Personalized Travel Sequence Recommendation

on Multi-Source Big Social Media

ABSTRACT

Automatic travel recommendation is an important problemin both research and industry. Big media, especiallythe flourish of social media (e.g., Facebook, Flick, Twitteretc.) offers great opportunities to address many challengingproblems, for instance, GPS estimationand travel recommendation. Travelogue websites (e.g., offer rich descriptions about landmarksand traveling experience written by users. Furthermore,community-contributed photos with metadata (e.g., tags,date taken, latitude etc.) on social media record users’ dailylife and travel experience. These data are not only usefulfor reliable POIs (points of interest) ming, travel routesming, but give an opportunity to recommend personalizedtravel POIs and routes based on user’s interest.

EXISTING SYSTEM

Automatic travel recommendation is an important problem in both research and industry. Big media, especially the flourish of social media (e.g., Facebook, Flick, Twitter etc.) offers great opportunities to address many challenging problems, for instance, GPS estimation and travel recommendation. Travelogue websites (e.g., offer rich descriptions about landmarks and traveling experience written by users. Furthermore, community-contributed photos with metadata (e.g., tags, date taken, latitude etc.) on social media record users’ dailylife and travel experience. These data are not only useful for reliable POIs (points of interest) ming [4], travel routes ming, but give an opportunity to recommend personalizedtravel POIs and routes based on user’s interest.

DISADVANTAGES

  • General travel route planning cannotwell meet users’ personal requirementsthe recommended POIs should bepersonalized to user interest since different users may prefer different types of POIs.
  • It is far more difficult and time consuming for users to plantravel sequence than individual POIs.

PROPOSED SYSTEM

This paper presents a personalized travel sequence recommendation from both travelogues and communitycontributed photos and the heterogeneous metadata (e.g., tags, geo-location, and date taken) associated with thesephotos. Topical package space including representativetags, the distributions of cost, visiting time and visiting season of each topic, is mined to bridge the vocabulary gap between user travelpreference and travel routes. We take advantage of the complementary of two kinds of social media: travelogue andcommunity-contributed photos. We map both user’s and routes’ textual descriptions to the topical package space to get user topicalpackage model and route topical package model (i.e., topical interest, cost, time and season). To recommend personalized POIsequence, first, famous routes are ranked according to the similarity between user package and route package. Then top ranked routesare further optimized by social similar users’ travel records. Representative images with viewpoint and seasonal diversity of POIs areshown to offer a more comprehensive impression.

ADVANTAGES

  • We automaticallymine user’s travel interest from user contributed photo collections including consumptioncapability, preferred time and season which is importantto route planning and difficult to get directly
  • Famous routes areranked according to the similarity between userpackage and route package, and top ranked famousroutes are further optimized according to social similarusers’ travel records.
  • Topical Package Model (TPM) methodto learn user’s and route’s travel attributes. It bridgesthe gap of user interest and routes attributes.

MODULES

  • Social Media Mining
  • User Topical Package Model Mining
  • Route Topical Package Model Mining

SYSTEM REQUIREMENTS

H/W System Configuration:-

Processor - Pentium –III

RAM - 256 MB (min)

Hard Disk - 20 GB

Key Board - Standard Windows Keyboard

Mouse - Two or Three Button Mouse

Monitor - SVGA

S/W System Configuration:-

•Operating system : Windows 7/UBUNTU.

•Coding Language: Java 1.7 , Hadoop 0.8.1

•IDE:Eclipse

•Database:MYSQL

Further Details Contact: A Vinay 9030333433, 08772261612, 9014123891

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