SimTrip: Modelling trip similarity for travel recommendations
Abstract (150 Words)
SimTrip: Modelling trip similarity for travel recommendations
Proposing presonalized travel recommendations using trip similarity and reinforcement learning
Abstract
In this paper, we present a self-adaptive model to make personalized trip recommendations. We train our model on 100 city pair locations using a heuristic approach for city pair similarities. We find trip similarity on any origin-destination combinations, allowing us to make personalised relevant recommendations. We use publically available economic, geographic, climate and demographics data as an input to our model. The similarity score is updated on user feedback to capture trend and seasonality for model updates. We discuss the calibration methods to tune the recommendation model and suggest evaluation techniques. We also present use case scenarios for our model.
SimTrip: Modelling trip similarity for travel recommendations
SimTrip: Modelling trip similarity for travel recommendations
Proposing presonalized travel recommendations using trip similarity and reinforcement learning
Abstract
In this paper, we present a self-adaptive model to make personalized trip recommendations. We train our model on 100 city pair locations using a heuristic approach for city pair similarities. We find trip similarity on any origin-destination combinations, allowing us to make personalised relevant recommendations. We use publically available economic, geographic, climate and demographics data as an input to our model. The similarity score is updated on user feedback to capture trend and seasonality for model updates. We discuss the calibration methods to tune the recommendation model and suggest evaluation techniques. We also present use case scenarios for our model.