Text-Based Price Recommendation System for Online Rental Houses
Online short-term rental platforms, such as Airbnb, have been becoming popular, and a better pricing strategy is imperative for hosts of new listings. In this paper, we analyzed the relationship between the description of each listing and its price, and proposed a text-based price recommendation sys...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2020-06-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2019.9020023 |
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| _version_ | 1832572945503879168 |
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| author | Lujia Shen Qianjun Liu Gong Chen Shouling Ji |
| author_facet | Lujia Shen Qianjun Liu Gong Chen Shouling Ji |
| author_sort | Lujia Shen |
| collection | DOAJ |
| description | Online short-term rental platforms, such as Airbnb, have been becoming popular, and a better pricing strategy is imperative for hosts of new listings. In this paper, we analyzed the relationship between the description of each listing and its price, and proposed a text-based price recommendation system called TAPE to recommend a reasonable price for newly added listings. We used deep learning techniques (e.g., feedforward network, long short-term memory, and mean shift) to design and implement TAPE. Using two chronologically extracted datasets of the same four cities, we revealed important factors (e.g., indoor equipment and high-density area) that positively or negatively affect each property’s price, and evaluated our preliminary and enhanced models. Our models achieved a Root-Mean-Square Error (RMSE) of 33.73 in Boston, 20.50 in London, 34.68 in Los Angeles, and 26.31 in New York City, which are comparable to an existing model that uses more features. |
| format | Article |
| id | doaj-art-5a3a2a9dd9164c0badb5540b5620e5aa |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2020-06-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-5a3a2a9dd9164c0badb5540b5620e5aa2025-02-02T05:59:18ZengTsinghua University PressBig Data Mining and Analytics2096-06542020-06-013214315210.26599/BDMA.2019.9020023Text-Based Price Recommendation System for Online Rental HousesLujia Shen0Qianjun Liu1Gong Chen2Shouling Ji3<institution content-type="dept">Department of Computer Science and Technology</institution>, <institution>Zhejiang University</institution>, <city>Hangzhou</city> <postal-code>310027</postal-code>, <country>China</country>.<institution content-type="dept">Department of Computer Science and Technology</institution>, <institution>Zhejiang University</institution>, <city>Hangzhou</city> <postal-code>310027</postal-code>, <country>China</country>.<institution content-type="dept">School of Electrical and Computer Engineering</institution>, <institution>Georgia Institute of Technology</institution>, <city>Atlanta</city>, <state>GA</state> <postal-code>30318</postal-code>, <country>USA</country>.<institution content-type="dept">Department of Computer Science and Technology</institution>, <institution>Zhejiang University</institution>, <city>Hangzhou</city> <postal-code>310027</postal-code>, <country>China</country>.Online short-term rental platforms, such as Airbnb, have been becoming popular, and a better pricing strategy is imperative for hosts of new listings. In this paper, we analyzed the relationship between the description of each listing and its price, and proposed a text-based price recommendation system called TAPE to recommend a reasonable price for newly added listings. We used deep learning techniques (e.g., feedforward network, long short-term memory, and mean shift) to design and implement TAPE. Using two chronologically extracted datasets of the same four cities, we revealed important factors (e.g., indoor equipment and high-density area) that positively or negatively affect each property’s price, and evaluated our preliminary and enhanced models. Our models achieved a Root-Mean-Square Error (RMSE) of 33.73 in Boston, 20.50 in London, 34.68 in Los Angeles, and 26.31 in New York City, which are comparable to an existing model that uses more features.https://www.sciopen.com/article/10.26599/BDMA.2019.9020023price recommendationnatural language processingsentence embeddinglong short-term memory (lstm)mean shift |
| spellingShingle | Lujia Shen Qianjun Liu Gong Chen Shouling Ji Text-Based Price Recommendation System for Online Rental Houses Big Data Mining and Analytics price recommendation natural language processing sentence embedding long short-term memory (lstm) mean shift |
| title | Text-Based Price Recommendation System for Online Rental Houses |
| title_full | Text-Based Price Recommendation System for Online Rental Houses |
| title_fullStr | Text-Based Price Recommendation System for Online Rental Houses |
| title_full_unstemmed | Text-Based Price Recommendation System for Online Rental Houses |
| title_short | Text-Based Price Recommendation System for Online Rental Houses |
| title_sort | text based price recommendation system for online rental houses |
| topic | price recommendation natural language processing sentence embedding long short-term memory (lstm) mean shift |
| url | https://www.sciopen.com/article/10.26599/BDMA.2019.9020023 |
| work_keys_str_mv | AT lujiashen textbasedpricerecommendationsystemforonlinerentalhouses AT qianjunliu textbasedpricerecommendationsystemforonlinerentalhouses AT gongchen textbasedpricerecommendationsystemforonlinerentalhouses AT shoulingji textbasedpricerecommendationsystemforonlinerentalhouses |