Touristic places recommendation system, 2017

Item — Call Number: MU Thesis Alm

Identifier: b7830971

Staff Only

Scope and Contents

From the Collection:

The collection consists of theses written by students enrolled in the Monmouth University graduate Software Engineering program. The holdings are bound print documents that were submitted in partial fulfillment of requirements for the Master of Science degree.

Dates

Creation: 2017

Creator

Almohaimeed, Saad Abdullah (1991- ) (Author, Person)
Rosca, Daniela (Thesis advisor, Person)

Conditions Governing Access

The collection is open for research use. Access is by appointment only.

Access to the collection is confined to the Monmouth University Library and is subject to patron policies approved by the Monmouth University Library.

Collection holdings may not be borrowed through interlibrary loan.

Research appointments are scheduled by the Monmouth University Library Archives Collections Manager (723-923-4526). A minimum of three days advance notice is required to arrange a research appointment for access to the collection.

Patrons must complete a Researcher Registration Form and provide appropriate identification to gain access to the collection holdings. Copies of these documents will be kept on file at the Monmouth University Library.

Extent

1 Items (print book) : 46 pages ; 8.5 x 11.0 inches (28 cm).

Language of Materials

English

Additional Description

Abstract

Recommending touristic places for visitors is an important service of touristic websites. Most websites allow visitors to either numerically rate or leave a review about the site they visited to express their opinion. Those reviews help future visitors choose the place that is the most attractive to them, and at the same time allow the touristic place owner to improve and increase the visitor's satisfaction. Sometimes, the amount of reviews can be overwhelming, discouraging intended visitors to manually read all the reviews to draw a conclusion. Relying only on star-rating of the touristic places can be misleading, as it lacks the context and details of the reviews.

That is why we propose a semantic based recommender system that relies on natural language processing and big data analysis of the visitors' reviews. The touristic places recommendation process is divided into three steps. First step is the "data preparation", which is the process of preparing the dataset to be used in the analysis. Second step is the "keywords extraction", which is the process of extracting keywords from the prepared dataset of reviews, to be selected by the user to guide the recommendation process. Finally, the "touristic places recommendation" step is the process of recommending places based on the extracted keywords, sentiment analysis of the reviews, and context sensitive information such as geographic location, temporal selection and keywords translation.

Partial Contents

Abstract -- Acknowledgements -- List of figures -- I. Introduction -- II. Literature review -- III. Solution -- IV. Limitations -- V. Future work -- VI. Conclusion -- Glossary -- References.