Kwan Hui Lim
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.