Improvising Personalized Travel Recommendation System with Recency Effects
A travel recommendation system based on social media activity provides a customized place of interest to accommodate user-specific needs and preferences. In general, the user’s inclination towards travel destinations is subject to change over time. In this project, we have analyzed users’ twitter da...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2021-09-01
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| Series: | Big Data Mining and Analytics |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2020.9020026 |
| _version_ | 1798029395766542336 |
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| author | Paromita Nitu Joseph Coelho Praveen Madiraju |
| author_facet | Paromita Nitu Joseph Coelho Praveen Madiraju |
| author_sort | Paromita Nitu |
| collection | DOAJ |
| description | A travel recommendation system based on social media activity provides a customized place of interest to accommodate user-specific needs and preferences. In general, the user’s inclination towards travel destinations is subject to change over time. In this project, we have analyzed users’ twitter data, as well as their friends and followers in a timely fashion to understand recent travel interest. A machine learning classifier identifies tweets relevant to travel. The travel tweets are then used to obtain personalized travel recommendations. Unlike most of the personalized recommendation systems, our proposed model takes into account a user’s most recent interest by incorporating time-sensitive recency weight into the model. Our proposed model has outperformed the existing personalized place of interest recommendation model, and the overall accuracy is 75.23%. |
| first_indexed | 2024-04-11T19:23:38Z |
| format | Article |
| id | doaj.art-d6a6b2ee03e3408ca99af299cba5a4b0 |
| institution | Directory Open Access Journal |
| issn | 2096-0654 |
| language | English |
| last_indexed | 2024-04-11T19:23:38Z |
| publishDate | 2021-09-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj.art-d6a6b2ee03e3408ca99af299cba5a4b02022-12-22T04:07:14ZengTsinghua University PressBig Data Mining and Analytics2096-06542021-09-014313915410.26599/BDMA.2020.9020026Improvising Personalized Travel Recommendation System with Recency EffectsParomita Nitu0Joseph Coelho1Praveen Madiraju2<institution content-type="dept">Department of Mathematical and Statistical Sciences</institution>, <institution>Marquette University</institution>, <city>Milwaukee</city>, <state>WI</state> <postal-code>53233</postal-code>, <country>USA</country><institution content-type="dept">Department of Mathematical and Statistical Sciences</institution>, <institution>Marquette University</institution>, <city>Milwaukee</city>, <state>WI</state> <postal-code>53233</postal-code>, <country>USA</country><institution content-type="dept">Department of Computer Science</institution>, <institution>Marquette University</institution>, <city>Milwaukee</city>, <state>WI</state> <postal-code>53233</postal-code>, <country>USA</country>A travel recommendation system based on social media activity provides a customized place of interest to accommodate user-specific needs and preferences. In general, the user’s inclination towards travel destinations is subject to change over time. In this project, we have analyzed users’ twitter data, as well as their friends and followers in a timely fashion to understand recent travel interest. A machine learning classifier identifies tweets relevant to travel. The travel tweets are then used to obtain personalized travel recommendations. Unlike most of the personalized recommendation systems, our proposed model takes into account a user’s most recent interest by incorporating time-sensitive recency weight into the model. Our proposed model has outperformed the existing personalized place of interest recommendation model, and the overall accuracy is 75.23%.https://www.sciopen.com/article/10.26599/BDMA.2020.9020026travel recommendationtime sensitivityrecency effectpersonalizationsocial media |
| spellingShingle | Paromita Nitu Joseph Coelho Praveen Madiraju Improvising Personalized Travel Recommendation System with Recency Effects Big Data Mining and Analytics travel recommendation time sensitivity recency effect personalization social media |
| title | Improvising Personalized Travel Recommendation System with Recency Effects |
| title_full | Improvising Personalized Travel Recommendation System with Recency Effects |
| title_fullStr | Improvising Personalized Travel Recommendation System with Recency Effects |
| title_full_unstemmed | Improvising Personalized Travel Recommendation System with Recency Effects |
| title_short | Improvising Personalized Travel Recommendation System with Recency Effects |
| title_sort | improvising personalized travel recommendation system with recency effects |
| topic | travel recommendation time sensitivity recency effect personalization social media |
| url | https://www.sciopen.com/article/10.26599/BDMA.2020.9020026 |
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