synthese recommender system
TRANSCRIPT
A Hybrid, Multi-Dimensional Recommender for Journal Articles
in a Scientific Digital Library
Andre [email protected]
Canada Institute for Scientific and Technical Information
National Research Councilhttp://lab.cisti-icist.nrc-cnrc.gc.ca/ WPRS 07
2 November 2007
David [email protected]
Dept. of StatisticsCornell University
2
Outline of Talk
• Introduction and Motivation
• Problems for Article Recommender Systems
• Proposed Solutions: – Hybrid of Collaborative Filtering (CF) +
Content Based Filtering (CBF)– PageRanked Citations– Multi-Dimensional based on IR Modes– Explanation-Based Interface
• Future Work
3
Introductionand Motivation
• CISTI – who we are– Government Science Library for Canada– Publisher of 16 science journals– Digital Collection
• 3667 Journals• 6,400,000 articles
• Motivation for this project– To enhance the process of scientific innovation by providing high-
quality, serendipitous, article recommendations
4
Typical Issues with CF Recommenders
• Data Sparsity– Ratio of Users / Items is low (~ 1:10)– Number of Ratings per User is low– Ratings matrix sparsity ~ 95%
• Cold Start Problem– First-time users get poor or no recommendations because CF
matrix has no entries• Rating Items
– CF recommender must be trained (explicitly or implicitly) by providing ratings to items
• Principle of Induction– People who exhibited similar behaviour in the past will tend to
exhibit similar behaviour in the future.
5
Specific Issues for Science Digital Libraries
• Data Sparsity– More Articles & Fewer Users (10x) – Fewer Item / Ratings (~ 99% sparsity)
• Rating Articles– Explicit ratings are more difficult to obtain
• DL users have less need to “express themselves” by explicitly rating items than movie watchers
– Implicit ratings depend on UI features of DL• No reliable method for inferring ratings from browsing and
query behaviour
• Principle of Induction not necessarily true in DL context– Interest drift– Context shifts
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General Research Strategy
• Follow in footsteps of TechLens+– “Fusion Mixed Hybrid” : CF + CBF – Seed CF recommender with citation matrix– Incorporate explanation feature in results interface
• With Extensions– PageRank on Citations– User IR modes and projects– Implicit user ratings derived from clickstream + project + mode– Distributed Multi-Dimensional Recommender– Explanation-based interface
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Recommender Citation Seeding
• Articles either cite or don’t cite other articles• Some articles that are cited are not in collection• Users’ “article collection profile” citations
TechLens approach to Cold Start / Data Sparsity problem
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Apply PageRank to Citation Matrix
• PageRank algorithm applied to citations
• d – damping factor = 0.85
• PR() – PageRank score of article • B() – articles that that cite • N – number of citations for article
47.5
135
87.5
47.5
47.5
87.5
87.5
)(
)()1()(
Bv vN
vPRddPR
Aurel Constantinescu “Ranking Full-Text Articles using Citation Based Methods” Master’s Thesis, University of Ottawa
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PageRank-weighted Citation matrix
• Apply Page Rank on Citations– Use citation data (as in TechLens+)– Apply PageRank to weight the citation-based “ratings”
• Done before but only at the Journal level (http://www.eigenfactor.org/)
0.4 0.5 0.4
0.2 0.6
0.7 0.5 0.5 0.3 0.6
0.2 0.3
p6p1 p5p2 p4p3
u2
p1
u1
p2
p4
p3
articles
citationsp7 p8
= constantusers
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User Project Profiles &IR Modes
Project Profiles• Explicit User-defined Projects
– Subject-matter expertise (Novice / Knowledgeable / Expert)
• Defined by a document collection that characterizes the project:– By content - the feature vectors (bag of words) from that collection– By CF similarity from “citations” list for the user
IR Modes• Users of DLs have a broad range of IR goals, such as
– seeking answers to highly specific scientific questions– developing literature surveys– establishing prior art for patent claims
• “innovation” / “information” / “authority”
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Implicit Preferences Generation In Context
Search Terms
Full Text
Author
Keyphrase
Journal
Abstract
Project
IR Mode
Clickstream
User State
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Multi-Dimensional Ratings Matrix
Tom
Alice
Bob
Carol
p1
p2
p3
p4 p5p6
InnovationInformation
Authority
0.3
0.6
0.3
0.7
0.4
0.7
0.2
G. Adomavicious, R. Sankaranarayanan, S. Sen, A. Tuzhilin, ACM Transactions on Information Systems 2005Incorporating Contextual Information in Recommender Systems Using a Multidimensional Approach
0.7
0.2
0.5
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Scaling Strategy: Distributed Recommenders
• Multiple ratings matrices decomposed by subject area
• Merge separate recommendations by subject
• Reduces matrix sparsity
• Improves accuracy of recommendations
Distributed Collaborative Filtering with Domain Specialization S. Berkovsky, T.Kuflik, and F. Ricci Proceedings of RecSys2007
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UI for Navigating Recommendations
• Inspiration for “recommendation refinement” UI– Carrot2 Cluster maps
• Explanation-based Recommendations– Provide transparency increase user trust– Take advantage of explanation to:
• Allow users to cluster by type of cause• Filter out recommendations
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Carrot2 Cluster maps
2D projection of RecommendedItem-User Similarity
ExplanationClusters
Dimensionality weighting slider
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Future Work
• V.1 of Synthèse– PageRanked Citations– Mixed Hybrid Recommender
• Study effect of PageRank on Recommendation Rankings• Build v.2 of Synthèse
– Modal user profiles– Distributed Multi-Dimensional Recommender
• Study effect of Multi-Dimensionality• Explore distributed recommender strategy• Study impact of additional information
– Author Hirsch Index / “Faculty of 1000” Article Ratings
• Refine content-based filtering with domain-specific semantics• Incorporate privacy protection measures to ensure anonymity
THANK YOU!Questions?
http://lab.cisti-icist.nrc-cnrc.gc.ca/