Implementation of Personalized Information Recommendation Platform System Based on Deep Learning Tourism

Abstract

In order to provide tourists with better tourism services, a system method of personal information recommendation platform based on deep learning tourism is proposed. The system includes noise reduction autoencoder, feature extraction module, data preprocessing module, recommendation calculation module, expert evaluation module, recommendation result output module, customer feedback module, and storage module. The personal information recommendation platform system based on deep learning tourism of the present invention enables tourists to obtain tourism information conveniently and quickly through scientific information organization and presentation form and helps tourists to better arrange tourism plans and form tourism decisions. By effectively aggregating multiple neighborhoods of nodes, embedding high-order collaboration information into the node embedding vector, obtaining the potential preferences of users, solving the problems of user data sparse and cold start, and finally through experimental analysis, a research method is proposed. It is used to build the model of tourist attraction recommendation system. Experimental results show that the proposed method for cold-start user recommendation has the best performance in terms of accuracy, recall, and normalized loss cumulative gain, and it is 17.9% higher than BPR in recall rate Recall@5 and 11.8% higher in accuracy rate. It is proved that the system has a significant impact on the diversity and novelty of tourist attraction recommendation.

Tourism Hotel Accommodation Recommendation Algorithm Based on the Cellular Space-Improved Divisive Analysis (CS-IDIANA) Clustering Model

“…The cellular space (d) is the hotel cellular () Hi C , whose code is the number of the hotel cellular () Hi C . The average dissimilarity is arranged in descending order (with the tourist attraction numbers in parentheses) as follows: 0.9193 (4 and 6), 0.9103 (5), 0.9098 (15), 0.8746 (7), 0.8724 (10), 0.8583 (1), 0.8336 (14), 0.8028 (13), 0.7827 (2), 0.7461 (9), 0.7349 (8), 0.7308 (12), 0.7292 (11), 0.6746 (3). Analyze the data in Table 4 and Figure 5.…”

“…The experiment proves that the algorithm can accurately process the highly sparse hotel data and recommend hotels to users with accurate requirements. Wang [14] uses the deep learning method to construct a hotel room reservation and recommendation system, achieving personalized recommendations based on user needs, and the recommendation results have good accuracy. Zhang [15] sets up a hotel recommendation prediction algorithm through deep learning using the promotional images and hotel text evaluations on hotel websites.…”

“…This research method does not consider real-world tourism scenarios and only improves the algorithm's performance, without making improvements in regard to reducing the travel costs and enhancing the travel efficiency. Thirdly, some research, e.g., [13][14][15], applies the users' scoring data, evaluation data or data mining results to obtain the users' interests, or improve the scoring rules for users, in order to improve the accuracy of recommendation systems and solve the problems of cold start and data sparsity. This method relies heavily on user scoring and evaluation and has significant subjective bias, and it also lacks consideration of the hotel feature attributes, spatial attributes and travel costs.…”

ResNetMF: Enhancing Recommendation Systems with Residual Network Matrix Factorization

“…Wang [33] proposes a classification technique based on deep learning, incorporating word embedding and factorization machines to improve recommendation accuracy. Wang [34] focuses on developing a personalized recommendation system for tourism, addressing challenges such as sparse user data and the cold start problem.…”

Retracted: Implementation of Personalized Information Recommendation Platform System Based on Deep Learning Tourism

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”