Know the basics of recommendation systems to the latest machine learning techniques and algorithms to start building a recommendation system that delivers results.

1. HOW TO BUILD AN AI­POWERED
RECOMMENDATION SYSTEM?
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The internet has transformed the way we shop, with a vast selection of
products available for purchase online. However, this convenience comes at
a cost, with consumers having to sort through countless options, making it
an overwhelming and tiring task.
On the other hand, the challenge for online stores is how to sell more goods
 

2. at a higher price and faster than their competitors. One solution is to use a
recommendation system that utilizes arti몭cial intelligence (AI) to provide
personalized recommendations to users. Such a system uses machine
learning algorithms that analyze user data, such as search history, purchase
behavior, and preferences, to predict what products a user is likely interested
in.
For consumers, the bene몭ts of personalized product recommendations are
obvious. They save time and e몭ort by o몭ering tailored suggestions more
likely to match consumers’ interests and preferences. They also help online
sellers boost revenue and pro몭t by giving their customers personalized
recommendations encouraging them to purchase more products. Besides,
they help build customer loyalty and trust by improving the overall shopping
experience for consumers.
Hence, personalized product recommendation systems are valuable for
online sellers and buyers alike.
Whether it’s e-commerce, media streaming, or any other sector o몭ering
content to users, recommending new material is crucial to the platform’s
success. Even a small increase in revenue percentage can translate into
millions of dollars in pro몭t. In fact, McKinsey estimates that personalized
products account for 35% of Amazon’s revenue and a staggering 75% of
Net몭ix’s revenue, with the numbers only expected to grow.
Recommendation systems, also known as recommender systems, o몭er a
compelling alternative to traditional search algorithms. They help users
discover new products they might have missed, o몭er personalized
recommendations based on their preferences, and ultimately make the
shopping experience more e몭cient and enjoyable. These systems can
signi몭cantly improve customer satisfaction and drive sales by automating the
search process and saving customers’ time. It’s no wonder why every large
platform today comes with a recommendation system.

3. Technically speaking, recommendation systems use machine learning
algorithms to give users personalized recommendations for products,
services, or information based on their behavior, preferences, and history.
According to statistics, 80% of customers are likelier to buy from a brand that
provides personalized experiences. Furthermore, businesses using a
recommendation engine can see a 150% increase in click-through rates,
leading to increased sales and revenue. Net몭ix is an excellent example of a
company that has leveraged the power of recommendation engines to
revolutionize streaming. By providing users with customized content, Net몭ix
has achieved lower cancellation rates, saving the company around a billion
dollars annually.
But how can you build an AI-powered recommendation system for your
business? In this article, we will explore the key components of an AI-
powered recommendation engine, the di몭erent types of recommendation
algorithms, and how to build and implement a recommendation engine that
can enhance user experience and boost your business’s performance. Let’s
delve deeper and explore this technology.
What is an AI-powered recommendation system?
Types of AI-powered recommendation systems
Bene몭ts of using AI-powered recommendation systems
How does an AI-powered recommendation system work?
How to build a recommendation system? A case study in Python using the
MovieLens dataset
What is an AI­powered recommendation
system?
An AI-powered recommendation system is a machine learning algorithm that
is trained to rank or rate products or users. It is designed to predict the
ratings a user might give to a speci몭c item and then return those predictions
to the user in a ranked list. This technology is used by many popular
companies such as Google, Amazon, and Net몭ix to increase user

4. engagement with their platforms. For example, Spotify may recommend
songs similar to ones you have previously listened to or liked to keep you
using their platform to listen to music. Amazon may suggest products to
users based on the data they have collected on that particular user.
Although recommender systems are incredibly useful, they are often seen as
“black boxes” because the models created by these companies are not easily
interpretable. Users may not understand why a certain recommendation is
being made, but they often 몭nd the recommendations helpful and relevant
to their needs and interests. The results generated are often for things the
user needs or wants but may not have been aware of until they were
recommended.
There are many ways to build a recommender system, and the approaches
can vary from algorithmic and formulaic to modeling-centric. These
approaches include page rank, collaborative 몭ltering, content-based, and link
prediction. However, it is important to note that complexity does not
necessarily translate to good performance, and often simple solutions and
implementations yield the strongest results. For example, large companies
like Reddit, Hacker News, and Google have used simple formulaic
implementations of recommendation engines to promote content on their
platform.
Identifying what de몭nes a good recommendation is a challenge that many
companies still struggle with. The de몭nition of “good” recommendations
helps to evaluate the performance of the recommender system that has
been built. The quality of a recommendation can be assessed through
various tactics that measure coverage and accuracy. Accuracy is the fraction
of correct recommendations out of the total possible recommendations,
while coverage measures the fraction of objects in the search space that the
system is able to provide recommendations for. The evaluation method of a
recommendation solely depends on the dataset and approach used to
generate the recommendation.

5. Recommender systems share several conceptual similarities with the
classi몭cation and regression modeling problem. In an ideal situation,
companies would want to see how real users react to recommendations and
track metrics around the user to improve their recommendations. However,
this is often di몭cult to accomplish.
Types of AI­powered recommendation
systems
There are several types of AI-powered recommendation systems, including:
Collaborative 몭ltering systems
Collaborative 몭ltering is a technique used in recommendation systems to
predict the interests and preferences of a user based on the data and
patterns from many users. The basic principle of collaborative 몭ltering is that
if two users have similar tastes in one product, they are also likely to have
similar tastes in other products. There are two main types of collaborative
몭ltering approaches: memory-based and model-based.
Memory-based approaches, also known as neighborhood collaborative
몭ltering, use the ratings of user-item combinations to predict their
preferences based on their neighborhoods. User-based collaborative 몭ltering
recommends products to a user based on the preferences of similar users,
while item-based collaborative 몭ltering recommends products based on the
similarity between items calculated using user ratings of those items.
On the other hand, model-based approaches use predictive models that
involve machine learning to parameterize the features associated with the
dataset as inputs of the model. This helps to solve an optimization-related
problem. Model-based approaches include decision trees, rule-based
approaches, and latent factor models.
Collaborative 몭ltering models are advantageous because they are easy to
implement, provide high-level coverage, and capture subtle characteristics

6. without requiring knowledge of the item content. Some examples of
collaborative 몭ltering algorithms include YouTube’s content
recommendations based on users who have subscribed or watched similar
videos and CourseEra’s course recommendations based on other individuals
who have 몭nished existing courses that a user has completed.
Jack Robert
Recommend this
difference to
Robert
Recommend this
difference to
Jack
Differences
Similarity
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The 몭gure shows the two di몭erent users and their interests, along with the
similarity in their tastes. It is found that both Jack and Robert have similar
tastes, so Jack’s interest is recommended to Robert and vice versa.
Content-based systems
A content-based recommendation system is a system that generates
recommendations based on a user’s preferences and pro몭le by matching
them to items they have liked previously. Instead of leveraging ratings
between the target user and other users, content-based models focus on the
ratings the target user provides. These models establish the level of similarity
between items based on the attributes of items liked by the user. To build a

7. content-based system, you need a strong source of item-level data
associated with the attributes of the item, such as price, published year, etc.,
along with some user feedback on the item, which can be either implicit or
explicit. Content-based models are particularly advantageous when
insu몭cient rating data is available, as they can leverage ratings and item
attributes to generate recommendations. Examples of content-based
systems include Amazon’s product feed, which recommends products similar
to those the user has previously purchased, and Spotify’s music
recommendations. Some companies like Hacker Rank and Reddit have also
used algorithmic approaches to recommend new posts to users based on
factors such as time of post, number of likes, dislikes, and comments, which
can be factored into a formula to generate a score for a post and hence a
recommendation.
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8. The above image shows di몭erent models of the One Plus phone. If a person
is looking for a One Plus 7 mobile phone, then One Plus 7T and One Plus 7T
Pro are recommended to them.
Hybrid recommendation system
Hybrid recommendation systems are designed to overcome the limitations
of individual recommendation systems by combining multiple data sources,
which can be achieved through two di몭erent designs: parallel and sequential.
In the parallel design, multiple recommendation systems are used in parallel
to generate recommendations and their outputs are combined to produce a
몭nal result. A single recommendation engine is used in the sequential design,
and its output is passed on to the next recommender in the sequence.
Hybrid systems o몭er several advantages, including increased robustness and
personalization in user recommendations. By combining di몭erent models,
hybrid systems can mitigate the weaknesses of individual models, leading to
more accurate and diverse recommendations. Net몭ix is a well-known
example of a company that uses a hybrid recommendation system,

9. example of a company that uses a hybrid recommendation system,
combining collaborative 몭ltering (based on user behavior) with content-
based 몭ltering (based on item characteristics) to provide more relevant
movie recommendations to its users.
Knowledge-based system
A knowledge-based system in a recommendation system generates
recommendations based on the user’s needs and domain expertise. It
involves de몭ning rules that set the context for each recommendation, such

10. involves de몭ning rules that set the context for each recommendation, such
as criteria for when a speci몭c product or service would bene몭t the user.
Unlike the content-based approach, these rules do not necessarily rely on a
user’s interaction history but may include other expert information or
attributes of customer products and services.
One advantage of a knowledge-based system is that the recommendations
can be easily explained, making it easier for users to understand why a
particular recommendation was made. However, building this type of
framework can be expensive, and it is better suited for complex domains
where items are infrequently purchased, and data may be lacking. One
bene몭t of this approach is that it does not su몭er from the same cold-start
problems as other methods.
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Benefits of using AI­powered
recommendation systems
Enhancing customer experience through
personalization
Personalization is essential to enhancing customer experience, and a
recommendation engine is vital in achieving this goal. The Epsilon research
report highlights that 80% of consumers prefer purchasing from a brand that
provides personalized experiences, indicating personalization can be a
signi몭cant competitive advantage for businesses seeking to improve
customer loyalty and satisfaction.
In today’s world of information overload, customers are inundated with

11. choices and information, making it challenging to capture their attention.
Personalization can help businesses stand out by providing customers with
relevant, tailored recommendations that meet their unique needs and
preferences. This, in turn, can increase engagement, drive sales, and foster
long-term customer loyalty. A recommendation engine can be an e몭ective
tool for delivering personalized customer experiences. By leveraging data
about a customer’s past behaviors and preferences, a recommendation
engine can suggest products or services most likely to appeal to that
individual, leading to a more seamless and enjoyable customer journey
where customers feel understood, valued, and appreciated.
Ensuring seamless customer experience across multiple
channels
Omnichannel marketing has become increasingly popular recently, as
customers expect seamless service across multiple channels. However, this
presents a signi몭cant challenge for brands – ensuring consistency. According
to Zendesk, 35% of customers expect to receive customer service through
multiple channels.
An AI-powered recommendation engine can be a game-changer to provide a
consistent experience across all channels, including social media, websites,
and mobile apps. The recommendation engine can ensure that users receive
a similar experience everywhere by collecting and utilizing data from
customer interactions on di몭erent channels. For instance, if a customer
searches for a product on your website, the recommendation engine can use
that data to suggest personalized products through email or other channels,
maintaining a consistent experience.
Deliver relevant content
In today’s digital age, delivering relevant content has become key to
customer engagement. Brands that provide personalized content are more
likely to attract and retain customers. Recommendation engines play a
crucial role in helping businesses achieve this goal by showing customized

12. and relevant content to their consumers.
One of the best examples of a brand delivering personalized content is
Medium, an online publishing platform that allows users to select their
preferred topics and, based on their reading behavior, creates customized
content lists for them. This approach ensures that users only see content
they are interested in, leading to higher engagement rates and increased
user satisfaction.
Businesses can tailor their content to each user’s speci몭c interests and
preferences using a recommendation engine which can be done by analyzing
user behavior and data such as search queries, past purchases and browsing
history. With this information, the recommendation engine can provide
personalized recommendations for content that the user is more likely to
engage with.
Minimizing customer frustration in the customer
experience
In today’s fast-paced world, customers have a limited attention span, and
irrelevant content or product recommendations can quickly lead to
frustration and a negative customer experience and loss of potential sales.
An AI-powered recommendation engine can help minimize this frustration by
몭ltering and displaying personalized and relevant content to customers.
By analyzing customer behavior and preferences, a recommendation engine
can provide tailored recommendations that are more likely to engage and
interest customers, improving the overall customer experience and can lead
to increased sales and revenue for businesses. With the help of an AI-
powered recommendation engine, businesses can avoid frustrating their
customers and provide a more personalized and engaging experience.
Meet customer experience expectations
As customer expectations continue to rise, delivering personalized and
seamless experiences has become more important than ever before. In fact,

13. it has become a key determinant of brand loyalty. A report by Microsoft
states that 96% of customers consider customer service and experience as
critical factors in몭uencing their loyalty to a brand.
To meet these expectations, businesses can leverage recommendation
engines. By analyzing customer behavior and preferences, these engines can
provide tailored product recommendations anticipating their needs and
desires. This personalized approach helps build stronger customer
relationships and drive loyalty over time.
In addition, recommendation engines can help to create a consistent
experience across all touchpoints, from the website to social media and
beyond. By providing personalized and relevant content, businesses can
engage customers on their preferred channels and ensure a seamless
experience throughout their journey.
Boost business performance using a recommendation
engine
As a business owner, you want to achieve higher sales, increase the average
order value and ultimately, boost revenue. One e몭ective way to achieve this
is by leveraging the power of a recommendation engine. By showing
personalized product recommendations to your customers, you can increase
the chances of a purchase. An AI-powered recommendation engine can also
enhance your up-selling and cross-selling strategies, making the buying
process smoother and more convenient for your customers.
By implementing a recommendation engine, you can boost business
performance and revenue. With the help of an AI solutions provider, you can
e몭ciently integrate a recommendation engine into your business operations
and enjoy the bene몭ts of increased sales, higher average order value, and
better revenue growth.
How does an AI­powered recommendation
system work?

14. A recommendation engine leverages a combination of machine learning
technology and data to create personalized recommendations. Data serves
as the foundation of a recommendation engine, providing the necessary
information to extract patterns. The more extensive the data, the greater the
engine’s ability to make pro몭table and relevant recommendations.
Recommendation engines complete a standard four-step process:
Step 1: Data collection
To build a recommendation engine, the 몭rst step is to gather data. This can
include both explicit data, such as ratings and comments provided by users,
and implicit data, such as order history, return history, cart events,
pageviews, click-through rates, and search logs. This data is collected for
every user who visits the site.
Behavioral data is relatively easy to collect, as it involves logging user
activities on the site without requiring extra input. However, analyzing this
data can be challenging, as 몭ltering out less relevant logs may be necessary.
Since each user has their own unique preferences, their data sets will also be
distinct. As more data is fed into the engine, it becomes smarter and better
at making relevant recommendations. This is the same principle used by
Amazon’s recommendation engine, which suggests products to users based
on their browsing and purchase history, using features like “Frequently
Bought Together” and “Recommended for You”.
Step 2: Storing the data
In a recommendation system, the quality of recommendations improves as
the volume of data increases. Consequently, recommendation projects often
grow into big data projects due to the sheer amount of data involved.
The type of data used for creating recommendations plays a signi몭cant role
in determining the appropriate storage type. The choice may include a
NoSQL database, a standard SQL database, or an object storage system. The
selection is primarily based on factors such as the type of data captured,

15. ease of implementation, storage capacity, integration with other
components, and data portability.
When storing user ratings or comments, using a scalable and managed
database is crucial to minimize the number of tasks and focus on improving
recommendations. Cloud SQL is an ideal option as it caters to these
requirements and simpli몭es data loading from Spark. Furthermore, with
cloud SQL, you can choose to use PostgreSQL, MySQL, or SQL Server for your
database, depending on your preference and requirements. This 몭exibility is
especially bene몭cial for organizations with unique data storage needs.
Step 3: Analyzing the data
The data is 몭ltered using various analysis methods to 몭nd items with similar
user engagement data in a recommendation system. Depending on the
requirements, di몭erent analysis methods can be used.
For instance, if immediate recommendations are to be provided to the user
while viewing a product, a more agile analysis is needed. Real-time systems
can process data as it is created and is used for giving in-the-moment
recommendations. Tools that can process and analyze streams of events are
usually involved in real-time systems.
On the other hand, batch analysis requires periodic processing of the data.
This approach requires enough data to be created to make the analysis
meaningful, such as daily sales volume. A batch system can be suitable for
sending an email later.
Another approach is near-real-time analysis, which enables data to be
gathered quickly so that the analytics can be refreshed every few minutes or
seconds. A near-real-time system is well-suited for providing
recommendations during the same browsing session.
Step 4: Filtering the data
In a recommendation system, 몭ltering is an essential step that helps to

16. retrieve the relevant data required to provide recommendations to the user.
To do so, we must choose an algorithm, such as content-based, cluster-
based or collaborative 몭ltering, that best suits the recommendation engine.
Content-based 몭ltering suggests products with similar characteristics to what
a user likes or views, while cluster 몭ltering suggests products that go well
together, regardless of other users’ behavior. Collaborative 몭ltering, on the
other hand, makes predictions based on user tastes and assumes that two
users who liked the same products in the past will like the same ones in the
future.
To represent data about ratings or interactions, we can use matrices with
products and users as dimensions. We can then use algorithms such as K-
Nearest, Jaccard’s coe몭cient, Dijkstra’s algorithm, or cosine similarity to
relate the datasets of people based on the ratings or products they interact
with.
After 몭ltering and using the algorithm, recommendations are given to the
user based on the timeliness of the recommendation, whether it’s a real-time
recommendation or an email to be sent later.
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How to build a recommendation system? A
case study in Python using the MovieLens
dataset
Numerous datasets have been gathered and made accessible for research

17. and benchmarking purposes with respect to recommendation systems.
Below is a list of top-notch data sources to consider. For beginners, the
MovieLens dataset curated by GroupLens Research is highly recommended.
Speci몭cally, the MovieLens 100k dataset is a dependable benchmark dataset
with 100,000 ratings from 943 users for 1682 movies. Moreover, each user
has rated at least 20 movies. This extensive dataset comprises various 몭les
that furnish details on the movies, users, and ratings provided by users for
the movies they have viewed.
The ones that are of interest are the following:
u.item: the list of movies
u.data: the list of ratings given by users
Contained within the 몭le “u.data,” are ratings presented in a tab-separated
list that includes user ID, item ID, rating, and timestamp. The initial lines of
the 몭le are as follows:
user_id
196 242 3
891717742
878887116
880606923
886397596
881250949
3
1
2
1
302
377
51
346
186
22
244
166
item_id rating timestamp
As demonstrated previously, the 몭le discloses a user’s rating of a speci몭c 몭lm.
This 몭le holds a total of 100,000 such ratings and will be utilized to anticipate
the ratings of movies that users are yet to see.

18. Building a recommender using Python
Python o몭ers numerous libraries and toolkits with diverse algorithm
implementations for creating recommenders. However, when it comes to
understanding recommendation systems, exploring Surprise is highly
recommended. Surprise is a Python SciKit that o몭ers a variety of
recommender algorithms and similarity metrics. Its purpose is to simplify the
process of constructing and analyzing recommenders.
Here’s how to install it using pip:
$ pip install numpy $ pip install scikit­surprise
Here’s how to install it using conda:
$ conda install ­c conda­forge scikit­surprise
You also need to install Pandas
$ python3 ­m pip install requests pandas matplotlib
Before utilizing Surprise, it’s crucial to familiarize yourself with a few
fundamental modules and classes that it o몭ers:
The Dataset module is utilized for loading data from 몭les, Pandas
dataframes, or even built-in datasets accessible for experimentation. The
built-in MovieLens 100k dataset is one such dataset within Surprise. To
load a dataset, various methods are available, including:
Dataset.load_builtin()
Dataset.load_from_file()
Dataset.load_from_df()
The Reader class is utilized for parsing 몭les that contain ratings. Its default

19. The Reader class is utilized for parsing 몭les that contain ratings. Its default
format accepts data where each rating is stored on a separate line, with
the order being user, item and rating. These order and separator settings
can be customized using the parameters:line_format is a string that stores
the order of the data with 몭eld names separated by a space, as in “item
user rating”.sep is used to specify separators between 몭elds, such as
‘,’.rating_scale is used to specify the rating scale. The default is (1,
5).skip_lines is used to indicate the number of lines to skip at the beginning
of the 몭le. The default is 0.
Below is a program that can be used for loading data from either a Pandas
data frame or the built-in MovieLens 100k dataset:
# load_data.py
import pandas as pd
from surprise import Dataset
from surprise import Reader
# This is the same data that was plotted for similarity earlier
# with one new user "E" who has rated only movie 1
ratings_dict = {
"item": [1, 2, 1, 2, 1, 2, 1, 2, 1],
"user": ['A', 'A', 'B', 'B', 'C', 'C', 'D', 'D', 'E'],
"rating": [1, 2, 2, 4, 2.5, 4, 4.5, 5, 3],
}
df = pd.DataFrame(ratings_dict)
reader = Reader(rating_scale=(1, 5))
# Loads Pandas dataframe
data = Dataset.load_from_df(df[["user", "item", "rating"]], reader)
# Loads the builtin Movielens­100k data

20. # Loads the builtin Movielens­100k data
movielens = Dataset.load_builtin('ml­100k')
In the program above, the data is stored in a dictionary, which is loaded into
a Pandas dataframe and then further into a Dataset object from Surprise.
Selecting the algorithm for the recommender system
To select the appropriate algorithm for the recommender function, it is
necessary to consider the technique being used. In the case of memory-
based approaches mentioned earlier, the KNNWithMeans algorithm, which is
closely related to the centered cosine similarity formula discussed above, is
an ideal choice.
The function must be con몭gured to determine similarity by passing a
dictionary containing the necessary keys as an argument to the
recommender function. These keys include:
“name”: This key speci몭es the similarity metric to be utilized. Available
options are cosine, msd, pearson, or pearson_baseline. The default is msd.
“user_based”: A boolean that indicates whether the approach will be user-
based or item-based. It is set to True by default, meaning the user-based
approach will be used.
“min_support”: This key speci몭es the minimum number of common items
necessary between users to consider them for similarity. For the item-
based approach, it corresponds to the minimum number of common users
between two items.
The following program con몭gures the KNNWithMeans function:
# recommender.py
from surprise import KNNWithMeans
# To use item­based cosine similarity

21. # To use item­based cosine similarity
sim_options = {
"name": "cosine",
"user_based": False, # Compute similarities between items
}
algo = KNNWithMeans(sim_options=sim_options)
The above program con몭gures the recommender function to use cosine
similarity and to 몭nd similar items using the item-based approach.
To use this recommender, you need to create a Trainset from the data.
Trainset is built using the same data but contains more information, such as
the number of users and items (n_users, n_items) used by the algorithm. You
can create Trainset either by using the entire data or a subset of it. You can
also split the data into folds, where some of the data will be used for training
and some for testing.
Here’s an example to 몭nd out how the user E would rate the movie 2:
from load_data import data
from recommender import algo
trainingSet = data.build_full_trainset()
algo.fit(trainingSet)
Computing the cosine similarity matrix...
Done computing similarity matrix.
<surprise.prediction_algorithms.knns.KNNWithMeans object at 0x7f04fec5
prediction = algo.predict('E', 2)
prediction.est
4.15

22. According to the algorithm’s prediction, user E would rate the movie 4.15,
which may be considered high enough to make a recommendation for it.
Endnote
In today’s constantly changing business landscape, companies face 몭erce
competition, which makes it essential for them to do everything in their
stride to stay ahead of the curve. Recommendation systems are one way to
gain an edge and achieve larger business goals like increasing sales,
advertising revenues, or user engagement. However, success with
recommendation systems requires careful consideration of their necessity
and agility.
A good recommendation system will provide value to the business and its
users without wasting valuable resources. Ongoing maintenance may be
necessary in some cases; hence, businesses should weigh the bene몭ts
against the costs before making a decision. The ultimate goal should be a
recommendation system that drives real business value.
Agility is equally important when it comes to recommendation systems. As
users change their behaviors, preferences, and needs, recommendation
systems must adapt to stay relevant and e몭ective. An agile recommendation
system will evolve over time, considering what works, what doesn’t, and what
additional data sources might help improve recommendations.
In conclusion, the success of recommendation systems lies in their necessity
and agility. Businesses that prioritize these qualities when building and
deploying recommendation systems will be better positioned to achieve their
larger goals and stay competitive in an ever-changing marketplace. By
continuously evaluating and improving recommendation systems,
businesses can o몭er users the best possible experience while achieving their
own objectives.
Ready to take customer experience to the next level? Connect with LeewayHertz’s
team of AI experts, who will show you how an AI-powered recommendation

23. system will help!
Author’s Bio
Akash Takyar
CEO LeewayHertz
Akash Takyar is the founder and CEO at LeewayHertz. The experience of
building over 100+ platforms for startups and enterprises allows Akash to
rapidly architect and design solutions that are scalable and beautiful.
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