Personalized Travel Recommendation System Using Average Cumulative Rating Matrix Factorization Technique: Concept and Framework
Recommendation systems in travel applications have a purpose to provide custom-made results to travelers while making a travel plan. These recommendation systems should be adaptable if user preferences change dynamically. To get custom-made results, the recommendation systems should be provided with...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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World Scientific Publishing
2023-05-01
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| Series: | Vietnam Journal of Computer Science |
| Subjects: | |
| Online Access: | https://www.worldscientific.com/doi/10.1142/S2196888822500361 |
| _version_ | 1797824110730936320 |
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| author | Niranjan Kumar K. V. Pallavi Bhagyashri R. Hanji |
| author_facet | Niranjan Kumar K. V. Pallavi Bhagyashri R. Hanji |
| author_sort | Niranjan Kumar |
| collection | DOAJ |
| description | Recommendation systems in travel applications have a purpose to provide custom-made results to travelers while making a travel plan. These recommendation systems should be adaptable if user preferences change dynamically. To get custom-made results, the recommendation systems should be provided with traveler’s interests such as traveler’s specifications, preferences concerning destinations, type of activities they are very much interested to do in their travel plan. However, current recommendation systems are unable to fetch required features from travelers and destination places. Moreover, current systems are lacking to recommend destination places by considering social interest and their experience (i.e. recommendations by considering many traveler’s interests, for example, when two travelers interest matches the places visit by the first traveler can be suggested to the second traveler or vice-versa and travelers experience concerning particular destination place). To address the issues and problems of the current system, we propose and implement a tourist recommendation system which is termed as Average Cumulative Rating (ACR) that supports the extraction of rating and experience which is in the form of text description. The overall score is computed based on rating and traveler experience and feed to the traditional Matrix Factorization (MF) technique for providing custom results for travelers. |
| first_indexed | 2024-03-13T10:34:08Z |
| format | Article |
| id | doaj.art-8677252a4cf5400e96edabbcba255683 |
| institution | Directory Open Access Journal |
| issn | 2196-8888 2196-8896 |
| language | English |
| last_indexed | 2024-03-13T10:34:08Z |
| publishDate | 2023-05-01 |
| publisher | World Scientific Publishing |
| record_format | Article |
| series | Vietnam Journal of Computer Science |
| spelling | doaj.art-8677252a4cf5400e96edabbcba2556832023-05-18T07:44:22ZengWorld Scientific PublishingVietnam Journal of Computer Science2196-88882196-88962023-05-01100215919510.1142/S2196888822500361Personalized Travel Recommendation System Using Average Cumulative Rating Matrix Factorization Technique: Concept and FrameworkNiranjan Kumar0K. V. Pallavi1Bhagyashri R. Hanji2Department of Computer Science & Engineering, Visvesvaraya Technological University, Karnataka, IndiaDepartment of Computer Science & Engineering, AMC Engineering College, Karnataka, IndiaDepartment of Computer Science & Engineering, Global Academy of Technology, Karnataka, IndiaRecommendation systems in travel applications have a purpose to provide custom-made results to travelers while making a travel plan. These recommendation systems should be adaptable if user preferences change dynamically. To get custom-made results, the recommendation systems should be provided with traveler’s interests such as traveler’s specifications, preferences concerning destinations, type of activities they are very much interested to do in their travel plan. However, current recommendation systems are unable to fetch required features from travelers and destination places. Moreover, current systems are lacking to recommend destination places by considering social interest and their experience (i.e. recommendations by considering many traveler’s interests, for example, when two travelers interest matches the places visit by the first traveler can be suggested to the second traveler or vice-versa and travelers experience concerning particular destination place). To address the issues and problems of the current system, we propose and implement a tourist recommendation system which is termed as Average Cumulative Rating (ACR) that supports the extraction of rating and experience which is in the form of text description. The overall score is computed based on rating and traveler experience and feed to the traditional Matrix Factorization (MF) technique for providing custom results for travelers.https://www.worldscientific.com/doi/10.1142/S2196888822500361Matrix factorizationrating and descriptioncumulative ratingsentiment scoreclass of documentrecommendation system |
| spellingShingle | Niranjan Kumar K. V. Pallavi Bhagyashri R. Hanji Personalized Travel Recommendation System Using Average Cumulative Rating Matrix Factorization Technique: Concept and Framework Vietnam Journal of Computer Science Matrix factorization rating and description cumulative rating sentiment score class of document recommendation system |
| title | Personalized Travel Recommendation System Using Average Cumulative Rating Matrix Factorization Technique: Concept and Framework |
| title_full | Personalized Travel Recommendation System Using Average Cumulative Rating Matrix Factorization Technique: Concept and Framework |
| title_fullStr | Personalized Travel Recommendation System Using Average Cumulative Rating Matrix Factorization Technique: Concept and Framework |
| title_full_unstemmed | Personalized Travel Recommendation System Using Average Cumulative Rating Matrix Factorization Technique: Concept and Framework |
| title_short | Personalized Travel Recommendation System Using Average Cumulative Rating Matrix Factorization Technique: Concept and Framework |
| title_sort | personalized travel recommendation system using average cumulative rating matrix factorization technique concept and framework |
| topic | Matrix factorization rating and description cumulative rating sentiment score class of document recommendation system |
| url | https://www.worldscientific.com/doi/10.1142/S2196888822500361 |
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