Diversity in a recommendation list has been recognized as one of the key factors to increase user’s satisfaction when interacting with a recommender system. Analogously to the modelling and exploitation of query intent in Information Retrieval adopted to improve diversity in search results, in this paper we focus on eliciting and using the profile of a user which is in turn exploited to represent her intents. The model is based on regression trees and is used to improve personalized diversification of the recommendation list in a multi-attribute setting. We tested the proposed approach and showed its effectiveness in two different domains, i.e. books and movies.

1. RecSys 2015 – 2nd Workshop on New Trends in Content-Based Recommender Systems
September 16 - 20, 2015 in Vienna, Austria
Exploiting Regression Trees as User Models for
Intent-Aware Multi-attribute Diversity
Paolo Tomeo, Tommaso Di Noia, Marco de Gemmis, Pasquale Lops,
Giovanni Semeraro, Eugenio Di Sciascio
{paolo.tomeo, tommaso.dinoia, eugenio.disciascio}@poliba.it
{marco.degemmis, pasquale.lops, giovanni.semeraro}@uniba.it
Polytechnic University of Bari - Bari (ITALY) University of Bari Aldo Moro - Bari (ITALY)

2. Exploiting Regression Trees as User Models for Intent-Aware Multi-attribute Diversity
P. Tomeo, T. Di Noia, M. de Gemmis, P. Lops, G. Semeraro, E. Di Sciascio
Introduction
Problem
How to diversify with different items attributes
complying with the users interests?
Proposal
Regression trees to represent user interests as a
combination of characteristics
Evaluation in terms of individual and aggregate diversity

3. Exploiting Regression Trees as User Models for Intent-Aware Multi-attribute Diversity
P. Tomeo, T. Di Noia, M. de Gemmis, P. Lops, G. Semeraro, E. Di Sciascio
Individual Diversity
Same Artist – Low Diversity Diverse Artists

4. Exploiting Regression Trees as User Models for Intent-Aware Multi-attribute Diversity
P. Tomeo, T. Di Noia, M. de Gemmis, P. Lops, G. Semeraro, E. Di Sciascio
Diversity and Satisfaction
Recommendations quality requires diversity even at the cost of
reducing accuracy
[Ziegler at al. WWW ‘05, McNee et al. CHI ‘06, Zhou at al. PNAS ‘10, Adamopoulos and
Tuzhilin RecSys ‘11, Hurley and Zhang TOIT ‘11, Vargas at al. RecSys ‘14, …]
Diversity has a significant positive influence on the user
satisfaction (study with 500+ users of Movielens)
[Ekstrand et al. “User Perception of Differences in Recommender Algorithms”, RecSys ‘14]

5. Exploiting Regression Trees as User Models for Intent-Aware Multi-attribute Diversity
P. Tomeo, T. Di Noia, M. de Gemmis, P. Lops, G. Semeraro, E. Di Sciascio
How to Diversify
Data
Top-M
List
Re-Ranked
Top-N
List
Recommender
System
Diversification
Algorithm
Items description
User profile
M >> N

6. Exploiting Regression Trees as User Models for Intent-Aware Multi-attribute Diversity
P. Tomeo, T. Di Noia, M. de Gemmis, P. Lops, G. Semeraro, E. Di Sciascio
Diversification Algorithms
Several algorithms presented so far:
- MMR (Carbonell and J. Goldstein, SIGIR ‘98)
- IA-Select (Agrawal et al., WSDM ‘09)
- xQuAD (Santos et. al, WWW ’10)
- …
- BinomDiv (Vargas et. al RecSys ‘14)
Need of information about items
- content
- external information
- statistical information
- …
Should take user interests into consideration (intent-aware)
- user model

7. Exploiting Regression Trees as User Models for Intent-Aware Multi-attribute Diversity
P. Tomeo, T. Di Noia, M. de Gemmis, P. Lops, G. Semeraro, E. Di Sciascio
Greedy strategy

8. Exploiting Regression Trees as User Models for Intent-Aware Multi-attribute Diversity
P. Tomeo, T. Di Noia, M. de Gemmis, P. Lops, G. Semeraro, E. Di Sciascio
xQuAD
eXplicit Query Aspect Diversification
likelihood of item i
being chosen given
the feature f
the user u interest
in the feature f

9. Exploiting Regression Trees as User Models for Intent-Aware Multi-attribute Diversity
P. Tomeo, T. Di Noia, M. de Gemmis, P. Lops, G. Semeraro, E. Di Sciascio
xQuAD
eXplicit Query Aspect Diversification
Penalization of
redundancy

10. Exploiting Regression Trees as User Models for Intent-Aware Multi-attribute Diversity
P. Tomeo, T. Di Noia, M. de Gemmis, P. Lops, G. Semeraro, E. Di Sciascio
Multi-Attribute Diversity
Attribute Features
Director Cameron Crowe
Year of release 2001
Actor Tom Cruise, Cameron Diaz,
Penélope Cruz
Genre Fantasy, Mystery, Romance
Attribute Features
Director Cameron Crowe
Year of release 1996
Actor Tom Cruise, Cuba Gooding Jr.,
Renée Zellweger
Genre Comedy, Drama, Romance

11. Exploiting Regression Trees as User Models for Intent-Aware Multi-attribute Diversity
P. Tomeo, T. Di Noia, M. de Gemmis, P. Lops, G. Semeraro, E. Di Sciascio
Regression Trees as User Models
A regression tree allows to represent user tastes as a
combination of interrelated characteristics
Romance Movie
falsetrue
AlPacino acts in
falsetrue
Direct by Crowe
falsetrue
interest = 5 interest = 5 interest = 3
Year < 2000
falsetrue
interest = 5 interest = 2.3

12. Exploiting Regression Trees as User Models for Intent-Aware Multi-attribute Diversity
P. Tomeo, T. Di Noia, M. de Gemmis, P. Lops, G. Semeraro, E. Di Sciascio
Rules from Regression Trees
M5Rules produces rules from regression trees.
Good compromise between rules accuracy and compactness
http://weka.sourceforge.net/doc.dev/weka/classifiers/rules/M5Rules.html

13. Exploiting Regression Trees as User Models for Intent-Aware Multi-attribute Diversity
P. Tomeo, T. Di Noia, M. de Gemmis, P. Lops, G. Semeraro, E. Di Sciascio
Rules in Diversification
Data
Recommender
System
Diversification
Algorithm
Items description
User profile
Rule1
Rule2
.
.
.
Rulen
Rule1 , Rule3
Rule1
Rule1
Rule1 , Rule3
Rule1 , Rule2
Rule1
Rule5 , Rule6
Rule2
Rule3 , Rule4
Rule5
Rule2 , Rule3
Rule1 , Rule7
Rule1

14. Exploiting Regression Trees as User Models for Intent-Aware Multi-attribute Diversity
P. Tomeo, T. Di Noia, M. de Gemmis, P. Lops, G. Semeraro, E. Di Sciascio
xQuAD adapted for multi-attribute
Original
Multi-attribute
domain of attribute A

15. Exploiting Regression Trees as User Models for Intent-Aware Multi-attribute Diversity
P. Tomeo, T. Di Noia, M. de Gemmis, P. Lops, G. Semeraro, E. Di Sciascio
xQuAD adapted for Rules
Original
For rules
rules of user u
matched by item i
importance of rule
m for user u
importance of the
rule m for item i

16. Exploiting Regression Trees as User Models for Intent-Aware Multi-attribute Diversity
P. Tomeo, T. Di Noia, M. de Gemmis, P. Lops, G. Semeraro, E. Di Sciascio
xQuAD adapted for Rules
Original
For rules
RT
binary function
DivRT
avg similarity between m
and each rule covered by item j

17. Exploiting Regression Trees as User Models for Intent-Aware Multi-attribute Diversity
P. Tomeo, T. Di Noia, M. de Gemmis, P. Lops, G. Semeraro, E. Di Sciascio
Evaluation

18. Exploiting Regression Trees as User Models for Intent-Aware Multi-attribute Diversity
P. Tomeo, T. Di Noia, M. de Gemmis, P. Lops, G. Semeraro, E. Di Sciascio
Datasets
Mappings
http://sisinflab.poliba.it/semanticweb/lod/recsys/datasets/
• Movielens 1M mapped with Dbpedia
-attributes: Genre, Decade of Release, Actors, Directors
-998,963 ratings from 6,040 users on 3,625 movies
-sparsity 95.7%
-split 60-40%
• LibraryThing mapped with Freebase
-attributes: Genre, Author, Subject
-565,310 ratings from 7,278 users on 27,358 books
-sparsity 99.7%
-split 80-20%

19. Exploiting Regression Trees as User Models for Intent-Aware Multi-attribute Diversity
P. Tomeo, T. Di Noia, M. de Gemmis, P. Lops, G. Semeraro, E. Di Sciascio
Evaluation Metrics
Accuracy Precision
Recall
nDCG
Individual Diversity Intra-List Diversity (ILD)
redundancy-aware nDCG (α-nDCG)
Aggregate Diversity Catalog coverage
Entropy

20. Exploiting Regression Trees as User Models for Intent-Aware Multi-attribute Diversity
P. Tomeo, T. Di Noia, M. de Gemmis, P. Lops, G. Semeraro, E. Di Sciascio
Evaluation Setting
Two steps
1. Top-200 recommendations computed with BPRMF
(MyMediaLite)
2. Top-10 diverse recommendations
• repeated varying the value of λ from 0 to 0.95 (step 0.05)

21. Exploiting Regression Trees as User Models for Intent-Aware Multi-attribute Diversity
P. Tomeo, T. Di Noia, M. de Gemmis, P. Lops, G. Semeraro, E. Di Sciascio
Compared Algorithms
Baseline
- xQuAD for multi-attribute
Proposed
- RT: xQuAD for Rules
- DivRT: xQuAD for Rules with a diversity analysis between rules
Combination
- xQuAD-after-RT: xQuAD on top-50 from RT
- RT-after-xQuAD: RT on top-50 from xQuAD

22. Exploiting Regression Trees as User Models for Intent-Aware Multi-attribute Diversity
P. Tomeo, T. Di Noia, M. de Gemmis, P. Lops, G. Semeraro, E. Di Sciascio
Individual diversity
Movielens LibraryThing
ILD
α-nDCG

23. Exploiting Regression Trees as User Models for Intent-Aware Multi-attribute Diversity
P. Tomeo, T. Di Noia, M. de Gemmis, P. Lops, G. Semeraro, E. Di Sciascio
Aggregate diversity
Movielens LibraryThing
Coverage
Entropy

24. Exploiting Regression Trees as User Models for Intent-Aware Multi-attribute Diversity
P. Tomeo, T. Di Noia, M. de Gemmis, P. Lops, G. Semeraro, E. Di Sciascio
Outcome
Rule-based approach
- less individual diversity
+ better aggregate diversity
Combination of baseline and proposed approach
+ good compromise among accuracy, individual and
aggregate diversity

25. Exploiting Regression Trees as User Models for Intent-Aware Multi-attribute Diversity
P. Tomeo, T. Di Noia, M. de Gemmis, P. Lops, G. Semeraro, E. Di Sciascio
Conclusion
We proposed a method to leverage regression trees as user model
technique for intent-aware multi-attribute diversity problem
We showed that combining attribute-based and rules-based re-rankings
obtains the advantages of both
Future work
- propose a method for combining attributes and rules within the same
formula
- evaluation of impact of our approach on recommendation novelty
- evaluation with user study

26. Exploiting Regression Trees as User Models for Intent-Aware Multi-attribute Diversity
P. Tomeo, T. Di Noia, M. de Gemmis, P. Lops, G. Semeraro, E. Di Sciascio
Thanks for your
attention!
Q & A