Recommender Systems

Anastasiia Kornilova

Agenda

Problems

Evaluation

Algorithms

General overview

We are overloaded of information:

• Books • Movies • News • Blogs • TV-channels • Music • …

As user:

• Do we need all of this things?

• No

• Can we choose most appropriate of them?

• Yes

• How?

• Recommender systems!

As Business Owner:

Do you need Recommender System?

• Netflix:

• 2/3 rented movies are from recommendation

• Google News

• 38 % more click-through are due to recommendation

• Amazon

• 35% sales are from recommendation

•Celma & Lamere, ISMIR 2007

Domains of recommendations

Content to Commerce

• Information

• News

• Restaurants

• Vendors

• TV-programs

• Courses in e-learning

• People

• Music playlists

One particularly interesting

property

• New items (movies, books, news, ..)

• Re-recommend old ones (groceries, music,…)

Examples: Retail

Examples: Banking

Deposits:

Deposit 1

Deposit 2

Credit products:

Credit card 1

Credit card 2

Personal loan 1

Personal loan 2

Insurance:

Endowment

Travel insurance

Service packages:

Premium package

Customer 2

Premium package

Travel insurance

Credit card 1

Customer 1

Travel insurance

Personal load 2

Deposit 1

Consultant can be replaced by Recommender System

Examples: Hotels

Examples: Advertisement

Shopping (browsing) history RSE

Purposes of Recommendation

Recommendations themselves (Sales, information)

Education of user/customer

Build a community of users/customers around products or content

Whose Opinion?

“Experts”

Ordinary “phoaks”

People like you

Personalization Level

Generic/Non-Personalized: everyone receives same recommendations

Demographic: matches a target group

Ephemeral: matches current activity

Persistent: matches long-term interests

Explicit input based RSE

Rating

Review Vote

Like

Implicit input

based RSE

Click

Purchase

Follow

Recommendation Algorithms

1. Non-Personalized Summary Statistics

2. Content-Based Filtering

Information Filtering

Knowledge-Based

3. Collaborative Filtering

User-user

Item-item

Dimensionality Reduction

4. Others

Critique / Interview Based Recommendations

Hybrid Techniques

Non-Personalized Recommender

Best-seller

Most popular

Trending Hot

Best-liked

People who X also Y

Personalized Recommender:

Collaborative Filtering

Use opinions of others to predict/recommend

User model – set of ratings

Item model – set of ratings

Common core: sparse matrix of ratings

Evaluation

Lift Cross-sales Up-sales

Conversions Accuracy Serendipity

Problems

“Cold start”

New user

New item

New system