Master thesis presentation

1. Content Based Recommendations
Enhanced with Collaborative Information
POLITECNICO DI MILANO
Scuola di Ingegneria Industriale e dell'Informazione
Corso di Laurea Magistrale in
Ingegneria Informatica
Anno Accademico 2014 – 2015
Candidato: Alessandro Liparoti (819828)
Relatore: Prof. Paolo Cremonesi

2. RECOMMENDER SYSTEMS
software tools which analyze different source of data in order to
predict the rating that a user would give to an item
Main Families:
• Collaborative Filtering
• Content-based Filtering
• Hybrid algorithms
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3. COLLABORATIVE FILTERING
Collaborative Filtering
assumption: users who agreed in the past will also agree in the
future
analyze past users’ ratings to compute predictions
User-Rating-Matrix (URM)
𝑟𝑢,𝑖 is the rating given
by user u to item i
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✔ good performances
✘ not applicable if no enough ratings
for both users and items
(cold-start problems)

4. CONTENT-BASED FILTERING
Content-Based Filtering
assumption: users will like items similar to those they liked in the
past
compute items similarities’ scores considering item features
Item-Content-Matrix (ICM)
𝑓𝑖,𝑘 = 1 if item i has
the feature k
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✔ no need of items’ ratings
(i.e. works in a new-item scenario)
✘ ignoring users’relations
leads to worse performances

5. HYBRID ALGORITHMS
• new-item recommendations
- Factorization Machines (FM)
- generic factorization model
- can represent different types of models
- UFSM
- computes item similarities as a CB approach
- uses collaborative data to personalize them for
each user
• no new-item recommendations
- SSLIM
- learns a matrix of item-item coefficients
- improves SLIM adding side information
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6. GOAL OF THE THESIS
usual hybridization use item content data to improve
collaborative models
our hybridization build a content-based model enhanced
with collaborative data
CONTENT-BASED COLLABORATIVE
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✔exploits collaborative data (also in a new-item scenario)
✔uses weigths for features and user-feature relations

7. CONTENT BASED COLLABORATIVE
content-based similarity function
CBC similarity function
• bk control the importance of feature k
(e.g. usually genre > year of production for movies)
• cu,k control the importance of the relation between user u and
feature k
(e.g. a user likes a particular actor)
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8. CBC VERSIONS
rating prediction
CBCrmse
partial effects:
analytical sequential approach
item recommendation
CBCbpr
stochastic gradient descent
approach
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CBC parameters θ are learned minimizing an error function

9. DATASET
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HetRec2011-Movielens
RecSys-Polimi IMDB

10. SIMULATIONS
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URM was split in three parts
two types of simulation
collaborative train on A+B test on C
new-item train on A test on C

11. TESTING
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- rating prediction metrics
RMSE
RMSEp (only on positive ratings)
- item recommendation metrics
precision
recall
mean average precision (MAP)
mean reciprocal rank (MRR)
normalized discounted cumulative gain (NDCG)

12. ALGORITHMS OF COMPARISON
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• Collaborative - Matrix Factorization (MF)
Asymmetric-SVD
Bayesian Personalized Ranking MF
• Hybrid
Factorization Machines
• Content-Based
item k-nearest-neighbor

13. RESULTS
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collaborative simulations

14. RESULTS – ITEM RECOMMENDATION
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new-item simulations

15. THINGS TO DO
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use weights to control the importance of relations
among items’ features
(e.g. two actors appearing together in many movies)
use a CBC-like similarity function to compare
users instead of items
(e.g. gender, age, demographic information,…)

16. THE END
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THANK YOU