Abstract: Recommendation systems are widely used in e-commerce websites as they can recommend appropriate movies, songs, books, and other items to users according to users' historical behavior. In traditional collaborative filtering algorithms, users' historical scores are usually used to predict the unknown item rating, while ignoring their textual reviews. Therefore, this paper proposes a park recommendation model based on user reviews and ratings (PRMRR). PRMRR first uses the latent Dirichlet allocation model to extract the statistical distribution of the park features. Secondly, it detects user preference distribution based on park features and user ratings. In order to measure the credibility of user ratings, user rating confidence level is considered to correct user preferences. Thirdly, it uses Kullback-Leibler divergence to calculate the similarity between different users and then predicts the unknown park rating for a specific user. Finally, the proposed algorithm is evaluated on two real park data sets, and the results on two different data sets show that the proposed approach outperforms other traditional approaches. Our recommendation algorithm thus has great potential to improve the quality of park recommendation and effectively handle the data sparsity problem.

Key words: collaborative filtering, user preference, park recommendation, user rating confidence level