Data Fusion for Dealing with the Recommendation Problem

Data Fusion for Dealing
with the Recommendation
Problem
Denis Parra, PUC Chile
Keynote for IFUP
Workshop on Multi-dimensional Information Fusion
for User Modeling and Personalization
UMAP 2016, Halifax, Canada

In this talk
• Recommendation of articles with user-controlled
fusion
• Fusing data in the music domain
• Fusion for e-marketplaces in virtual worlds
• How to integrate time into collaborative filtering?
7/17/16 D. Parra, IFUP keynote, UMAP 2016 2

Part 1: Recommendation of Articles
with User-Controlled Fusion

Recommendation of Articles
• Problem: a) Traditional user feedback is (was?) difficult
to obtain, b) Sparsity
• There are several potential sources of
recommendation, but mostly from the items:
• Content
• Co-citations, co-authorship
• Etc.
• Our approach: give users control over what to fuse.
• Would it work?
• How much data combination is the optimum?
• Does visual representation affect the behavior/accuracy?

References
• Verbert, K., Parra, D., Brusilovsky, P., & Duval, E. (2013).
Visualizing recommendations to support exploration,
transparency and controllability. In Proceedings of the 2013
international conference on Intelligent user interfaces (pp.
351-362). ACM.
• Parra, D., Brusilovsky, P., & Trattner, C. (2014). See what you
want to see: visual user-driven approach for hybrid
recommendation. In Proceedings of the 19th international
conference on Intelligent User Interfaces(pp. 235-240).
ACM.
• Verbert, K., Parra, D., & Brusilovksy, P. (2014). The effect of
different set-based visualizations on user exploration of
recommendations. In Proceedings of the Joint Workshop on
Interfaces and Human Decision Making in Recommender
Systems(pp. 37-44).

TalkExplorer
• Implemented initially for a user study in ACM
Hypertext 2012 for Conference Navigator.
• Main question to address: Do users consider the
fusion of several sources of data when choosing
relevant items?

Recap – Conference Navigator
Program Proceedings Author List Recommendations

TalkExplorer Interface

TalkExplorer - Entities
Entities
Tags, Recommender Agents,
Users

TalkExplorer – Central Canvas
Recommender
Recommender
Cluster with
intersection
of entities
Cluster (of talks)
associated to only
one entity
• Canvas Area: Intersections of Different Entities
User

TalkExplorer - Articles
Items
Talks explored by the
user

TalkExplorer Studies I & II
• Study I
• Controlled Experiment: Users were asked to discover
relevant talks by exploring the three types of entities:
tags, recommender agents and users.
• Conducted at Hypertext and UMAP 2012 (21 users)
• Subjects familiar with Visualizations and Recsys
• Study II
• Field Study: Users were left free to explore the
interface.
• Conducted at LAK 2012 and ECTEL 2013 (18 users)
• Subjects familiar with visualizations, but not much with
RecSys

Evaluation: Intersections & Effectiveness
• What do we call an “Intersection”?
• We used #explorations on intersections and their
effectiveness, defined as:

Results of Studies I & II
• Effectiveness increases
with intersections of more
entities
• Effectiveness wasn’t
affected in the field study
(study 2)
• … but exploration
distribution was affected

SetFusion
• Main motivation was investigating a simpler way to
visualize recommendations from several sources.
Would that improve “effectiveness” ?
• 3 studies were conducted
• Field study in CSCW 2013
• Controlled user with iConference series
• Field study in UMAP 2013

SetFusion

SetFusion I
Traditional
Ranked List
Paperssorted by
Relevance.
It combines3
recommendation
approaches.

SetFusion - II
Sliders
Allow the user to control the importance of
each data source or recommendation method
Interactive Venn Diagram
Allows the user to inspect and to filter papers
recommended. Actionsavailable:
- Filter item list by clicking on an area
- Highlight a paper by mouse-over on a circle
- Scroll to paper by clicking on a circle
- Indicate bookmarkedpapers

SetFusion Controlled Study
• 40 users, within-subjects study, simulated
iConference attendance

Controlled Study Main Results
• Controlling and fusing sources of relevancy
produces more bookmarks:
• 58.44% of bookmarks after using sliders
• 28.08% of bookmarks after using Venn diagram

Controlled Study Main Results
• Users prefer articles recommended by a fusion of
methods, in both conditions, but the effect is
stronger with the visualization

SetFusion – UMAP 2013
• Field Study: let users freely explore the interface
- ~50% (50 users) tried the
SetFusion recommender
- 28% (14 users) bookmarked at
least one paper
- Users explored in average 14.9
talks and bookmarked 7.36
talks in average.

TalkExplorer Vs. SetFusion
Clustermap Venn diagram

TalkExplorer vs. SetFusion
• Comparing distributions of explorations
In studies 1 and 2 over
TalkExplorer we observed an
important change in the
distribution of explorations.

TalkExplorer vs. SetFusion
• Comparing distributions of explorations
Comparing the field studies:
- In TalkExplorer, 84% of
the explorationsover
intersectionswere
performed over clusters of
1 item
- In SetFusion, was only
52%, compared to 48%
(18% + 30%) of multiple
intersections, diff. not
statistically significant

Take-aways
• We showed that intersections of several contexts of
relevance help to discover relevant items.
• The visual paradigm used can have a strong effect
on user behavior: we need to keep working on
visual representations that promote exploration
without increasing the cognitive load over the
users.

Part 2: Fusing Data in the Music
Domain

References
Parra-Santander, D., & Amatriain, X. (2011). Walk the
Talk: Analyzing the relation between implicit and
explicit feedback for preference elicitation.
Proceedings of UMAP 2011, Girona, Spain
Parra, D., Karatzoglou, A., Amatriain, X., & Yavuz, I.
(2011). Implicit feedback recommendation via
implicit-to-explicit ordinal logistic regression
mapping. Proceedings of the CARS Workshop, RecSys
Chicago, IL, USA, 2011.

Introduction (back in 2011)
• Most of recommender system approaches rely on
explicit information of the users, but…
• Explicit feedback: scarce (people are not especially
eager to rate or to provide personal info)
• Implicit feedback: Is less scarce, but (Hu et al., 2008)
There’s no negative feedback … and if you watch a TV program just
once or twice?
Noisy … but explicit feedback is also noisy
(Amatriain et al., 2009)
Preference & Confidence … we aim to map the I.F. to
preference (our main goal)
Lack of evaluation metrics … if we can map I.F. and E.F., we can
have a comparable evaluation

Introduction (Today)
• Is it possible to map implicit behavior to explicit
preference (ratings)? These data can eventually be
fused into a single compact model.
• OUR APPROACH: Study with Last.fm users
• Part I: Ask users to rate 100 albums (how to sample)
• Part II: Build a model to map collected implicit feedback
and context to explicit feedback

Walk the Talk (2011)
Albums they listened to during last:
7days, 3months, 6months, year,
overall For each album in the list we
obtained: # user plays (in each
period), # of global listeners and # of
global plays

Walk the Talk - 2
• Requirements: 18 y.o., scrobblings > 5000

Quantization of Data for Sampling
• What items should they rate? Item (album) sampling:
• Implicit Feedback (IF): playcountfor a user on a given album.
Changed to scale [1-3], 3 means being more listened to.
• Global Popularity (GP): global playcount for all users on a
given album [1-3]. Changed to scale [1-3], 3 means being
more listened to.
• Recentness(R) : time elapsed since user played a given
album. Changed to scale [1-3], 3 means being listened to
more recently.

Regression Analysis
• Including Recentness increases R2 in more than 10% [ 1 -> 2]
• Including GP increases R2, not much compared to RE + IF [ 1 -> 3]
• Not Including GP, but including interaction between IF and RE
improves the variance of the DV explained by the regression model. [
2 -> 4 ]
M1: implicit feedback
M2:
implicit
feedback &
recentness
M4:
Interaction of
implicit
feedback &
recentness
M3: implicit
feedback,
recentness,
global
popularity

Regression Analysis
• We tested conclusions of regression analysis by
predicting the score, checking RMSE in 10-fold
cross validation.
• Results of regression analysis are supported.
Model RMSE1 RMSE2
User average 1.5308 1.1051
M1: Implicit feedback 1.4206 1.0402
M2: Implicit feedback + recentness 1.4136 1.034
M3: Implicit feedback + recentness + global popularity 1.4130 1.0338
M4: Interaction of Implicit feedback * recentness 1.4127 1.0332

Part II: Extension of Walk the Talk
• Implicit Feedback Recommendation via Implicit-to-
Explicit OLR Mapping (Recsys 2011, CARS
Workshop)
• Consider ratings as ordinal variables
• Use mixed-models to account for non-independence of
observations
• Compare with state-of-the-art implicit feedback
algorithm

Recalling the 1st study (5/5)
• Prediction of rating by multiple Linear Regression
evaluated with RMSE.
• Results showed that Implicit feedback (play count
of the album by a specific user) and recentness
(how recently an album was listened to) were
important factors, global popularity had a weaker
effect.
• Results also showed that listening style (if user
preferred to listen to single tracks, CDs, or either)
was also an important factor, and not the other
ones.

... but
• Linear Regression didn’t account for the nested
nature of ratings
• And ratings were treated as continuous, when they
are actually ordinal.
User 1
1 3 5 3 0 4 5 2 2 1 5 4 3 2
User n
3 2 1 0 4 5 2 5 4 3 2 1 3 5
. . .

So, Ordinal Logistic Regression!
• Actually Mixed-Effects Ordinal Multinomial Logistic
Regression
• Mixed-effects: Nested nature of ratings
• We obtain a distribution over ratings (ordinal
multinomial) per each pair USER, ITEM -> we
predict the rating using the expected value.
• … And we can compare the inferred ratings with a
method that directly uses implicit information
(playcounts) to recommend ( by Hu, Koren et al.
2007)

Ordinal Regression for Mapping
• Model
• Predicted value

Datasets
• D1: users, albums, if, re, gp, ratings,
demographics/consumption
• D2: users, albums, if, re, gp, NO RATINGS.

Results

Conclusions (after 5 years)
• Fusion of Implicit feedback (scrobbles) and recency
can help to make more precise recommendations
• Models like the one by Gurbanov and Ricci presented
this year at UMAP offer a more compact way to work
with these data:
“Modeling and Predicting User Actionsin Recommender Systems”
by Tural Gurbanov, Francesco Ricci, Meinhard Ploner
• Evaluation is still a challenge!

Part 3: Data Fusion for Virtual Worlds

References
Lacic, E., Kowald, D., Eberhard, L., Trattner, C., Parra, D., &
Marinho, L. B. (2015). Utilizing online social network and
location-based data to recommend products and
categories in online marketplaces. In Mining, Modeling,
and Recommending 'Things' in Social Media (pp. 96-115).
Springer International Publishing.
Trattner, C., Parra, D., Eberhard, L., & Wen, X. (2014,
April). Who will trade with whom?: Predicting buyer-
seller interactions in online trading platforms through
social networks. In Proceedings of the 23rd International
Conference on World Wide Web (pp. 387-388). ACM.

Second Life
Social Network
Marketplace
Virtual World
Christoph Trattner
Know-Center
Graz, Austria

Dataset (Task: Item recommendation)

Recommendation Approaches
• User-based Collaborative Filtering, where
• Hybrid approaches (combine features)

Similarity Features - I

Similarity Features II

Hybrids

Different Task: Predict Buyer-Seller

Predict Buyer-Sellers: AUC Results

Summary
• These studies show that social network data is very
important for certain types of recommendations.
• Due to the lack of available cross-service data in the
real world, using data from Second Life has the
potential of a Proxy to build models for the real
world.

Part 4: Fusion of Time into CF

References
Larrain, S., Trattner, C., Parra, D., Graells-Garrido, E.,
& Nørvåg, K. (2015).
Good Times Bad Times: A Study on Recency Effects in
Collaborative Filtering for Social Tagging.
In Proceedings of the 9th ACM Conference on
Recommender Systems (pp. 269-272). ACM.

Time-Aware Collaborative Filtering
• Collaborative Filtering (User and Item-based)
considers all transactions equally important
• But transactions which happened too long ago
might be less important shaping the user model…
5
4
2
1
5
4
Active
user
User_1
User_2
2
3
4
Item 1
Item 2
consumed
2 years ago
consumed
1 month
ago

Two Concepts for Time-Aware CF
• Items consumed recently might be more important
than items consumed long time ago.
•Whenand how to incorporate time in user-
and item-based collaborative filtering?

When and How in UB-CF
Item 1 Item 2 … Item j Item m
User 1 1 5 2
User 2 5 1 4 2
…
User i 3 4
…
User n 2 5 5
Step 1: Find similar users. Weight transactions
based on recency difference

When and How in UB-CF
User 1 1 5 2
User 2 5 1 4 3
…
User i 3 4
…
User n 2 5 4
Step 2: Similar users found. Recommend items
with high ratings and consumed recently.

When and How in IB-CF
User 1 1 5 2
User 2 5 1 4 2
…
User i 3 4
…
User n 2 5 5
Step 1: Find similar items sim(items(user i)).
Weight items based on recency. Consu-
med 1
week
ago
Consu-
med 1
year
ago

When and How in IB-CF
User 1 1 5 2
User 2 5 1 4 2
…
User i 3 4
…
User n 2 5 5
Step 2: Find similar items Item 1. Weight items
based on recency difference.

Decay functions
• Exponential
• Power
• Linear
• Logistic
• BLL

Parameters and fitting
Days from bookmark
Median = 50 days

Evaluation: Datasets

Evaluation: Results I

Evaluation: Results II

Summary
• Best results: Post-filtering combined with power
decay gives the best
• Pre- and Post-filtering produce a strong effect, but
UB-CF is more susceptible than IB-CF to the effect
of filtering specially pre-filtering.
• The hybridization of UB and IB improves makes the
recommendation more robust.
• Future work: fit parameters on a user basis rather
than dataset basis.

Wrapping up

• Visual approaches for user-controllable data fusion can
work, but there’s room to find effective visual-
interactive combinations.
• In the music domain and other domains, time and
recency can work very well for recommendation.
• …but using time requires an adequate modeling of the
decay functions.
• Information from Virtual worlds could may be used as
proxy to build models and use them for transfer
learning.

Promising works in this UMAP 2016
• Using Semantic Information: Extend the work of
Musto et al. (UMAP 2016) to support better models
and more explainable models.
• Combine taxonomies with implicit/explicit feedback
using compact graphical models (co-authored by g.
Guo)
• Extend models with time and other sources of
feedback (Turgnov et al.)

Ideas for Data Fusion
• Combine multimodal information within the same
embedding using deep learning has given great
results in visual processing + NLP fields:
• Visual Q&A
• Automatic Captioning of Pictures
Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Lawrence Zitnick, C., & Parikh,
D. (2015). Vqa: Visual question answering. In Proceedings of the IEEE International
Conference on Computer Vision (pp. 2425-2433).

Thanks!
dparras@uc.cl
http://dparra.sitios.ing.uc.cl/

Data Fusion for Dealing with the Recommendation Problem

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