Social media reviews based hotel recommendation system using collaborative filtering and big data

To eliminate the concerns of cold-start and scalability within the filtering, collaborative recommendation system for a hotel under the ranking list for the customer; this study proposed an extensive data analysis for such an application. Alongside, the application is very user-friendly. Technology keeps growing daily and is also very helpful in the future. This paper brings out the innovation. This approach configures the latest variant of CNN termed as Capsule Network (CapsNet) to recommend the best suited hotel based on user collaboration. With the help of this application, we can make precise and accurate recommendations to the user. Besides, generally employed, the procedure adopted for the model recommended integrated with the filtering practice that includes analyzing user's preferences and making product or service recommendations through a similarity index among the ratings specified in the database. Finally, the merging technique employed in the system is discussed and evaluated on standard data sets based on the general architectural design of the recommendation system. An enhanced strategy attains fusion idea of filtering technique in collaborative to classified data for addressing this issue. To put the proposed approach to the test, we employ hotel recommendation data. Also, the outcome proved the best findings of the comprehensive list taken as the top listed hotels from which the scale has been considered through the proposed method.

Automated summary

In order to address the issues of cold-start and scalability in the collaborative filtering-based hotel recommendation system under a ranking list, this study proposed an extensive data analysis. Additionally, the user-friendly nature of the application was highlighted. This paper presents an innovative approach using Capsule Network (CapsNet) to recommend the most suitable hotel for the users. Overall, the importance of this research is rated at 7 out of 10.