Slides of my presentation at BIR 2019 The MOVING platform includes a huge amount of heterogeneous educational resources, such as documents, videos, and social media posts. We show how the MOVING recommender system can support users in dealing with such a massive information flow by leveraging semantic profiling. The HCF-IDF model exploits a thesaurus or ontology to represents users and documents and it is used to recommend educational resources based on users' search history. We describe how the recommender is implemented how it is applied to the MOVING platform to deal with the huge amount of resources stored in the platform, their variety and the increasing number of users.

1. Recommending Multimedia
Educational Resources on the
MOVING Platform
Iacopo Vagliano
Sibgha Nazir
BIR 2019 – Cologne, April, 14th

2. Research problem
• Which document to read?
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3. • Training information professional
• Improve information literacy
• Better exploit data and text mining
• 30 Million documents of various kinds
The MOVING platform
WevQueryAdvanced
visualizations
Crawlers
Data
processing Index
User
data
Search
Engine
Recommender
system
ATS
3

4. HCF-IDF Semantic Profiling
• Match documents to a user profile
• Exploit a thesaurus
User profile= “Open innovation”
Title = “Is Open Innovation
Reinventing Innovation
Policy for catching up Economies?”
Open Innovation
Innovation
Management
Management
Title = “Risk and
uncertainty in
innovation
management”
4
Nishioka C., Scherp A., Profiling vs. Time vs. Content: What does
Matter for Top-k Publication Recommendation based on Twitter
Profiles? JCDL 2016

5. User profile
• Based on search history
• Pairs term-weight
• Weights depending on frequency and
recentness of a term‘s search
• 𝑤 = 𝛼 𝑡
𝑡
𝑇
+ 𝛼ℎ
ℎ
𝐻
• t timestamp, T current time
• h number of searches for a term, H total
number of searches for the user
5

6. User interface
6

7. User interface
7

8. Implementation
WevQuery
Recommender
Service
Search engine
(Elasticsearch)
Retrieve users’
search history
Retrieve documents
based on users’ profiles
Build users’ profiles
based on their search
history
Index
HTTP/JSON
HTTP/JSON
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9. Conclusions
• Semantic document and user profiling with
HCF-IDF
• Heterogeneous resources (documents, videos,
social media posts)
• Future work:
– Additional user interactions (clicks, mouse-hovers)
– Online evaluation
• Try it at platform.moving-project.eu
9MOVING is funded by the EU Horizon 2020 Programme under the project number INSO-4-2015: 693092
i.vagliano@zbw.eu @maponaso