Research recommendations at Mendeley
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Research recommendations at Mendeley Marco Rossetti Data Scientist @ross85 24/11/2015 Elsevier, Amsterdam
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Outline
Research recommendations at Mendeley
• What is Mendeley
• Recommender Systems at Mendeley – Why
– Data Sources
– Algorithms
– Business Logic & Analytics
– User Interface 24/11/2015
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What is Mendeley
Research recommendations at Mendeley 24/11/2015
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Mendeley builds tools tohelp researchers …
Research recommendations at Mendeley
Read &
Organize
Search &
Discover
Collaborate &
Network
Experiment &
Synthesize
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Read & Organize
Research recommendations at Mendeley
Reference management
Cite-as-you-
write
Full-text article search
Digitalised
annotations
24/11/2015
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Search & Discover
Research recommendations at Mendeley
Mendeley Suggest
Literature
Search
Related Documents
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Collaborate & Network
Research recommendations at Mendeley
Research network
Groups
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Mendeley & Elsevier
Research recommendations at Mendeley 24/11/2015
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Recommender Systems
Research recommendations at Mendeley 24/11/2015
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Why Recommender Systemsat Mendeley?
Research recommendations at Mendeley
Vision: “To build a personalised research advisor that helps you to organise your work, contextualise it within the global body of research, and connect you with relevant researchers and artifacts.”
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Recommender Systemsat Mendeley – Related Documents
Research recommendations at Mendeley 24/11/2015
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Recommender Systemsat Mendeley – Mendeley Suggest
Research recommendations at Mendeley https://www.mendeley.com/suggest/
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Recommender SystemComponents
Research recommendations at Mendeley
Algorithms
Business Logic and Analytics
User Experience
Data Sources Algorithms
Business Logic
& Analytics
User Interface
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• Mendeley – User Libraries
• What the users have in their libraries (what they read, what they annotate, what they highlight, what folders they have, etc. etc.)
– Articles metadata (title, authors, abstract, keywords, tags, etc. etc.) – Groups
• Scopus – Citation network
• Science Direct – Logs
• …
Data Sources
Research recommendations at Mendeley 24/11/2015
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Algorithms
Research recommendations at Mendeley
1. Collaborative filtering User-based
If Alice read X, Y, Z and Bob read X, Y, Z and W, we recommend W to Alice + Efficient for us because users << items - Only for users with enough articles in the library
Item-based Users who read X also read Y + Item-item similarity matrix is useful to model last n articles read - Expensive in our setting (millions of items)
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Algorithms [2]
Research recommendations at Mendeley
1. Collaborative filtering (still) Matrix factorization
+ Best CF model in literature - A lot of latent factors, generate recommendations on a catalog of million of items is too slow
1 1 11 1 1? ? 1 ? 1 ?
1 1 11 1
1 1 1
U n x k
V k x m
X n x m
X ≈
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Algorithms [3]
Research recommendations at Mendeley
2. Content-based I read articles about text mining, show me other stuff about text mining + Good for semi-cold users (users with only a few articles) - Overspecialisation: items recommended are too similar
3. Popularity/Trending I work in Computer Science, show me popular/trending articles in Computer Science + Perfect for cold users - Non personalised, discipline too broad
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Algorithms [4]
Research recommendations at Mendeley
4. Citation Network Articles similar to articles I cited
Articles that cite me
Articles from my co-author
+ Good for some kind of users
- Young researchers do not have (enough) publications
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Offline experiments
Research recommendations at Mendeley
Offline Evaluation of 100+ algorithms variations on an historical dataset
• Split data into training and testing based on timestamps: train until day X, try to predict what users will add in the next day/week/month
• Computed different metrics to measure different dimensions: • Accuracy (precision, recall, f-score, nDCG, MAP) • Diversity • Recency • Popularity • Consistency • Coverage
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Offline results
Research recommendations at Mendeley
Warm Users
Cold Users
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User Based CF Item Based CF Content Based
Citation Network Popularity Trending
Content Based Popularity Trending
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Business Logic / Analytics
Research recommendations at Mendeley
• Business put some constraints that could have an impact on the recommendation experience
– Don’t show articles outside the user discipline – Show articles only with a minimum readership – Show only recommendations that you can explain (especially for people recommendations, a different matter)
• Analytics – Dashboard on the recommender statistics:
• Number of recommendations served • Number of users with recommendations • …
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User Interface
Research recommendations at Mendeley
• Original idea: One list fits all
Create a single list with the best recommendations for the user: use advanced methods to take into account every signal and provide what is best for you!
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User Interface [2]
Research recommendations at Mendeley
• However… – Different kinds of users can have different information needs!
– The same user in different contexts can have different information needs!
VS
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User Interface [3]
Research recommendations at Mendeley
• Solution: different lists! • Provide multiple lists that satisfy different information needs • More likely for a user to find something he is interested in
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Online Survey
Research recommendations at Mendeley
Survey with Mendeley Advisors (pre-launch)
24/11/2015
Based on all the articles
Good
Bad
Popular
Good
Bad
Based on the last article
Good
Bad
Trending
Good
Bad
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Online Statistics
Research recommendations at Mendeley
Different statistics collected:
• overall and list
• click on title oradd to library • different metrics:
– # users – CR – CTR
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What’s next
Research recommendations at Mendeley
• New lists! – Based on your research interests – …
• Improve current lists
• Researchers you may want to follow
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Thank you!