Multi-objective reinforcement learning approach for trip recommendation

Trip recommendation is an intelligent service that provides personalized itinerary plans for tourists in unfamiliar cities. It aims to construct a series of ordered POIs that maximizes user travel experiences with temporal and spatial constraints. When appending a candidate POI to the recommended trip, it is critical to capture users' dynamic preferences according to real-time context. Meanwhile, the diversity and popularity of the POIs in the personalized trip play an important role in users' selections. To address these challenges, in this article, we propose a MORL-Trip (short for Multi -Objective Reinforcement Learning for Trip Recommendation) approach. MORL-Trip models the personalized trip recommendation as a Markov Decision Process (MDP), and implements it upon the Actor-Critic framework. MORL-Trip enhances the state representation with sequential information, geographic information and order information to learn user's context from real-time location. In addition, MORL-Trip augments the standard Critic component by designing a composite reward function to enforce three principal objectives: accuracy, popularity and diversity. We conduct extensive experiments on the public datasets and compare the performance of MORL-Trip with the most advanced methods to verify its superiority, and show the importance of reinforcing popularity and diversity as complementary objectives in the personalized trip recommendation.

Automated summary

Trip recommendation is an intelligent service that offers personalized itinerary plans to tourists in unfamiliar cities, considering temporal and spatial constraints. In this article, we propose MORL-Trip, a Multi-Objective Reinforcement Learning approach, to address the challenges of capturing users' dynamic preferences and enhancing the diversity and popularity of personalized trips. MORL-Trip models the recommendation as a Markov Decision Process and incorporates sequential, geographic, and order information to learn user's context. It also introduces a composite reward function to reinforce accuracy, popularity, and diversity as principal objectives.