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Authors: Rosario Sotomayor, Joe Carthy and John DunnionAuthors: Rosario Sotomayor, Joe Carthy and John Dunnion
Speaker: Rosario SotomayorSpeaker: Rosario Sotomayor
Intelligent Information Retrieval Group (IIRG)Intelligent Information Retrieval Group (IIRG)
UCD School of Computer Science and InformaticsUCD School of Computer Science and Informatics
University College Dublin University College Dublin
IrelandIreland
The IIRG GroupThe IIRG Group University College DublinUniversity College Dublin
The Design and Implementation of an The Design and Implementation of an Intelligent Online Recommender SystemIntelligent Online Recommender System
University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group
An overview of Recommender SystemsAn overview of Recommender Systems
Collaborative Filtering (CF)
Singular Value Decomposition (SVD)
An SVD-CF Approach in the Recommender Systems Domain
The IORS System goals
The IORS Interface
The IORS Architecture
Testing Evaluation
Conclusions/Further work
OutlinesOutlines
University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group
What is a Recommender System?What is a Recommender System?
- Computer-based intelligent technique
- Manages Information Overload
- Used to efficiently provide personalized services in most e-commerce domains
- Supports a customization of the customer experience through the representation of the products sold on a website
- Enables the creation of a virtual world store personally designed for each customer
The Goals of a Recommender SystemThe Goals of a Recommender System:
- Generate suggestions about new items
- Predict the usefulness of a specific item for a particular user
An overview of Recommender SystemsAn overview of Recommender Systems
University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group
Recommender Systems in research systemRecommender Systems in research system ::
- - GroupLens- - Movielens
Recommender Systems inRecommender Systems in commercial use :commercial use :
- Amazon.com
- CDNOW
- Pandora
- Media Unbound
An overview of Recommender SystemsAn overview of Recommender Systems
University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group
Amazon.comAmazon.com
An overview of Recommender SystemsAn overview of Recommender Systems
University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group
Pandora:Pandora:
An overview of Recommender SystemsAn overview of Recommender Systems
University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group
An overview of Recommender Systems
Collaborative Filtering (CF)Collaborative Filtering (CF)
Singular Value Decomposition (SVD)
An SVD-CF Approach in the Recommender Systems Domain
The IORS System goals
The IORS Interface
The IORS Architecture
Testing Evaluation
Conclusions/Further work
OutlinesOutlines
University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group
Collaborative Filtering (CF):Collaborative Filtering (CF): A promising Recommender System technology. Used in many of the most successful Recommender Systems on the web
Collaborative filtering (CF)Collaborative filtering (CF)
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f
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University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group
Consists of a number of Sub-Tasks:Consists of a number of Sub-Tasks:
- Representation
- Neighborhood formation
- Recommendation generation
Applications of CFApplications of CF:
- E-commerce : - Amazon.com (item-to-item collaborative filtering)
- CDNow
LimitationsLimitations:
- Scalability
- Sparsity
Collaborative filtering (CF)Collaborative filtering (CF)
University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group
An overview of Recommender Systems
Collaborative Filtering (CF)
Singular Value Decomposition (SVD)Singular Value Decomposition (SVD)
An SVD-CF Approach in the Recommender Systems Domain
The IORS System Goals
The IORS Interface
The IORS Architecture
Testing Evaluation
Conclusions/Further work
OutlinesOutlines
University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group
Singular Value Decomposition (SVD)Singular Value Decomposition (SVD): Dimensionality reduction technique
Filters the useful data from the noise in large data sets.
ApplicationsApplications:
- Information retrieval: Latent Semantic Indexing (LSI)
- Recommender systems- Real-time signal processing- Seismic reflexion tomography
Latent Semantic Indexing (LSI):Latent Semantic Indexing (LSI):
- SynonymySynonymy: “There are many ways to refer to the same object” - Polysemy- Polysemy: “Most words have more than one distinct meaning”
Singular Value Decomposition (SVD)Singular Value Decomposition (SVD)
University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group
X T0
S0
D0
t x d t x r r x r r x d
· ·
=
term
s
documents
0
0
Singular Value Decomposition (SVD)Singular Value Decomposition (SVD)
X = T0 · S0 · D0
University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group
Singular Value Decomposition (SVD)Singular Value Decomposition (SVD)
X = T0 · S0 · D0WhereWhere:
T0 , D0 = orthogonal matrices
r= rank of the matrix X
S = diagonal matrix = singular values of matrix X
University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group
S0
0
0
Singular Value Decomposition (SVD)Singular Value Decomposition (SVD)
interesting evidence of latent structure
noise, coincidences, anomalies, …
University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group
X T0
S0
D0
t x d t x r r x r r x d
· ·
=
term
s
documents
0
0
Singular Value Decomposition (SVD)Singular Value Decomposition (SVD)
X = T0 · S0 · D0
X T
S
D
t x d t x k k x k k x d
· ·
term
s
documents q
0
0
T0·S·D0 = X T·S·D
Singular Value Decomposition (SVD)Singular Value Decomposition (SVD)
University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group
University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group
An overview of Recommender Systems
Collaborative Filtering (CF)
Singular Value Decomposition (SVD)
An SVD-CF Approach in the Recommender Systems DomainAn SVD-CF Approach in the Recommender Systems Domain
The IORS System Goals
The IORS Interface
The IORS Architecture
Testing Evaluation
Conclusions/Further work
OutlinesOutlines
University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group
ScenarioScenario:
- Customers and their sets of products
Dimensionality reduction technologyDimensionality reduction technology :
- Singular Value Decomposition (SVD) :
- Obtain less noisy reduced orthogonal dimensions
- To capture latent relationships between customers and products
Collaborative filtering:Collaborative filtering:
- To retrieve relevant information
An SVD-CF Approach in the An SVD-CF Approach in the Recommender System DomainRecommender System Domain
University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group
An overview of Recommender Systems
Collaborative Filtering (CF)
Singular Value Decomposition (SVD)
An SVD-CF Approach in the Recommender Systems Domain
The IORS System The IORS System GoalsGoals
The IORS Interface
The IORS Architecture
Testing Evaluation
Conclusions/Further work
OutlinesOutlines
The Intelligent Online Recommender The Intelligent Online Recommender System (IORS) goalsSystem (IORS) goals
Reduce the SparsityReduce the Sparsity
Improve the quality of feedbackImprove the quality of feedback
Retrieval Time Reduction:Retrieval Time Reduction:
- Timely feedback
Search Shaping:Search Shaping:
- Anticipate user wishes
- Reduce the noise generated by large quantities of data
- Support the user in the process of selection
University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group
The Intelligent Online Recommender The Intelligent Online Recommender System (IORS) goalsSystem (IORS) goals
Unveiling of New PreferencesUnveiling of New Preferences:
- Customers can take advantage of new relationships among users and products.
Interactive GUI feedback:Interactive GUI feedback:
- Filters in different fashions
University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group
University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group
An overview of Recommender Systems
Collaborative Filtering (CF)
Singular Value Decomposition (SVD)
An SVD-CF Approach in the Recommender Systems Domain
The IORS System Goals
The IORS InterfaceThe IORS Interface
The IORS Architecture
Testing Evaluation
Conclusions/Further work
OutlinesOutlines
The IORS InterfaceThe IORS Interface
University College DublinUniversity College Dublin
V
The IORS InterfaceThe IORS Interface
University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group
University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group
An overview of Recommender Systems
Collaborative Filtering (CF)
Singular Value Decomposition (SVD)
An SVD-CF Approach in the Recommender Systems Domain
The IORS System Goals
The IORS Interface
The IORS ArchitectureThe IORS Architecture
Testing Evaluation
Conclusions/Further work
OutlinesOutlines
The IORS ArchitectureThe IORS Architecture
University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group
University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group
An overview of Recommender Systems
Collaborative Filtering (CF)
Singular Value Decomposition (SVD)
An SVD-CF Approach in the Recommender Systems Domain
The IORS System Goals
The IORS Interface
The IORS Architecture
Testing EvaluationTesting Evaluation
Conclusions/Further work
OutlinesOutlines
Testing EvaluationTesting Evaluation
Current testing is being done in order to measure the accuracy of SVD-CF methods. In order to do so, real data in sufficient quantity is being collected
University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group
University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group
An overview of Recommender Systems
Collaborative Filtering (CF)
Singular Value Decomposition (SVD)
An SVD-CF Approach in the Recommender Systems Domain
The IORS System Goals
The IORS Interface
The IORS Architecture
Testing Evaluation
Conclusions/Further workConclusions/Further work
OutlinesOutlines
Conclusions/Further workConclusions/Further work
CF is one of the most successful recommender system technologies, widely popular among e-tailers sites
Recommender system technologies have become stretched by the huge volume of user information and are becoming even more stretched with the growth of Internet domain
SVD plays a key role in the recommendation process of our system by addressing the gap left by collaborative filtering during the processing of high quantities of data
It is important for SVD method that the derived k-dimensional factor space does not reconstruct the original term space perfectly, since the original set is deemed to be unreliable
University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group
Further testing is required to understand the different results found when the k factor varies
Further work is required to exploit SVD for item selection in order to find possible hidden relations among items
University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group
Conclusions/Further workConclusions/Further work
The EndThe End
www.cs.ucd.ie
Thank you!
University College DublinUniversity College DublinThe IIRG GroupThe IIRG Group
