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A Decision Tree based Recommendation System for Tourists
In this paper author is implementing C4.5 decision tree algorithm with MRMR features selection to recommend travel areas to tourist by using dataset from past tourist experiences. All existing algorithms such as collaborative or content filtering algorithms uses current user past experience data to recommend him new locations. These algorithms will not work if this current user has no past experiences data.
To overcome from above problem author is asking to use C4.5 decision tree algorithms which take experiences of previous users and then build a model and if new user enter his requirements then decision tree will predict best location based on his given input. Decision tree don’t need new users past experience data.
To implement decision tree model we need to have dataset and this dataset sometime will have empty or garbage values and this values will put bad effect on decision tree model so we can remove such empty or garbage values by applying pre-process techniques.
Sometime to predict or build model no need to use all columns (attributes) values from dataset and this unnecessary attributes can be remove by apply features selection algorithms and here we are using MRMR features selection algorithms to remove unnecessary attributes to reduce execution time of building model and to increase system accuracy.
Below are the dataset columns or attributes taken from previous users to build model.
This data set is populated by crawling TripAdvisor.com. Reviews on destinations in 10 categories mentioned across East Europe are considered. Each traveller rating is mapped as Excellent (4), Very Good (3), Average (2), Poor (1), and Terrible (0) and average rating is used against each category per user.
Dataset columns and values
userid,art_galleries,dance_clubs,juice_bars,restaurants,museums,resorts,parks_picnic_spots,beaches,theaters,religious_institutions,location
Above are the column names and below are the column values
User 1,0.93,1.8,2.29,0.62,0.8,2.42,3.19,2.79,1.82,2.42,Amsterdam_Heining_2
User 2,1.02,2.2,2.66,0.64,1.42,3.18,3.21,2.63,1.86,2.32,Amsterdam_Jachthaven_ijbur
User 3,1.22,0.8,0.54,0.53,0.24,1.54,3.18,2.8,1.31,2.5,Amsterdam_Bert_Haanstra_Kad
User 4,0.45,1.8,0.29,0.57,0.46,1.52,3.18,2.96,1.57,2.86,Amsterdam_Ruigoord_Ker
In above values first column is USER_ID and second column is ART_GALLERIES and third is DANCE CLUB etc and for each column user had given rating from 4 to 0 and 4 means Excellent service.
Now using above values we can build C4.5 decision tree and prediction will be done using below test values
'User 122',0.93,1.8,2.29,0.62,0.8,2.42,3.19,2.79,1.82,2.42,?
'User 222',1.02,2.2,2.66,0.64,1.42,3.18,3.21,2.63,1.86,2.32,?
'User 3222',1.22,0.8,0.54,0.53,0.24,1.54,3.18,2.8,1.31,2.5,?
'User 4222',0.45,1.8,0.29,0.57,0.46,1.52,3.18,2.96,1.57,2.86,?
'User 522',0.51,1.2,1.18,0.57,1.54,2.02,3.18,2.78,1.18,2.54,?
In above test values new user has given values to look for location which has above service rating but new user don’t know which location provides such services so he will put question mark and when we upload above test values to decision tree then it will take decision and predict best location and inform to user.
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