Utilizing Language Models for Tour Itinerary Recommendation

TL;DR

This paper explores the application of language models, including word embeddings and transformers, to improve personalized tour itinerary recommendations by addressing both relevance and constraint satisfaction.

Contribution

It introduces novel use of language models for integrating POI relevance and itinerary planning in a unified framework.

Findings

Language models can effectively generate personalized itineraries.

Embedding techniques improve POI relevance in recommendations.

Transformer models help satisfy itinerary constraints.

Abstract

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.