recommendation engine for next generation

WHITEPAPER

Recommender SystemsDatashop Recommend: Introducing the next generation of relevant, real-time recommendations

INTRODUCTION

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, social shares, 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

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.


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 listened to. 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.


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

capabilities in 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 product base 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 before it 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.


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 holistic approach 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

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

FINANCIAL

SERVICES

Offer more relevant and timely financial products to

customers

We incorporate the latest advances in recommender systems theory with

billions of data points on customers, products, social media interactions, and

product discussions to provide the best recommendations for businesses.


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:

[email protected]

+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.

mailto:[email protected]






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