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#LyX 2.0 created this file. For more info see http://www.lyx.org/
\lyxformat 413
\begin_document
\begin_header
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\secnumdepth 3
\tocdepth 3
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\end_header
\begin_body
\begin_layout Title
Recommending Favourable Dishes in nearby Restaurants
\end_layout
\begin_layout Author
Tarun Tater(IMT2012047)
\end_layout
\begin_layout Section*
Problem Statement
\end_layout
\begin_layout Standard
Quite a few online services take user reviews and use them to provide better
services by taking in consideration the suggestions and comments in the
user reviews.
But there are many other things user reviews can do.
The user reviews are also being used to personalise the user's experience.
This project also aims to do something similar.
\end_layout
\begin_layout Standard
Given a user's location(in Bangalore), the aim is to suggest a known favourable
dish to a user in a nearby restaurant given.
The dishes suggested are one of the dishes the user has already reviewed
before.
\end_layout
\begin_layout Section*
Data Processing
\end_layout
\begin_layout Standard
The data about the user reviews for dishes and restaurants were taken through
Zomato's API.
Zomato's API gives all user reviews for a particular restaurant in a city.
The project only targeted Bangalore.
So, a list of all restaurants in Bangalore was retrieved from the Zomato
API.
There were 7730 restaurants in total.
For each restaurant, there were a list of attributes related to it.
The attributes retrieved for each restaurant were:
\end_layout
\begin_layout Standard
\begin_inset Float table
placement H
wide false
sideways false
status open
\begin_layout Plain Layout
\begin_inset Tabular
<lyxtabular version="3" rows="5" columns="3">
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<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
\begin_inset Text
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name
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</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" usebox="none">
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hasDelivery
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hasBar
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\begin_inset Text
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longitude
\end_layout
\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text
\begin_layout Plain Layout
avgCostForTwo
\end_layout
\end_inset
</cell>
<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
\begin_inset Text
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smokingArea
\end_layout
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</cell>
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<row>
<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
\begin_inset Text
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latitude
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</cell>
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\begin_inset Text
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hasDineIn
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</cell>
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hasAC
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cuisines
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hasTakeAway
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acceptsCreditCards
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<row>
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\begin_inset Text
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timings
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isPureVeg
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</cell>
<cell alignment="center" valignment="top" topline="true" bottomline="true" leftline="true" rightline="true" usebox="none">
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hasWifi
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\end_inset
\begin_inset Caption
\begin_layout Plain Layout
List of Attributes for Restaurants
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
The data related to timings had different formats and had to be extensively
processed to bring it into a standardised format.
\end_layout
\begin_layout Standard
Then, for each restaurant, all the user reviews were extracted.
The user reviews were filtered to cater to the need of the project.
Only those user reviews were used which had a dish name in them.
A list of dishes was compiled from different sources which served as the
database of dishes available.
\end_layout
\begin_layout Standard
The user reviews were further processed by removing unwanted punctuations.
Now, the adjectives which are generally used to describe the dishes was
also compiled.
\end_layout
\begin_layout Section*
Analysis of Dataset
\end_layout
\begin_layout Standard
Each user review was shortened by identifying and keeping only sentences
which had opinions in them.
Each opinion was classified using a trained sentiment analyser for food
reviews.
A Naive Bayes classifier was used which gives a positive and negative rating
to a sentence.
These opinions were thus given a positive or negative tag with respective
probabilities.
\end_layout
\begin_layout Standard
The opinions with their respective probabilities for positive sentiment
were chunked together for each user.
There were more than 80,000 users who have written reviews.
This rendered a json of each user having dish wise reviews and their probabilit
ies for positive sentiments.
\end_layout
\begin_layout Subsection*
Algorithm
\end_layout
\begin_layout Standard
For each user, the list of dishes he/she had commented upon were taken as
the dishes he/she would prefer to eat.
This seemed a reasonable assumption as the dishes considered were only
those which the user had eaten previously and was passionate enough to
write a review about it.
This may result in missing a few dishes but would never give a false positive.
\end_layout
\begin_layout Standard
The number of reviews about a particular dish was also given importance
in deciding the favourable dish.
The idea was that if a user had reviewed a particular dish multiple times,
it was more likely that the user prefers this dish over another.
\end_layout
\begin_layout Standard
\begin_inset Float algorithm
placement H
wide false
sideways false
status open
\begin_layout Plain Layout
def getPreferences(userId):
\end_layout
\begin_layout Plain Layout
#Getting user preferences from dishes reviews
\end_layout
\begin_layout Plain Layout
get all reviews for this userId.
\end_layout
\begin_layout Plain Layout
for dish in eachReview:
\end_layout
\begin_layout Plain Layout
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
increment the score for the dish.
#Based on a metric
\end_layout
\begin_layout Plain Layout
sort the dishes score in decreasing order
\end_layout
\begin_layout Plain Layout
#the dish with the highest score is the most preferred dish.
\end_layout
\begin_layout Plain Layout
return the top
\begin_inset Formula $n$
\end_inset
dishes with their scores from the sorted list.
\end_layout
\begin_layout Plain Layout
\begin_inset Caption
\begin_layout Plain Layout
Retrieve Dishes Preferred By User
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
The metric used to increment the score or evaluate the order of preferences
of dish can vary.
The metric used for this project is depends on the number of reviews a
user has given about a particular dish and the positive sentiment related
to each comment.
Taking the number of reviews about a dish by a user as
\begin_inset Formula $n$
\end_inset
\end_layout
\begin_layout Itemize
\begin_inset Formula $if\, n<3,\, score=average\, positive\, sentiment\, about\, dish\, given\, by\, user$
\end_inset
\end_layout
\begin_layout Itemize
\begin_inset Formula $if\, n\geq3\, and\, n\leq5,\, score=average\, positive\, sentiment+0.01*n$
\end_inset
\end_layout
\begin_layout Itemize
\begin_inset Formula $if\, n>5,\, score=average\, positive\, sentiment+0.02*n$
\end_inset
\end_layout
\begin_layout Standard
\begin_inset Float algorithm
placement H
wide false
sideways false
status open
\begin_layout Plain Layout
def recommend(userId, distancePrefered, lat, lon, currentDay, currentTime):
\end_layout
\begin_layout Plain Layout
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
feasibleRestaurants = []
\end_layout
\begin_layout Plain Layout
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
recommendations = []
\end_layout
\begin_layout Plain Layout
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
for eachRestaurant in restaurantsList:
\end_layout
\begin_layout Plain Layout
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
check distance from current location
\end_layout
\begin_layout Plain Layout
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
if distance from current location < distancePrefered:
\end_layout
\begin_layout Plain Layout
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
if the restaurant is currently open:
\end_layout
\begin_layout Plain Layout
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
append to feasibleRestaurants
\end_layout
\begin_layout Plain Layout
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
dishesPrefered = getPreferences(userId)
\end_layout
\begin_layout Plain Layout
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
for eachRestaurant in feasibleRestaurants:
\end_layout
\begin_layout Plain Layout
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
if any dish preferred by the user is served by the restaurant:
\end_layout
\begin_layout Plain Layout
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
calculate the average positive sentiment of that dish in this restaurant
\end_layout
\begin_layout Plain Layout
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
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\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
\begin_inset space ~
\end_inset
append this dish,restaurant and avg.
positive sentiment in recommendations
\end_layout
\begin_layout Plain Layout
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sort the recommendations in decreasing order of their positive sentiment
\end_layout
\begin_layout Plain Layout
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return the recommendations
\end_layout
\begin_layout Plain Layout
\begin_inset Caption
\begin_layout Plain Layout
Recommend
\end_layout
\end_inset
\end_layout
\end_inset
\end_layout
\begin_layout Standard
For a user, his/her preferred dishes are evaluated depending on his/her
previous reviews and based on the above mentioned metric.
Then, in the vicinity of user's location i.e., in a user given preferred
distance(radius), a search is made for all the restaurants using the Haversine
distance formula.
The restaurants in the vicinity are then checked if they are open at that
particular time on present day.
Now, all these open restaurants in the vicinity are checked if they serve
any of the dishes preferred by the user.
All such restaurants (where any of the preferred dishes are served) are
then ranked based on their average positive sentiment corresponding to
the preferred dish.
The top 10 results are displayed to the user.
\end_layout
\begin_layout Standard
The user can put other constraints such as averagar cost, take away facility,
AC, vegetarian restaurant etc.
All the available options are mentioned in Table1.
\end_layout
\begin_layout Section*
Result
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\begin_layout Standard
Although, there was no direct way to test the results.
The hope is that such a system would keep learning from user's behaviour
and improve over time.
Since, the dishes suggested are one of the dishes that the user has already
tried and commented upon.
It can be reasonably assumed that the user likes the dish.
And since the number of comments about a particular dish are also taken
into account, the suggested dish seems to be one of the favoured dishes
to a particular customer, if not the most favoured dish.
\end_layout
\begin_layout Standard
The metric to evaluate preferred dishes can be changed depending on the