Design and implementation of tourism enhancement through use of web mashup

Tourism industry is one of the most rapidly growing industry in the world. It has major contribution to the economy of the country. Through literature survey, it has been observed that there is much scope of improvement in this sector. This paper presents the tourism recommendation system which is built using the concept of web mashup which extracts data from multiple websites. This extracted data is used for analysis and providing recommendation about the touristic spots to the users. Recommendation uses components from tourist database, namely restaurant id, place id, hotel id, event id as item sets for performing apriori algorithm. The antecedents are matched with the last transaction of the logged in user and the precedents are displayed.

[1]  Sajal Halder,et al.  Predicting students yearly performance using neural network: A case study of BSMRSTU , 2016, 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV).

[2]  Yun Yang.  Chapter 7 – Temporal Data Clustering via a Weighted Clustering Ensemble With Different Representations , 2017 .

[3]  Tao Hu,et al.  Research on the Pattern of Tourism E-commerce , 2012, 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum.

[4]  Ridi Ferdiana,et al.  Mobile tourism services model: A contextual tourism experience using mobile services , 2014, 2014 6th International Conference on Information Technology and Electrical Engineering (ICITEE).

[6]  Yun Yang.  Temporal Data Clustering , 2017 .

[7]  Gergely Mezei,et al.  Beyond mashups: Graph transformations on web data , 2014, IEEE 18th International Conference on Intelligent Engineering Systems INES 2014.

[8]  S. S. Dhenakaran,et al.  An ontology-based framework for Semantic Web Content Mining , 2014, 2014 International Conference on Computer Communication and Informatics.

[9]  Chin-Chih Chang,et al.  A Recommender System Combining Social Networks for Tourist Attractions , 2013, 2013 Fifth International Conference on Computational Intelligence, Communication Systems and Networks.

[10]  Dong Chen,et al.  Identifying places of interest for tourists using knowledge discovery techniques , 2014, 2014 International Conference on Industrial Automation, Information and Communications Technology.

[11]  María N. Moreno García,et al.  A collaborative filtering method for music recommendation using playing coefficients for artists and users , 2016, Expert Syst. Appl..

[12]  Yu Tsao,et al.  Personalizing Recurrent-Neural-Network-Based Language Model by Social Network , 2017, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[13]  Shanshan Gao,et al.  The application and comparison of web services for sentiment analysis in tourism , 2015, 2015 12th International Conference on Service Systems and Service Management (ICSSSM).

[14]  Sankaran Mahadevan,et al.  An improved method to construct basic probability assignment based on the confusion matrix for classification problem , 2016, Inf. Sci..

[15]  Nazaraf Shah,et al.  Hybrid Recommendation System for Tourism , 2013, 2013 IEEE 10th International Conference on e-Business Engineering.

[16]  Kun Wang,et al.  Evaluation of the attractiveness of tourism destinations based on link analysis , 2010, 2010 18th International Conference on Geoinformatics.

[17]  Edoardo Ardizzone,et al.  Extracting Touristic Information from Online Image Collections , 2012, 2012 Eighth International Conference on Signal Image Technology and Internet Based Systems.