Main Reference PaperPersonalized Travel Sequence Recommendation on Multi-Source Big Social Media, IEEE Transactions on Big Data, May 2016 [J2EE/Hadoop].
  • A personalized travel sequence recommendation system to facilitate comprehensive Points of Interest (POIs), topical interest, cost, time and season that are recommended to social media users. This work propose an learning algorithm called Topical Package Model which learns users travel preferences from text descriptions associated with geo-tagged photos. Then optimized the top ranked famous travel sequences are recommended according to social similar users travel records.

+ Description
  • A personalized travel sequence recommendation system to facilitate comprehensive Points of Interest (POIs), topical interest, cost, time and season that are recommended to social media users. This work propose an learning algorithm called Topical Package Model which learns users travel preferences from text descriptions associated with geo-tagged photos. Then optimized the top ranked famous travel sequences are recommended according to social similar users travel records.

  • To achieve the accuracy of recommendation.

  • To recommended the POI travel sequence of time and session.

  • To achieve the scalability.

+ Aim & Objectives
  • To achieve the accuracy of recommendation.

  • To recommended the POI travel sequence of time and session.

  • To achieve the scalability.

  • This work contributes time aware POI recommendation, which aims to return a set of POIs for a user to visit at a specified time in a day. During recommendation mine the temporal behaviors of users by analyzing their historical check ins, and make use of the mined temporal behaviors for time aware POI recommendations.

+ Contribution
  • This work contributes time aware POI recommendation, which aims to return a set of POIs for a user to visit at a specified time in a day. During recommendation mine the temporal behaviors of users by analyzing their historical check ins, and make use of the mined temporal behaviors for time aware POI recommendations.

  • Java JDK 1.8, Hadoop 1.2.1, MySQL 5.5.40

  • Netbeans 8.0.1, J2EE, Hadoop

+ Software Tools & Technologies
  • Java JDK 1.8, Hadoop 1.2.1, MySQL 5.5.40

  • Netbeans 8.0.1, J2EE, Hadoop

  • B.E / B.Tech / M.E / M.Tech

+ Project Recommended For
  • B.E / B.Tech / M.E / M.Tech

Professional Ethics: We S-Logix would appreciate the students those who willingly contribute with atleast a line of thinking of their own while preparing the project with us. It is advised that the project given by us be considered only as a model project and be applied with confidence to contribute your own ideas through our expert guidance and enrich your knowledge.