Information mining for travel has become a popular research domain in the development of tourism industry nowadays. Currently, studies focusing on the extraction of available travel information from actual tourists' evaluations and reviews on social media sites have been conducted under numerous occasions. In this study, we have proposed another effective yet simple method of discovering local travel-information from traveler's blogs by locating region-sensitive information for the travelers. In this process, regionally restricted words are first extracted based on their frequency of appearances inside the blog. Then, a region-restrictedness score of each blog is calculated through the analysis of the previously extracted regionally restricted phrases. From there, we begin analyzing the content of each blog and classify them into pre-defined categories, using LDA model and word embedding-representations. Through this process, we are able to generate blog recommendations based on our method and see some successful examples as a proof for the effectiveness of our approach.

A Method for Discovering Local Travel Information from Travel-blogs

In general online websites contain millions of articles related to different domains. The major flaw found in these websites is the absence of security for files uploaded without any protection. It is made in such a way as it is simply available for any users to change the content of these files. Thus, to overcome the above drawback we propose the system “Framework for Enhancing Privacy of the Files in Web Using Random Key Generation Technique” which makes all the files protected using key generation and encryption techniques so that no other invalid user is allowed to access the file. Even the authorized user must request to the admin to get the key for the particular file from the database to access it. Thus the system provides secured data access with original information uploaded by the data owner, it also avoids the duplication of the same content from different users of the web.

/pdf/framework-for-enhancing-privacy-of-files-in-web-using-random-s3lg1mixrc.pdf

Framework for enhancing privacy of files in web using random key generation technique

This paper proposes a method to recommend paragraphs of Wikipedia pages as a travel guide. This method helps tourists (users) read attractive descriptions of Wikipedia pages during their travel. In order to rate paragraphs, importance, non-redundancy, and novelty of the paragraphs are evaluated based on a Tf-Idf manner. Especially, novelty is done by user's experience estimated from geo-tagged tweets located to places that the user visited in the past. As the results of the experiments, we confirmed the geo-tagged tweets reflected user's experience and the recommendations exceeded the expectation of MAP criteria.

Recommending paragraphs of wikipedia pages as a travel guide

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Recommending paragraphs of wikipedia pages as a travel guide