Data Science

Qloo creates highly structured data science on consumer taste and entity attributes across the largest categories of entertainment, culture, and consumer products.

Machine Learning

To process the data in real-time, Qloo uses proprietary machine learning algorithms that utilize leading statistical methodologies, rooted in the latest research in the emerging field of Neuroaesthetics — these include: deep learning methods, Bayesian statistics, neural networks, and proprietary NLP algorithms. We optimize for correlations that are meaningful and uniquely correlated (known as “anomalous co-occurrences”). Proprietary recursive training models are used to validate insights, properly partitioning all training data to avoid overfitting. Qloo connects over a dozen data domains in search of uniquely relevant insights to drive true excellence in programmatic cultural intelligence.

Our Deep Learning data science works across three distinct layers:

Qloo data stack

Layer 1

Entity Database

THE CULTURE DATABASE

Qloo maintains a database of cultural entities that includes a detailed ontology of all attributes pertaining to each entity. The goal here is to understand the fundamental DNA of each cultural entity, (whether book, movie, product or hotel) by mapping and structuring, and scoring the most important attributes.

Below is a breakdown of entity counts by category, Qloo’s AI currently services more than 150 million primary entities along with all their associated attributes). These primary entities are available turn-key as input signals into the Qloo API.

CategoryTotal Number Of Entities
Music: Artists, Albums, Songs142,500,000+
Film: Movies, Actors, Directors4,500,000+
TV: TV Shows, Actors1,150,000+
Dining: Restaurants, Cafes2,950,000+
Nightlife: Bars, Clubs950,000+
Fashion: Brands, Designers, Stores550,000+
CategoryTotal Number Of Entities
Books: Book Titles, Authors
27,500,000+
Travel: Destinations, Hotels, Attractions4,700,000+
People: Public Figures, Influencers375,000+
Media: Apps, Podcasts, Games
1,550,000+
Products: Brands, Products
85,000,000+
Sports: Athletes, Teams25,000+

CONTENT BASED STRUCTURED DATA

For every entity, Qloo maps a rigorous ontology of its most salient attributes, and cross references this to all other entities within the domain and even across domains.

For instance, for two entity types, (a music artist and a restaurant) the below table shows the top level attributes that Qloo might maintain for each entity.

Music

ARTIST

Parent Genre
Biographical Details
Sub-GenresMusical Characteristics
BandmembersLive Performances
Record LabelChart Statistics
Discography Influenced
Track NamesInfluencers
InstrumentsCollaborators

dining

Restaurant

Cuisine
Review Highlights
Address
Owner
Business Attributes
Dress Code
Hours of Operation
Price Levels
Other Locations
Noise Levels
All Menu Items
Ambience
Dietary Details Similar To

Layer 2

TASTE SIGNALS

ENTITY BASED PERSONALIZATION

Qloo API processes anonymized batch signals across culture. Qloo API is fundamentally GDPR-compliant as a data processor.

ENTITIES

Input signal into Qloo’s API can be any of the 150+ million entities pre-mapped, and can also be attributes.

Examples of raw signals include:

  • Unique Items Fashion Brand, Music Artist, Hotel Name
  • Meta-Level Classifications Movie Genre, Book Genre, Restaurant Cuisine
  • Demographic Endpoints and Custom Personas Gender, Age, Lifestyle Traits
  • Custom Endpoints
R

Response

Desired response is delivered in less than 50 milliseconds on average along with any delimiting fields, appended attributes and parameters

M

Model Training

Qloo then retrains models for subsequent calls based on batch anonymized input signals. Primary machine learning involves proprietary neural nets.

Layer 3

DEEP LEARNING

DEEP INSIGHTS

Qloo develops custom routes for enterprises to address specific value-adds. These models will typically combine primary taste signals with Layer 1 attributes to create models that address unique use-cases.

Some examples of models that have been developed for clients include the following routes:

Content Themes
Social Issues
Generational Affinity
Mainstream/NoveltyTravel PreferencesLifestyle Preferences
Introversion/Extraversion
Price Sensitivity Genre Preferences
VanityOpennessPropensity to Consume
Political LeaningsIrony/SincerityMedia Preferences

Further reads from qloo engineering

Popular evaluation metrics in recommender systems explained by Giorgos Papachristoudis, PHD – Chief Data Scientist


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