ii-sdv 2014 recommender systems for analysis applications (roger bradford - agilex technologies,...
TRANSCRIPT
Recommender Systems for Analysis Applications
Roger BradfordAgilex Technologies
14 April 2014
International Information Conference on Search, Data Mining and Visualisation
2
• Customers who Shopped for ' A Tale of two Cities' also Shopped for ….
• Customers Who Bought Items in Your Recent History Also Bought ….
• Users who Enjoyed Titanic also Enjoyed ….
Recommender Systems in Internet Commerce
3
Provider Items RecommendedAmazon Items to BuyFastWeb ScholarshipsLeShop Groceries to BuyNetflix Movies to RentPandora Music to Listen toTripadvisor Places to VisitTwitter People to FollowYaHoo Movies to Watch
Popular Commercial Recommender Applications
4
• Business Strategy Development• Investment Analysis• Risk Analysis• IP Analysis• Fraud Detection• Event Monitoring• Technology Monitoring
Example Analysis Applications
In Analytic Applications, Recommender Systems Primarily Function as
Knowledge Discovery Tools
5
Value of Recommender Systems for Analysis
� Automatically Identify Important Information in Large Quantities of Incoming Data
� Reduce the Cognitive Load on Analysts� Aid in Discovery of New Relevant Information
- that the User didn’t Know to Search for� Produce Alerts about Entities of Importance –
not just more Documents to Read
6
Typical Commercial Applications
Typical Analytic Applications
# of Users >> # of Items # of Items >> # of Users
User Interests are Fairly Stable
User Interests are Dynamic
Unambiguous Indicators are Available
Indicators are Mostly Subtle
Missing a Recommendation Typically has Small Impact
Missing a Recommendation may have a Large Impact
Recommender Application Differences
7
Approach Recommendations Based on
• Collaborative Filtering Actions of Other People
• Content-based Characteristics of Items
• Demographic User Characteristics
• Knowledge-based Example Cases or Constraints
• Community-based Social Networks
• Hybrid Combinations of the Above
Implementation Approaches
8
Incoming Reporting Stream
RecommenderEngine
User-provided Exemplars
XxxxxxxxxXxxxxxxxx
.criminalXxxxxxxxx
...crime..
RecommendedDocuments Recommended
Entities
User Action
Artifacts
Jason Brown
Robert Fisher
Walter Williams
Analytic Recommendation Process
9
Example User Interface
Example Documents
used to Define
Interests
Recommended Items in Relevance Order
Confidence Indictors
A 2011 report issued by the US Geological Survey and US Department of the Interior, "China's Rare-Earth Industry," outlines industry trends within China and examines national policies that may guide the future of the country's production. The report notes that China's lead in the production of rare-earth minerals has accelerated over the past two decades. In 1990, China accounted for only 27% of such minerals. In 2009, world production was 132,000 metric tons; China produced 129,000 of those tons. According to the report, recent patterns suggest that China will slow the export of such materials to the world: "Owing to the increase in domestic demand, the Government has gradually reduced the export quota during the past several years." I
User Feedback Mechanism
Exemplar Management
Console
10
Key Requirements forAnalytic Recommenders
� Quickly Identify and Present Desirable Information to the User without Overwhelming the User with Irrelevant Information.
� Be Flexible Enough to Deal with Variability in Individuals and Activities
� Evaluate Complex Associations Based on Multiple Attributes (Including Metadata)
� Incorporate Data from Multiple Sources.� Begin Making Recommendations Based on
Small Amounts of Data
11
� Accommodate Data Volumes that can be Expected to be Very Large
� Deal with Data that is Sparse, Incomplete, and Noisy.
� Make Explanations of the Reasoning Used in Reaching the Recommendations Available to the User.
� Work with Data from Existing Corporate or Government Data Repositories.
Key Requirements forAnalytic Recommenders (Cont’d)
12
• # of Items >> # of Users • Dynamic Items & User
Interests• High Accuracy & Low Miss
Rate Requirements
Requirements Drive Implementation Approach
Primary Recommendation
Technique must be Content-based
Matrix Factorization is the best Available Content-based Approach
13
� 100 Million Ratings of 17,770 Movies by > 480,000 Users
� $1Million (US) Prize for 10% Improvement� 44,000 Entries, From Over 41,000 Teams� Won by Koren and Bell using a Combination
of Techniques, Featuring Matrix Factorization
The Netflix Challenge
14
Matrix Factorization Advantages*
� Prediction Accuracy Superior to Other Techniques.� Use of a Memory-efficient, Compact Model.� Simple Training.� Natural Ability to Integrate Multiple Forms of User Feedback.� Ability to Incorporate Temporal Dynamics of User Interests
and Item Attributes.� No Reliance on Arbitrary or Heuristic Similarities.� Inherent Protection against Overfitting.� Ability to Capture the Totality of Weak Signals in the Data. � Ability to Incorporate Confidence Levels.� High Scalability.
*Koren & Bell, Recommender Systems Handbook, Springer, 2011
15
Rec
omm
enda
tion
Acc
urac
y C
ompa
red
to B
asel
ine
Degree of Text Corruption
Noise Resilience
16
Search Terms
Viewing an Item
Time Spent Viewing an Item
Saving an Item
Printing an Item
Refining User Interests
Explicit Input Implicit Indicators
Exploit both Positive and Negative Indicators
17
• Accuracy• Confidence Indicators for Recommendations• User Control• Explanation
Contributors to User Confidence
18
Explainability - Documents
19
Explainability - Entities
20
Lists
Tables
Text
Analyst’s Notes:
Identified Relevant
Documents
DocumentsIn
NoveltyOrder
Previously Seen Information
PublishedReports
PreviouslyReviewedDocuments
Novelty in Recommendations
21
Crosslingual Recommendations
Documents in Multiple Languages
FarsiArabic
English
Recommendations in Relevance Order
Recommended Items
22
Acc
urac
y +
Com
plet
enes
sof
Cat
egor
izat
ion
Number of Simultaneous Languages
English Documents &English Examples
Documents in Latin Languages & English Examples
Range of Human
Performance
High-Accuracy Multilingual Recommendations
23
Multimedia Recommendations
Integrated Semantic Analysis
Structured Data
Images
Text Audio
8/18/02500 lbPicric Acid
Saif al Adel
ZaidKhayr
DateAmountMaterialSellerBuyer
Sensor Data
Video
Geospatial DataBiometrics
24
High Performance with Modest HardwareTi
me
in H
ours
Number of Documents
K K KK K
Minimum Latency –Single Processor
Maximum Throughput –16-node Hadoop Cluster
25
� Algorithm Scalability� Conversational Recommender Systems� Context-aware Recommenders� Explanations and Evidence� Preference Elicitation� Privacy and Security� Semantic Web Technologies for
Recommendation� Trust and Reputation
Recommender Topics of Current High Interest
26
� The ACM Recommender System Conference (RecSys 2014), Foster City, California, USA, 6-10 October 2014
� Recommender Systems Handbook , F. Ricci, L. Rokach, B. Shapira, and P. Kantor, Springer Publishing, 2011 118€
� Recommender Systems , P. Melville and V. Sindhwani, In Encyclopedia of Machine Learning, Springer, 2010. Available at: http://www.prem-melville.com/publications/recommender-systems-eml2010.pdf
� Matrix Factorization Techniques for Recommender Systems , Y. Koren, Y., R. Bell, and C. Volinsky, IEEE Computer, August 2009, pp. 42-49. Available at: http://www2.research.att.com/~volinsky/papers/ieeecomputer.pdf
Resources
