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Integrating User Profiles and Collaborative Filtering for Smart Recommendation of Tourism City Cultural and Creative Products.

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Bibliographic Details
Title: Integrating User Profiles and Collaborative Filtering for Smart Recommendation of Tourism City Cultural and Creative Products.
Authors: Zhang, Shuping
Source: International Journal of High Speed Electronics & Systems; Dec2025, Vol. 34 Issue 4, p1-19, 19p
Subject Terms: RECOMMENDER systems, HERITAGE tourism, URBAN tourism, CONSUMER profiling, SATISFACTION, KNOWLEDGE acquisition (Expert systems)
Abstract: In the domain of tourism and cultural creative product recommendations, the design and implementation of personalized recommendation systems are crucial for enhancing user satisfaction and engagement. Although traditional recommendation systems, such as Collaborative Filtering techniques, have been widely applied, they still face challenges in dealing with sparse data and achieving highly personalized recommendations. To solve this problem, a collaborative filtering recommendation method combining user profiles and multi-task knowledge acquisition is proposed in this study. We first construct consumer profiles with the aid of studying users behavioral facts and demographic statistics, after which layout a multi-task learning framework that improves the accuracy and personalization degree of tips through getting to know common functions of various however associated recommendation duties through shared representations. We evaluated our approach on numerous actual-international datasets, and the experimental outcomes display sizable improvements over conventional collaborative filtering strategies and other existing recommendation systems in key overall performance signs along with accuracy, recall, and F1-score. These results indicate that the collaborative filtering advice approach is more advantageous via the mixing of person profiles and multi-venture learning holds sizable software potential inside the subject of tourism metropolis cultural and innovative product recommendations. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract:In the domain of tourism and cultural creative product recommendations, the design and implementation of personalized recommendation systems are crucial for enhancing user satisfaction and engagement. Although traditional recommendation systems, such as Collaborative Filtering techniques, have been widely applied, they still face challenges in dealing with sparse data and achieving highly personalized recommendations. To solve this problem, a collaborative filtering recommendation method combining user profiles and multi-task knowledge acquisition is proposed in this study. We first construct consumer profiles with the aid of studying users behavioral facts and demographic statistics, after which layout a multi-task learning framework that improves the accuracy and personalization degree of tips through getting to know common functions of various however associated recommendation duties through shared representations. We evaluated our approach on numerous actual-international datasets, and the experimental outcomes display sizable improvements over conventional collaborative filtering strategies and other existing recommendation systems in key overall performance signs along with accuracy, recall, and F1-score. These results indicate that the collaborative filtering advice approach is more advantageous via the mixing of person profiles and multi-venture learning holds sizable software potential inside the subject of tourism metropolis cultural and innovative product recommendations. [ABSTRACT FROM AUTHOR]
ISSN:01291564
DOI:10.1142/S0129156425402967