Personalized Recommendation Engine for Restaurants and dishes
The
objective of this article is to propose a design of a personalized
recommendations application for restaurants owners which can be used by the
customers of the given credit/debit card issuer. The developed recommendation application
can be installed as mobile application in the mobile phone of the customers.
The recommendation engine will use machine learning algorithms and predictive
analytics techniques to derive information from the credit transaction data, mobile location
data, social media data (Facebook, Twitter), public data (Yelp.com/Bundle.com etc.),
mobile browser data and data of the restaurants*.
The
recommendation engine will have two modules. First module will use the
clustering algorithm to derive actionable information from credit card data and
restaurants data. It will also use the data mining techniques to integrate the
useful information available in the web space with restaurant data (explained
later). This module will execute in server of the bank periodically. Decision Science analyst of the bank will
have ability to improve this module whenever it is required. Second module will have ability to access
the data from social media, browser, wish list and mobile location data and
output of the module 1. Also use the machine learning algorithm to learn about
the personality of the customer using the data those can be accessed by this
module. Additionally user can control the use of this module by enabling and
disabling it.
The
recommendation system also has ability to learn over the period by the
activities and visits of the customer to the recommended restaurants and
improvises the recommendation.
Section 1 describes the sources of the data to
be used in the system. Section 2 discusses optimization of the data sources
integration. Section 3 discusses the analytical methods to be used to conclude
the factors influencing the customer’s decision. Section 4 discusses the
machine learning algorithm used to model the recommendation engine. Section 5 describes how the use of learning
algorithm allows the recommendation system to learn by itself over the time.
Then finally, section 6 discusses the various scenarios where customer can use
the recommendation system.
Section 1
Data
from various sources is required to integrate to develop the robust
recommendation engine. This section describes the major data sources which can
be used
1.
Credit
Card Transaction Data : Analysis of credit card
transaction data will be carried out to
calculate the average ticket size of the transaction, kind of restaurants
visited (Indian, continental, Chinese, Italian, multi cuisine, family
restaurants vs Bar), frequency of visits, location of restaurants, number of days between two successive visit, time of
visit (breakfast, lunch, dinner etc.). This information will be obtained from
the credit card transaction data and will be summarized at customer level with
customer id (or credit card number) as primary key. Also a table will be created for list of restaurants customer has
already visited. The decision on the number of months of data to use for
creating above variables will be taken based on exploratory data analysis of
transaction data and customer demographics.
Assuming that based exploratory analysis of data 3 months of transaction
data is sufficient of the robust recommendation engine.
2.
Mobile
Location Data: In
this age most of the mobiles have GPRS system using which we can track the
customer movements. The time spent in any restaurant indicates customers’
interests in the restaurant. Time spent can be tracked using mobile location
data. Current location of the customer will be used to recommend the customers
the restaurants with distance information or only nearby restaurants rather
than recommending restaurants which are located a distant. The location data
will be also used to know whether customer is travelling by car (or other
vehicle) or by public transports or by walk. The speed with which customer moves from one place to another
surrogates his mode of convenience.
3. Restaurant Data: The
information of different restaurants is available in their website and in
review websites like zomato.com. Using these information a data base will be
created which will contain following information:
Name of restaurants, website link,
type of restaurants, Menu, Dishes famous for, Location, timing, happy hour
timings, reviews and rating on website like yelp.com, zomato.com etc.
The
database can be created and managed by the bank itself and can be periodically
refreshed or Bank can tie up with restaurants for updating their data.
Restaurants can easily be convinced for this as implementation of
recommendation engine will increase the footfall in the restaurants.
As
this data cannot be exhaustive hence on few occasions, data of the restaurants
in web space will be accessed real time.
4.
Public
data: Publicly available data in websites
like yelp.com, zomato.com, justeat.in, Bundle.com etc. Reviews and rating
regarding dishes, services and ambiance of the restaurants will be used to
carry out the sentimental analysis of the restaurants and will be summarized at
restaurants level. The summary will be integrated with the Restaurants data. These
data will be refreshed periodically.
5.
Social
Media data: Customer information from social
media can used to predict the persona of the customer which will be an input in
recommendation of the restaurants. As the social media data is not available
publicly when customer installs the recommendation application in his/her
mobile a pop up will ask to access the social media data. For customer who does
give access while application installation will get the message each time
he/she uses the application with some charismatic lines as “ For improved
recommendation please share Facebook/twitter data” etc.
6. Customer wish list: Recommendation
engine will also request to maintain the wish list and will be stored in
mobile. Engine will pop up periodically to request customer to update the wish
list. Customer can create wish list of restaurants, places to visit, dishes to
try, adventure sports, electronic gadgets, cars etc. These data will be used to
study the personality of the customer.
7. Mobile browser search
data: Customer are using mobile for internet
browsing and search related to restaurants or food joint will be accessed by
the application to improve the recommendation system.
Section 2:
Data
sources discussed in the section 1 will be summarized at different levels for
example credit card transaction data will be summarized at customer level and
also a list of restaurants will be created. Restaurant data will be created at
restaurant level with its all attribute information. Social Media data and
customer wish list will be used to define the personality of the customer and
summarized information will be stored in mobile. A logical data model will be
designed using which data sources will be integrated by customer as well as
restaurants. Data from sources like Social media data, customer wish list will
be present in mobile only or will be accessed real time.
Data
from public websites like yelp.com, Bundle.com etc. will be integrated by
restaurants with the restaurant data table. Credit card transaction data will
be integrated with restaurant data table by restaurants while with social media
data by customer.
Section 3
This
section discusses how the data sources mentioned in previous section are useful
for decision making process of the recommendation system. I discuss which data
will be used to derive which specific information of the customer.
Credit card transaction
data will be used to identify transaction pattern of the customers at
restaurants and food joints. Information
like his/her transaction ticket size and frequency of visits will be used to
create value of the customers in terms of high spender, medium spender or low
spenders at food points. FRM metric will
be created to define these categories where
FRM
(Frequency, Recency, Monetary)
= (Frequency of restaurants visits X Amount Spent)/(No of days since last visit)
Type
restaurants visited by the customer will be provide the information of the
customer’s taste choice, price range choice, timings, weekends, day of visits
(special day), preference etc. These attributes will be used to identify list
of restaurants where customer would like to visit after considering current
location and customer wish list and customer persona. Type of restaurants
visited also tells which dishes customer likes. If the transaction value has no
pattern means customer likes to try different dishes at the restaurants
assuming he/she is visiting alone.
Customers
visiting websites like yelp.com, zomato.com are more sensitive to reviews,
ratings and pricing, ambiance. Those who writes reviews also likes to share
their views with others which is in fact inadvertently a promotion (good
reviews) or discouragement (bad reviews)
Customer
wish list tells about his aspirations and taste. Variety of restaurants wish
list contributes to the fact that customer likes to try out different
restaurants. Presence of healthy food items reveals he/she is health conscious.
This also tells about his/her eating habits.
Data
from social media presents the customers personality. Frequent twits or status
updates, photo uploads tells that he/she is open person. Likes of Facebook
pages tells about his/her likes. No of
friends tells that he/she is friendly person.
Section 4
Using
the variable frequency of transactions to restaurants by credit cards customers
group of customers will be created.
First group will be group of customers those visits restaurants frequently.
There will two more groups which
will be less frequent visitors and never visited. Using the customers transactions pattern over
the 3 month period , balance pattern and demographics a look alike model will be created to
identify customers who are similar to frequent visitors in all attributes but
visits to restaurants. This will enable the bank to tap the customers who are
actually visiting the restaurants but using cards of other bank.
 |
| Total Customer target base= Frequent visitors + Look alike customers |
Using
this customer base develop the clusters of customers using K means clustering technique.
The variables related to restaurant transaction will not be used for clustering
but other attributes will be. The restaurant transaction variables will be used
to create cluster profiles like his/her taste choice, ticket size, restaurant
type choice, frequency, timings, outing and special days, holiday places likes
to visit, price sensitivity etc. Such clusters profiling will enable to
identify restaurants preference factors for any customer have not used credit card of
this bank in the restaurants basis the
cluster profile to which he/she belongs. Let’s call these attributes customer’s
restaurants and dishes selection attributes.
Restaurant
data will be used to create clusters of the restaurants using clustering
technique. The output clusters will have
homogeneous restaurants in same cluster but heterogeneous in different cluster.
The location of the restaurant will not be used as clustering variables but
price range, type of cuisine, ambiance, theme, dishes famous for, ratings,
happy hour timings, discounts and offers etc. Using the Customer’s
restaurants and dishes selection attributes from the clusters of
restaurants a set of initial recommendation of restaurants will be generated.
The
first set of recommended restaurants will further pass through a filter of
distance from the current position of customer tracked by GPRS system of the
smart phone. This will also suggest time
to reach to restaurant in current traffic situation based on Google map data
and also route for car, public transport or by walk. But there is always possibility that customer
is in another country and only few restaurants from that country have been
detailed in restaurant data table. In that case customers’ attributes of
restaurants selection will be used to search the restaurants in web space and
public review sites like bundle.com, yelp.com. This will generate recommendations
of restaurants keeping the location/place of the customer as one of the
criteria.
These
restaurants recommended will also pass through optional filter of customers’
wish list. Any recommended restaurants till and also on the customer wish list
will be given higher priority compared to others. Additional filter of
customer’s persona will apply on these recommended restaurants. The persona
information will be stored in mobile as the machine learning algorithm will run
at the back end of engine to update the persona information of the customers on
weekly basis.
User
of this recommendation system presently credit card customer of the bank will
have some controlling power to generate the recommendation system for different
case. Some of these cases have been discussed in section 6. Customer will be
able to disable the location filter/wish list or personality filter of the
recommendation engine. The recommendation
system will have ability to download the customers’ restaurants selection
attribute information in mobile.
Recommendation engine will be coded in such a way that it can access the
restaurants cluster data on real time basis.
 |
  Flow of the recommendation Engine can be explained by pictorial model below |
Bank Space: Data stored at bank’s storage server and server executing the clustering algorithms. Credit card data is refreshed periodically by the bank. Restaurant data is updated by the bank itself or has tie up with listed restaurants.
Web Space:
Public data like yelp.com, zomato.com and restaurants websites etc. Data mining
algorithms text mining techniques executes at Bank’s server and derives the
information post sentimental analysis. The resultant is integrated with
restaurants data by restaurants.
Initial
set of recommended restaurants is generated by overlaying customers’ restaurants
and dishes selection attributes and attributes of the restaurants.
Section 5:
I
am proposing a recommendation engine which will be using the dynamic data
rather than a static data. Three months data of credit card transaction will be
refreshed monthly so the customers’ restaurants and dishes selection attributes
will be updated on monthly basis. Also restaurants data table will be refreshed
periodic basis and as this table also uses information (ratings and review etc)
from public websites so over the time more homogenous cluster of the
restaurants will be created.
Customers’
feedback is tracked indirectly with smart phone as mobile can track which
restaurants customer has visited out of all the recommendation based on
location data and total time spent there. More time spending in particular type
of the restaurants also reveals his choice. Using the machine learning
algorithm over the time engine can fine tune the choices of the customer. If
customer visits none of the recommended restaurants and uses browser to search
restaurants further. It means engine could not recommend any restaurants of
his/her choice. The attributes of the visited restaurants will be considered in
future recommendation which will be done by using machine learning algorithm
and use of credit card data.
Customer
wish list will pop up periodically for updating. The machine learning algorithm
developed for defining persona of the customer will learn more about the customer
over the time with the twits, status updates, likes, photo sharing, time spent
on social media , wish list etc.
Bank
must develop a robust plan to deploy a system which can house the information
derived by running clustering algorithm on credit card transaction data and
restaurant data. I have named this as Bank
space. In my design of recommendation engine for best personalized
recommendation I am proposing to use the processor of the mobile phone to
execute the module 1 of the engine. Processor will be also used to access the
customer’s social media data and movements (location). This system provides
mechanism to the engine to learn over the time. Another challenge in
implementation may be round the clock access of summarized output of clustering
algorithm deployed in server by application installed in the mobile phone. This challenge can overcome by downloading
the attributes of restaurants selection of customer and information of initial
set of recommended restaurants in mobile automatically.
Section 6.
A
customer can use the recommendation system in different ways. Considering cases
one by one where the recommendation system will help the customer with the
example of Mr John.
1. John
on a Sunday evening went to see an art exhibition and while returning wants
have dinner but he is not quite familiar with the locality. He uses the recommendation
application and set of restaurants nearby with map is recommended. These
restaurants are selected based Mr John’s restaurants and dishes selection
attributes and persona. He also finds in result that this is busy hour for
these restaurants so he thinks while going back home he look for more
recommendation and will go where he can have dinner quickly.
2. Next
Monday John will be turning 31. He wants to through a party to his colleagues.
He is confused whether he should go out for dinner or lunch. Keeping that he is
giving party to his colleagues he switches of the personality and his personal
attributes of restaurants. The recommendation system generates set of
restaurants with information of type of cuisine and best time to visit.
3. John
is in business trip to France. He uses
recommendation system to find the restaurant which he can enjoy. As the
restaurants data does not have data of restaurants in France the initial
internal recommendation is null but by using the John’s restaurants and dishes selection
attributes and persona information recommendation engine searches for the local
restaurants. The results are 2 Chinese and 3 Indian restaurants with
metropolitan ambience in distance of 2 miles. As John is Indian so he has
frequently visited Indian and Chinese restaurants in other countries also (not
only in India) which is captured in attributes.
The process flow of
the design clearly explains which data sources should be used to derive what
information and how the personalized recommendation can be generated. This
design can be implemented using any programming language.
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