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Multi-objective reinforcement learning approach for trip recommendation
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PRE
AI
DOI：10.1016/j.eswa.2023.120145.png)
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摘要
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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.
Keyword:
Recommender system
Deep neural network
Reinforcement learning
Attention mechanism
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
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