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In the face of problems such as information overload and the information cocoon resulting from big data, it is a key point of current research to solve the problem of semantic fuzziness of online reviews and improve the accuracy of personalized recommendation algorithms by using online reviews. Based on the advantage of the probabilistic language term set to deal with fuzzy information and the historical data of online hotel reviews, this paper proposes a collaborative filtering recommendation algorithm for hotels. Firstly, the text data of hotel online reviews are crawled by a crawler and processed by jieba and TF-IDF tools. Secondly, the hotel evaluation attribute set is constructed, and the sentiment analysis of the review statements is carried out with the help of the HowNet sentiment dictionary and manual annotation method. The probabilistic language term set is used to classify the data and derive statistics, and the maximum deviation method is used to determine the weight of each attribute. Then, the cosine similarity formula is fused with the modified cosine similarity formula to calculate the similarity and construct the decision matrix. Finally, combined with the historical data of the user's hotel selection, the hotel recommendation results are generated. This paper collected review data from 10 hotels in Macau from the official "Ctrip" website. The proposed recommendation algorithm model was then applied to process and analyze the data, resulting in the generation of a ranked list of hotel recommendations. To validate the accuracy and effectiveness of this research, the recommendation results were compared with those produced by other algorithms. [ABSTRACT FROM AUTHOR]

The continuous development of e-commerce recommendation systems enables people to better obtain the products they need, saving economic and time costs. When recommending products, massive online user reviews can objectively reflect the efficacy of products and provide more practical reference value. However, due to the fuzziness of expression, there exists great uncertainty about the information in user reviews. The probabilistic linguistic term set (PLTS) is a useful tool that can help solve the information ambiguity and distinguish the importance of different terms. This paper regards PLTS as a data statistical tool to depict the information of user reviews. Based on the theory of PLTS, this paper proposes a hotel recommendation algorithm. First, we analyse the statements of online user hotel reviews with the aid of Jieba and TF-IDF. Second, the statements of reviews are translated into PLTSs and stored in the evaluation matrix. Third, we adopt the maximum deviation method to derive the attribute weights of hotels. Finally, we use probabilistic linguistic cosine similarity to calculate the similarity degrees of hotels and rank the recommended hotels according to similarity. With respect to different hospital products of users, the recommendation orders of hotels are given according to their degrees of similarity. To verify the effectiveness and superiority of the proposed algorithm, this paper selects 10 hotels in the city of Zhengzhou for case application and conducts a comparative analysis with other recommendation methods. [ABSTRACT FROM AUTHOR]