The purpose of this Clinic project is to help Expedia, Inc. expand the search capabilities it offers to its users. In particular, the goal is to help the company respond to unconstrained search queries by generating a method to associate hotels and regions around the world with the higher-level attributes that describe them, such as “family- friendly” or “culturally-rich.” Our team utilized machine-learning algorithms to extract metadata from textual data about hotels and cities. We focused on two machine-learning models: decision trees and Latent Dirichlet Allocation (LDA). The first appeared to be a promising approach, but would require more resources to replicate on the scale Expedia needs. On the other hand, we were able to generate useful results using LDA. We created a website to visualize these results.
| Identifer | oai:union.ndltd.org:CLAREMONT/oai:scholarship.claremont.edu:scripps_theses-1681 |
| Date | 01 January 2015 |
| Creators | Long, Hannah |
| Publisher | Scholarship @ Claremont |
| Source Sets | Claremont Colleges |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Scripps Senior Theses |
| Rights | © 2015 Hannah A. Long, default |
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