The document summarizes the plista Dataset, which contains news article recommendation data from 13 publishers. It includes contextual transactional data like impressions and clicks from over 50 attributes, as well as content features of the news articles. The dataset aims to support research on recommender systems for news articles, providing features not available in other datasets like cross-domain applications and dynamics of new content creation. It can be used to evaluate recommender algorithms and compare performance across different news publishers.

1. The plista Dataset
ACM RecSys 2013, Hong Kong
Authors:
Kille, Benjamin
and Hopfgartner, Frank
and Brodt, Torben
and Heintz, Tobias
Speaker:
Brodt, Torben
International News Recommender
Systems Workshop and Challenge
October 13th, 2013

2. Introduction and Motivation
● Context: News Article Recommendation

3. Introduction and Motivation
● Do we need another recommendation data
set?
we have
...
● What features are those data sets missing?
● What requirements entail news articles for
recommendation?

4. Introduction and Motivation
● Features that had not been available in
existing data sets:
○ contextual features: device, operating system,
browser, etc.
○ cross-domain features: 13 different news providers
included
○ different interaction types: interactions with
recommendations (clicks), as well as news items
(impressions)
○ content features: headline, URL, images, text
snippets, etc.

5. Introduction and Motivation
● Additional requirements for recommending news articles
○ real-time → recommendations must be provided within a
short time interval (< 200ms)
○ changing relevancy → items’ relevancy decreases with
time
○ dynamics → new news items are being continuously
added
● Requirements inherent to existing recommender systems:
○ sparsity → users typically read only few news articles
○ cold start → systems refrain from requesting users to
create profiles; this results in a majority of small user
profiles

6. Dataset characteristics
{ // json
"type": "impression",
"context": {
"simple": {
"27": 418, // publisher
"14": 31721, // widget
...
},
"lists": {
"10": [100, 101] // channel
}
... specs hosted at http://orp.plista.
api
} com

7. Dataset characteristics
● object types
○
○
○
○
impressions → users reading news articles
clicks → users clicking recommendations
creates → news articles being created
updates → news articles being updated
api specs hosted at http://orp.plista.
com

8. Dataset usage

9. Dataset usage
● Evaluation based on
Click-Through-Rate
(CTR)
● ~ 84 million
impressions
● ~ 1 million clicks

10. Dataset usage
● evaluation cross-news portal
recommenders
● 10 - 36 % user overlap in
between different news
portals

11. Dataset usage
● news portal comparisons
● do we observe similar user
behaviour on news portals
offering similar content?

12. Dataset usage
● evaluating contextual
recommendation algorithms
● sensitive to
○ weekday
○ hour of day
○ ...

13. Dataset usage
When using the data set you may consider…
● … we identify users by session IDs
○
○
individual users may have several IDs
users sharing their device might be mapped to one ID
● … interactions (clicks, impressions) and content
dynamics (creates, updates) differ between news
portals
● … contents are restricted to German
● … preferences are represented on a binary scale (user
read article, user clicked recommendation)
● … clicking on recommendations might not reveal the
actual relevancy of an item

14. Conclusions
● we introduce a new data set intended to
support recommender systems research
● we outlined novel features which existing
data sets lacked
● we presented scenarios which can be
evaluated using the data set
● we pointed to critical aspects which ought
to be considered when working with the data
set

15. Summary
● news articles
○ of ~13 publishers
● transactional data
○ Impressions
○ Clicks
● contextual data
○ of ~50 attributes
● cross domain application

16. The plista Dataset
@inproceedings{Kille:2013,
title = {The plista Dataset},
author = {
Kille, Benjamin
and Hopfgartner, Frank
and Brodt, Torben
and Heintz, Tobias
},
booktitle = {
NRS'13: Proceedings of
the International Workshop and
Challenge on News Recommender Systems
},
year = {2013},
month = {10},
location = {Hong Kong, China},
publisher = {ACM},
pages={14--21}
}