This document describes a recommender system developed for the CSIRO Data Access Portal to help users discover research data. It examines two types of recommender systems: content-based, which uses item properties, and collaborative filtering, which uses user similarities. The system was built using both explicit metadata and implicit user behavior data. It calculates similarity between datasets across various features using techniques like TF-IDF and develops recommendations. An evaluation found the top recommendations were relevant 98% of the time. Future work includes enhancing the model and further evaluation.

1. Data You May Like: A Recommender System for Research
Data Discovery
1MINERAL RESOURCES, 2INFORMATION MANAGEMENT & TECHNOLOGY
Anusuriya Devaraju1, Rob Davy2 and Dominic Hogan2
IN21D: New Approaches to Data Discovery Across Geoscience Domains I AGU 2016, 13th December 2016.
image: orbital-recruitment.co.uk

2. Introducing Recommender Systems
We can classify
recommender systems into
two broad groups:
• Content-based filtering
systems examine properties of
the items recommended.
• Collaborative filtering systems
recommend items based on
similarity measures between
users or item co-occurrences.
2 | Data You May Like: A Recommender System for Research Data Discovery | Anusuriya Devaraju et al.

3. Introducing Case Study
• Total collection (30.11.2016) : 1853
• (1802 public, 51 private collections)
• Domains
• Agriculture & food
• Astronomy & space science
• Data61
• Energy
• Food & nutrition
• Health & biosecurity
• Land & water
• Manufacturing
• Mineral resources
• Oceans & atmosphere
3 | Data You May Like: A Recommender System for Research Data Discovery | Anusuriya Devaraju et al.
CSIRO Data Access Portal (DAP)

4. Motivations
• Direct search
• data search is limited in terms
of title, keyword and
descriptions.
• Faceted browsing
• exhaustive filters and time
consuming.
4 | Data You May Like: A Recommender System for Research Data Discovery | Anusuriya Devaraju et al.

5. A Recommender System for Research Data
• Search and recommendations are complementary.
• Enhances data visibility, especially for users unfamiliar with the datasets.
5 | Data You May Like: A Recommender System for Research Data Discovery | Anusuriya Devaraju et al.
interested [ view, download ]
similar
Dataset A
likely interested
Similar Datasets
Data User
Similar datasets may be
determined based on :
• Explicit information, e.g.,
metadata of datasets
• Implicit information, e.g.,
data consumption details
inferred from logs.

6. Data Sources
6 | Data You May Like: A Recommender System for Research Data Discovery | Anusuriya Devaraju et al.
DAP Web Service: https://ws.data.csiro.au/
DAP Google Analytics Reporting API
and server log files
Explicit Information
Implicit Information

7. Data Similarity Model
The overall similarity between two datasets (Di, Dj) is :
7 | Data You May Like: A Recommender System for Research Data Discovery | Anusuriya Devaraju et al.
• Di, Dj : Datasets
• S : Overall similarity
• ω1… ωn : Feature weights
• Sfn(Di,Dj) : Similarity between Di and Dj datasets based on feature class n
(normalized value between [0, 1])
S(Di, Dj) = ω1Sf1(Di,Dj) + ω2Sf2(Di,Dj) + ω3Sf3(Di,Dj) + … + ωnSfn(Di,Dj)

8. Best Choice of Weights?
• Survey Period : 08.06.2016 –
23.08.2016
• Respondents : Data owners and
consumers
• Number of respondents : 151
8 | Data You May Like: A Recommender System for Research Data Discovery | Anusuriya Devaraju et al.

9. Feature Extraction
9 | Data You May Like: A Recommender System for Research Data Discovery | Anusuriya Devaraju et al.
Feature Class Feature extraction Similarity Measures
Title TF-IDF score Cosine similarity
Description TF-IDF score Cosine similarity
Keyword TF-IDF score Cosine similarity
Activity TF-IDF score Cosine similarity
Fields of
Research
Presence/absence of research fields Jaccard coefficient
Lead Researcher Presence/absence of lead researchers Jaccard coefficient
Contributor Presence/absence of contributors Jaccard coefficient
Search behaviour Common query term Cosine similarity
Download Datasets downloaded together Cosine similarity

10. Examples : Infer Related Datasets
10 | Data You May Like: A Recommender System for Research Data Discovery | Anusuriya Devaraju et al.
rock
Search results
• Dataset 1
• Dataset 2
• Dataset 3
• Dataset 4
• …..
[search term]
Common Search Term Daily Data Download

11. Example
11 | Data You May Like: A Recommender System for Research Data Discovery | Anusuriya Devaraju et al.

12. System Architecture
12 | Data You May Like: A Recommender System for Research Data Discovery | Anusuriya Devaraju et al.
CSIRO Data Access Portal (DAP)
You may
also like
……
HDF
RecommenderService
SQL
database
Research Data
Recommender Engine
CSIRO-DAP
Web Service
Analytics Reporting API
DAP Server Logs

13. Examples of Web Service Requests
• Obtaining the similarity result is via GET request:
http://{server-name}/simhdf?collection=DAP&nn=5&uw=0&target=csiro:6110
• Get the features and weights associated with a collection
http://{server-name]/features?collection=DAP
13 | Data You May Like: A Recommender System for Research Data Discovery | Anusuriya Devaraju et al.

14. Offline Evaluation
• Respondents: Lead researchers
• Number of datasets evaluated : 52
• Evaluation period : 10.11-30.11.2016
• Binary relevance tests
a. Top-ranked datasets : 51/52 datasets (98%)
are rated as relevant.
– 1 dataset was ‘undecided’
b. Next-ranked datasets (not created by
evaluator) : 46/52 datasets (89%) rated as
relevant
- 6 datasets are rated as ‘less relevant’
14 | Data You May Like: A Recommender System for Research Data Discovery | Anusuriya Devaraju et al.
Business Unit Number of
Evaluators
Agriculture 11
ICT 11
Energy 3
Land & Water 14
Manufacturing 3
Mineral Resources 10

15. What’s Next?
• Enhance the model with addition of new features – spatial and temporal
information.
• More evaluation! number of evaluators, compare ranked lists, 10-fold
cross validation.
• Apply the recommender model to infer similar research datasets from
other repositories, e.g., data.gov.au, TERN, etc.
15 | Data You May Like: A Recommender System for Research Data Discovery | Anusuriya Devaraju et al.

16. Mineral Resources
Anusuriya Devaraju
Postdoctoral Fellow
e anusuriya.devaraju@csiro.au
IMT
Robert Davy
Research Engineer
e Robert.Davy@csiro.au
IMT
Dominic Hogan
Data Librarian
e dominic.hogan@csiro.au
MINERAL RESOURCES
Acknowledgement:
• CSIRO eResearch Collaboration Project (ERRFP-368).
• CSIRO IMT Data Management Capability
Enhancement Program (DMCEP).