Recommender Systems

Recommender Systems
10/28/2010
1
Talis

Abstract
The amount of data in the digital universe is estimated to hit 1.2 Zettabytes (1 billion terabytes) during 2010.
These data quantities make discovering relevant information a difficult task.
Recommender Systems are an integral tool for assisting users in information discovery.
By combining wisdom of crowds, content, user profiles, etc. Recommender Systems find relevant data for us.
“We are leaving the age of information and entering the age of recommendation”
Chris Anderson, The Long Tail
10/28/2010
Talis
2

Outline
Introduction
Standard recommenders
Content-based
Collaborative filtering-based
Hybrid recommenders
Context-aware recommenders
Recommenders at Talis
10/28/2010
Talis
3

Introduction
IMDb, one of the first online recommender systems, turned 20 on October 17th 2010.
Ever since, recommender systems have, through relatively simple techniques, produced adequately good results
Is adequately good good enough?
How can recommender systems be improved?
What do we need to improve them?
10/28/2010
Talis
4

Recommender System TypesIntroduction
Semantic recommenders – explicit information
Content
Keywords
Genre
etc.
Social recommenders – implicit information (collaborative filtering)
Item-based user-user similarities, i.e. which users like similar things
Content-ignorant
Hybrid recommenders
Combinations of content- and CF-based
Aware of the current situation
10/28/2010
Talis
5

Content-based recommenders
10/28/2010
Talis
6

Social recommenders
Most common recommender systems approach use Collaborative Filtering
How does collaborative filtering work?
Calculates similarities between all users
Finds users similar to you
Fills in your ”gaps” based on similar users, usually by a k-nearest neighbor algorithm
10/28/2010
Talis
7
Recommend a book for user C

Social recommenders
10/28/2010
Talis
8

Social recommenders
10/28/2010
Talis
9

Hybrid models
Hybrid recommender systems combine semantic recommenders with collaborative filtering ones.
10/28/2010
Talis
10

Hybrid models
Hybrid recommender systems combine semantic recommenders with collaborative filtering ones.
10/28/2010
Talis
11

Context-awareness
Is an item as relevant on a Sunday afternoon as on a Tuesday morning?
10/28/2010
Talis
12

What is context?Context-awareness in RecSys
”Any information that can be used to characterise the situation of entities”, Dey 2001
Item context
Seasonal (Christmas, Oscar’s)
Relation (movie sequel, director, actor)
User context
Surroundings (weather, location)
Company (alone, with friends)
Mood/emotions
any user related factor
10/28/2010
Talis
13

Why Context?Context-awareness in RecSys
10/28/2010
Talis
14
+
Filters relevant information
Ad hoc recommendations
Aware of changes
-
What is context?
Where do we find it?

Applying Context-awareness
Current state of the art research presents two types of context-awareness:
Context-aware collaborative filtering
Performs standard CF on virtual, contextual, items or users
Benefits: simple
Drawbacks: statically defined context
10/28/2010
Talis
15

Applying Context-awareness
Current state of the art research presents two types of context-awareness:
Context-aware collaborative filtering
Performs standard CF on virtual, contextual, items or users
Benefits: simple
Drawbacks: statically defined context
Tensor factorization for context-awareness
Models the data as a tensor
Applies higiher-order factorization techniques (HoSVD, PARAFAC, HyPLSA, etc) to model context in a latent space
Benefits: no prior context identification necessary
Drawbacks: adds complexity
10/28/2010
Talis
16

My work
10/28/2010
Talis
17
Semantic recommenders
Social recommenders

Where does this fit at Talis?
Library data
Loan events – CF
Book meta data – semantic recommenders
Time of loan event – context-awareness
10/28/2010
Talis
18

Distributed higher order recommender system
Use matrix factorization techniques to make a tensor factorization approximation in MapReduce
By matricizing the tensor, standard matrix factorization approaches can be run in parallel
What is matrix factorization?
Decomposition of a matrix into its building blocks (SVD example)
A = UΣVTwhere A is the matrix, Σ is a diagonal matrix and U and V are unitary matrices.
By only taking the k first diagonal values in Σ and multiplying the resulting matrix back with U and V we obtain a k ranked approximation of the initial A matrix
10/28/2010
Talis
19
meta data
book
user

Questions?
10/28/2010
Talis
20
Thank you!

Recommender Systems

by Alan Said on Oct 28, 2010

Accessibility

Categories

Tags

Upload Details

Usage Rights

Statistics

Recommender Systems — Presentation Transcript