<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In order to discover users’ interest preferences from the massive travel information and meet users’ needs for personalized tourist attraction recommendations. In this paper, we construct correspondingly according to the domain knowledge and complete the design and implementation of the mapping according to the corresponding process. The item representation enhancement module is designed to obtain a more comprehensive user preference by extracting information from the user’s historical preferences and then using graph convolution to learn the correlation between the user’s historical behavioral preferences and entities to enhance the KG semantic information of items. An item representation enhancement model based on RippleNet is proposed, from the item representation enhancement module to the interest module, to establish a connection between the user and the implicit information of the item so as to consider their mutual influence and represent the missing information more comprehensively, and finally, a personalized travel recommendation model based on knowledge graph and deep learning is constructed. From the results of the study, it is obtained that the model in this paper is 4.14% and 4.6% better than the best model SASRec in the comparison method in NDCG@10, Recall@10, MRR@10 indicators on both 1.93% higher. At the recommended list length K=20, the Recall rate Recall@20 has reached 80.78%. The superiority of the performance of the model constructed in this paper is demonstrated in the experiments, which brings technical innovation to the personalized attraction recommendation system.</p>
</abstract>

In order to discover users’ interest preferences from the massive travel information and meet users’ needs for personalized tourist attraction recommendations. In this paper, we construct correspondingly according to the domain knowledge and complete the design and implementation of the mapping according to the corresponding process. The item representation enhancement module is designed to obtain a more comprehensive user preference by extracting information from the user’s historical preferences and then using graph convolution to learn the correlation between the user’s historical behavioral preferences and entities to enhance the KG semantic information of items. An item representation enhancement model based on RippleNet is proposed, from the item representation enhancement module to the interest module, to establish a connection between the user and the implicit information of the item so as to consider their mutual influence and represent the missing information more comprehensively, and finally, a personalized travel recommendation model based on knowledge graph and deep learning is constructed. From the results of the study, it is obtained that the model in this paper is 4.14% and 4.6% better than the best model SASRec in the comparison method in NDCG@10, Recall@10, MRR@10 indicators on both 1.93% higher. At the recommended list length K=20, the Recall rate Recall@20 has reached 80.78%. The superiority of the performance of the model constructed in this paper is demonstrated in the experiments, which brings technical innovation to the personalized attraction recommendation system.

Innovative Research on Deep Learning Algorithm in Intelligent Attractions Recommendation Technology

Party Committee Teacher Work Department (Personnel Office)

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In order to discover users’ interest preferences from the massive travel information and meet users’ needs for personalized tourist attraction recommendations....

Innovative Research on Deep Learning Algorithm in Intelligent Attractions Recommendation Technology

Pubblicato online: 04 ott 2023

Pagine: -

Ricevuto: 10 gen 2023

Accettato: 14 apr 2023

DOI: https://doi.org/10.2478/amns.2023.2.00501

Parole chiave
Attraction recommendation, Knowledge graph, Deep learning, RippleNet, Item representation enhancement model

© 2023 Yu Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Innovative Research on Deep Learning Algorithm in Intelligent Attractions Recommendation Technology

Pubblicato online: 04 ott 2023

Pagine: -

Ricevuto: 10 gen 2023

Accettato: 14 apr 2023

DOI: https://doi.org/10.2478/amns.2023.2.00501

Parole chiaveAttraction recommendation, Knowledge graph, Deep learning, RippleNet, Item representation enhancement model

© 2023 Yu Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Attraction recommendation, Knowledge graph, Deep learning, RippleNet, Item representation enhancement model