Increasingly, Recommendation engines, with their ability to predict consumer needs are proving to be key differentiators for businesses. I thought you might be interested to know how you can use recommendations to drive business and improve customer experience.

1. WHITEPAPER
Recommender Systems
Datashop Recommend: Introducingthe next generation
of relevant, real-time recommendations

2. INTRODUCTIO
N
Nothing replaces the holy grail of marketing, word of mouth. However, timely
recommendations help us add that one extra item to our shopping cart,
choose that one last Youtube video to watch before calling it a night, and
Facebook friend that long lost classmate from elementary school.
How do Amazon, Youtube/Netflix/Hulu, Facebook/Twitter/LinkedIn know
just what we need? Is it an apparition of the all-encompassing “Big Data”? In a
sense, yes.
The value proposition for recommenders is so compelling that
recommendation engines would seem an obvious technology for any
e-commerce company to integrate in order to drive material increases in
key metrics such as order sizes, time spent on site, socialshares, as well as marked
decreases in cart abandonment rates.
However, the main challenges behind integrating recommender systems
lie in implementing their complex algorithms as well as understanding the
subtle nuances and tradeoffs of the various types of systems.
This white paper aims to demystify the inner workings of recommenders
and unveil our take on a plug-n-play, next generation real-time recommender
system.
Datashop Recommend | Relevant real-time recommendations 2

3. WHY
RECOMMENDATIONS
Recommender systems have
helped businesses improve
average cart sizes, time spent on
sites, and customer loyalty.
Recommender systems provide relevant choices to consumers, be it a
choice to buy a product or read a news article or listen to a song.
From a business perspective, these systems drive increases in revenue from
additional shopping cart items, increases in publicity through social shares of
articles, and generally help increase key operating metrics.
THE
EVOLUTION
Early recommender systems were developed using content-based
approaches that recommended items most similar to ones that users have shown a
proclivity to in the past.
With the advent of larger datasets and greater computing power, a technique
called collaborative-filtering was developed which recommended items
based on how groups of users rated similar items in the past.
Both systems come with strengths and weaknesses which led to the
creation of a hybrid system that brings together elements from both
techniques.
Datashop Recommend | Relevant real-time recommendations 3

4. HOW THEY
WORK
Recommender systems evaluate
the probability of a user liking a
particular product. The systems
are optimized to produce
recommendations that increase a
specific metric such as shopping
cart size or time spent on a site.
Fundamentally, recommender systems work by evaluating how likely a user
is to like a content/product presented to them. The calculations behind the
system are driven by what we know about the user or the product. Generally,
the more we know, the better the recommendation. Recommender systems
are designed to optimize for specific desired outcomes such as increasing
the average shopping cart size, time spent on a website, or number of
songs listenedto. Core differentiations between recommender systems
are primarily based on what each system knows about the user/product
and how they subsequently drive recommendations to optimize desired
outcomes.
Content-Based Filtering
These systems keep track of an item’s attributes and match them against a
user’s profile to recommend items that are most similar to what a user has
liked in the past. For example, Pandora keeps track of attributes such as
the type of song, artist,beats per minute, and other descriptors which are
ranked against the listener’s history to make recommendations on what to
listen to next based on matching song attributes to the listener’s history.
Collaborative Filtering
Rather than relying on the preferences and tastes of a single person, this system
makes recommendations by analyzing how groups of people have rated a product
or how they feel about a certain song. For example, Last. fm creates stations
based on what songs groups of people listen to in sequence. It then makes
recommendations on what to play next based on what other people have
listened to subsequently.
Datashop Recommend | Relevant real-time recommendations 4

5. Hybrid Filtering
Hybrid filtering models use the best of both worlds – Content- Based (CB) and
Collaborative Filtering (CF). To calibrate these models,data scientists either
combine recommendations from CB and CF models, add content- based
capabilitiesin collaborative filter models, or add collaborative filtering capabilities
in content based models.
This type of filtering is primarily used by the likes of Amazon and Netflix to
produce recommendations.
Common Challenges in Current Recommender Engines
Cold Start: Recommender systems require a lot of data to produce accurate
recommendations. In cases where the user or productbase is small, the system
finds it difficult to produce accurate recommendations.
Sparsity: When new songs or items are added to a site, it needs to
be rated by a substantial number of users beforeit can show up as a
recommendation. Thus, without a strong base of ratings, recommenders won’t
have the data to draw from to produce recommendations.
Datashop Recommend | Relevant real-time recommendations 5

6. NEXT GENERATION RECOMMENDER
SYSTEMS
Recommender systems continue to be a major area of research and
innovation. Some of the newer approaches have attempted to apply neural
networks and deep learning techniques to recommender systems. Such approaches
have resulted in improved recommendations but are hard to train and validate.
The most promising approach is that of context-aware recommenders.
A holistic approach that takes in
disparate pieces of information,
connects them together, and
produces recommendations that
are better informed and more
accurate.
A context-aware recommender factors in multiple variables including user
context, pricing, location, social channels, and other information to produce a
highly relevant, timely recommendation.
This holisticapproach takes in disparate pieces of information, connects
them all together, and produces a recommendation that is better informed and
more accurate than ones created by content-based or collaborative filtering
methods.
Datashop Recommend | Relevant real-time recommendations 6

7. DATASHOP
RECOMMEND
Using the latest in data science methods including probabilistic graph
models, we developed Datashop Recommend which brings the power of
Context-Aware solutions to enable more effective and accurate
recommendations in:
Datashop Recommend uses
state-of-the-art graph models
to bring powerful context-aware
recommendations.
E-
COMMERCE
Reduce cart abandonment rates and increase basket
sizes by recommending more appropriate items and add-
ons
MEDIA Increase video viewership and social shares by
suggesting content that is attune to a user’s complete
profile
MOBILE APPS Increase acquisition and retention metrics by
recommending app shares to highly relevant friends
FINANCIA
L
SERVICES
Offer more relevant and timely financial products to
customers
We incorporate the latest advances in recommender systems theory with
billions of data pointson customers, products, social media interactions, and
product discussions to provide the best recommendations for businesses.
Datashop Recommend | Relevant real-time recommendations 7

8. ABOUT
INNOVACCER
At Innovaccer, we create products that transform the way organizations use
their data. Our products are deployed at critical government, commercial,
and non-profit institutions around the world to solve sophisticated and world
changing problems.
Datashop is as our core technology that powers our data-driven solutions.
If you have any questions or would like to learn more, feel free to contact us:
info@innovaccer.com
+1 714 729 4038
San Francisco | Palo Alto | Delhi
© Innovaccer Inc 2015
Innovaccer, Innovaccer Inc, and Innovaccer Datashop are trademarks of Innovaccer Inc. All other
company and product names may be trademarks with which they are associated with. Datashop
Recommend is a proprietary technology and Intellectual Property of Innovaccer.