Enhancing Hotel Recommendations through Feature- based Clustering

This paper addresses the challenge of sparse interaction data in recommendation systems for the hotel industry. Due to the infrequent nature of hotel stays (often once or a few times annually), customer-product interaction data is typically sparse, hindering the effectiveness of traditional collaborative filtering techniques. We propose a novel hybrid recommendation framework specifically designed for this scenario. Unlike conventional systems that rely solely on user preference similarity, our framework leverages hotel clustering based on binary attributes to segment the product space. User interactions are analyzed within these clusters, leading to a more refined recommendation process. We take advantage of several clustering and feature reduction techniques and assign the final recommendation through ballot scoring. The experiments are performed on a real-world hotel sales data set including both sales information and hotel attributes. We evaluate our methodology and demonstrate significant improvements over baseline approaches which is the case of not using the found clusters for recommendation. The proposed framework achieves a two-fold increase in both the number of users receiving recommendations and the number of correct recommendations. These results highlight the potential of cluster- based recommendations for mitigating sparsity issues in tourism recommender systems.