Travel Recommender System for Social Media

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Travel Recommender System for Social Media 3 Pages Posted: 19 May 2022 See all articles by Vinod AloneVinod AloneVasantdada Patil Pratishthan's College of Engineering and Visual Arts Mumbai, IndiaManish GangawaneVasantdada Patil Pratishthan's College of Engineering and Visual Arts Mumbai, IndiaSachin BarahateVasantDada Patil Pratishthans College of EngineeringAtul ShintreVasantdada Patil Pratishthan's College of Engineering and Visual Arts Mumbai, IndiaSrikant BagewadiVasantdada Patil Pratishthan's College of Engineering and Visual Arts Mumbai, India Date Written: April 8, 2022 Abstract With the help of personalized recommender systems nowadays the customized services to users are made available. Social media plays an important role to develop a personalized recommender systems. In this study we explores the twitter data to personalize recommendations for the users who travels most. Using a machine learning classification model we identify the travel related tweets. These travel related tweets are then applied to personalize recommendation system to know places of interest for the user. We classified the places of interest such as: Old or historical buildings, gardens, ancient museums , famous places and restaurants. To get the better results we also considered the travel tweets of friends, family members. The model has been evaluated by comparing the predictions made by the model with the users choices in the survey. These evaluations show 70% prediction accuracy. This accuracy can be increased with good travel-tweet training dataset and travel category identification technique using machine learning. In order to refine the recommendations , different categories for travels can be included like sports grounds, musical shows, theaters, etc. The proposed model lists places of users interest from each category in proportion to the travel category score generated by the model. Keywords: recommender systems, social media analysis, twitter data, personalization, travel tweet Suggested Citation: Suggested Citation Alone, Vinod and Gangawane, Manish and Barahate, Sachin and Shintre, Atul and Bagewadi, Srikant, Travel Recommender System for Social Media (April 8, 2022). Available at SSRN: https://ssrn.com/abstract=4114101 Vinod Alone (Contact Author) Vasantdada Patil Pratishthan's College of Engineering and Visual Arts Mumbai, India ( email ) Manish Gangawane Vasantdada Patil Pratishthan's College of Engineering and Visual Arts Mumbai, India ( email ) Sachin Barahate VasantDada Patil Pratishthans College of Engineering Vasantdada Patil Education ComplexChunabhatti, SionMumbai, MA 400022India Atul Shintre Vasantdada Patil Pratishthan's College of Engineering and Visual Arts Mumbai, India ( email ) Srikant Bagewadi Vasantdada Patil Pratishthan's College of Engineering and Visual Arts Mumbai, India ( email ) Download This Paper Open PDF in Browser Do you want regular updates from SSRN on Twitter? Follow SSRN Paper statistics Downloads 3 Abstract Views 11 PlumX Metrics Related eJournals Information Systems eJournal Follow Information Systems eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 221 PAPERS 4,293 Applied Communication eJournal Follow Applied Communication eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 78 PAPERS 1,708 Communication & Computational Methods eJournal Follow Communication & Computational Methods eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 53 PAPERS 1,360 International Conference on Innovations & Research in Technology & Engineering (ICIRTE 2022) Follow International Conference on Innovations & Research in Technology & Engineering (ICIRTE 2022) Subscribe to this free journal for more curated articles on this topic FOLLOWERS 5 PAPERS 0 Feedback Feedback to SSRN Feedback (required) Email (required) Submit If you need immediate assistance, call 877-SSRNHelp (877 777 6435) in the United States, or +1 212 448 2500 outside of the United States, 8:30AM to 6:00PM U.S. Eastern, Monday - Friday. Submit a Paper Section 508 Text Only Pages SSRN Quick Links SSRN Solutions Research Paper Series Conference Papers Partners in Publishing Jobs & Announcements Newsletter Sign Up SSRN Rankings Top Papers Top Authors Top Organizations About SSRN SSRN Objectives Network Directors Presidential Letter Announcements Contact us FAQs Copyright Terms and Conditions Privacy Policy We use cookies to help provide and enhance our service and tailor content. To learn more, visit Cookie Settings. This page was processed by aws-apollo4 in 0.314 seconds