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A brief presentation of a new research area of Recommender Systems : Multi-Criteria Recommender Systems.
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Diversity in Recommender SystemHow to extend SINGLE-CRITERIA Recommender Systems ?
Author :DAVIDE GIANNICO
Specialists for managing information systems based on the semantic manipulation of information -University of Bari
Multi-Criteria Recommender Systems
Outline
• Introduction to RECOMMENDER SYSTEMS• Introduction to MULTI-CRITERIA RECOMMENDER SYSTEMS (MCRS)•MCRS : TYPOLOGIES & Some recent works•OPEN ISSUES AND CHALLENGES
Specialists for managing information systems based on the semantic manipulation of information -University of Bari
Multi-Criteria Recommender Systems - Specialists for managing information systems based on the semantic manipulation of information - University of Bari
Information Overload
How much Information?
Multi-Criteria Recommender Systems - Specialists for managing information systems based on the semantic manipulation of information - University of Bari
RECOMMENDER SYSTEMS are a SOLUTION to the Information Overload…
We need a INTELLIGENT Information AccessWe need a way to FILTER the information
Multi-Criteria Recommender Systems - Specialists for managing information systems based on the semantic manipulation of information - University of Bari
Which RECOMMENDATION TECHNIQUES do we have ? (1/2)
COLLABORATIVE FILTERING
CONTENT-BASED
Multi-Criteria Recommender Systems - Specialists for managing information systems based on the semantic manipulation of information - University of Bari
HYBRID
KNOWLEDGE-BASED
Which RECOMMENDATION TECHNIQUES do we have ? (2/2)
Knowledge
A
B
CRecommend
Model
Multi-Criteria Recommender Systems - Specialists for managing information systems based on the semantic manipulation of information - University of Bari
Are the CLASSICAL RECOMMENDATIONtechniques PERFECT?!
Single-criteria movie RS Multi-criteria movie RS
7 8
7 8
Story : 5Actors : 9
Story : 9Actors : 7
Story : 8Actors : 6
Story : 7Actors : 9
(a typical example)
Multi-Criteria Recommender Systems - Specialists for managing information systems based on the semantic manipulation of information - University of Bari
A
B
A
B
RECOMMENDATION as a MULTI-CRITERIA
DECISION MAKING PROBLEM
Bernard Roy’s (pioneer in MCDM) METHODOLOGY:
1. Define the object of decision
2. Defining a consistent family of criteria
3. Developing a global preference model
4. Selection of the decision support process
Multi-Criteria Recommender Systems - Specialists for managing information systems based on the semantic manipulation of information - University of Bari
CLASSIFICATION of MCRS*
MCRS
DecisionProblematic
Types of criteria
Global preferencemodel approach
* According to the MCDM framework
Multi-Criteria Recommender Systems - Specialists for managing information systems based on the semantic manipulation of information - University of Bari
Chooice
Ranking
Sorting
Description
Measurable
Ordinal
Probabilistic
Fuzzy
Value Focused Model
Multi Objective Optimization Model
Outranking relation model
Preference disaggregation model
* According to raccomandation Approach
CLASSIFICATION of MCRS*
MCRS
Multi-attribute contentpreference modeling
Multi-attribute contentsearch and filtering
Multi-criteria rating-basedpreference elicitation
Multi-Criteria Recommender Systems - Specialists for managing information systems based on the semantic manipulation of information - University of Bari
MULTI CRITERIA RATING–BASED PREFERENCE ELICITATION
WHERE could we USE that information?
5
5
6
7
7
6
5
6
7
7
6
9
5
??? ?7 7
Star Wars Fargo Toy Story Saw
• PREDICTION PHASE
• RECOMMENDATION PHASE
6
65 9
95
5 7 ? 7 ? 7 ? 7 ?
5 7 5 7 9 5 6 9 5
6 6 6 6 5 6 5 9 6
? ? ? ?
Multi-Criteria Recommender Systems - Specialists for managing information systems based on the semantic manipulation of information - University of Bari
MULTI-RATING RS – an EXAMPLE
Single-criteria movie Recommender Systems
Multi-criteria movie Recommender Systems
5,2,2,8,8 7,5,5,9,9 5,2,2,8,8 7,5,5,9,9
5,8,8,2,2 7,9,9,5,5 5,8,8,2,2 7,8,8,2,2
6,3,3,9,9 6,4,4,8,8 6,3,3,9,9 6,4,4,8,8
? Reting to bepredicting
Reting to beusing in prediction
Reting to bepredicting
Reting to beusing in prediction
5 7 5 7 ?
5 7 5 7 9
6 6 6 6 5
?
9
5,2,2,8,8 7,5,5,9,9 5,2,2,8,8 7,5,5,9,9 ?,?,?,?,?
5,8,8,2,2 7,9,9,5,5 5,8,8,2,2 7,8,8,2,2 9,8,8,10,10
6,3,3,9,9 6,4,4,8,8 6,3,3,9,9 6,4,4,8,8 5,2,2,8,8
?,?,?,?,?
5,2,2,8,8
Multi-Criteria Recommender Systems - Specialists for managing information systems based on the semantic manipulation of information - University of Bari
A
B
C
A
B
C
Prediction -phase: HEURISTIC-BASED(1/3)
• NEIGHBORHOOD-BASED collaborative filtering recommendation (context)
Similarity computation method in single-rating : correlation-base & cosine-based
Person correlation-based Cosine-based
HOW TO EXTEND THIS TO MULTI-CRITERIA?
Multi-Criteria Recommender Systems - Specialists for managing information systems based on the semantic manipulation of information - University of Bari
Prediction-phase : HEURISTIC-BASED(2/3)
Two approaches :
1. Aggregation of traditional similarities that are based on each individual criteria
a. Calculate similarity between two users separately on each indidualcriterion;
b. Final similarity between two users is obtained by aggregatingindividual similarity values. How?
I.
II.
(Adomavicius)
(Adomavicius)
III. (Tang an McCalla)
Multi-Criteria Recommender Systems - Specialists for managing information systems based on the semantic manipulation of information - University of Bari
Two approaches :
2. Calculate similarity using multidimensional distance metrics
a. Calculate distance between two users u e u’on item i
I.
II.
III.
b. Calculate overall distance between two users
I.
Prediction-phase : HEURISTIC-BASED(3/3)
Multi-Criteria Recommender Systems - Specialists for managing information systems based on the semantic manipulation of information - University of Bari
Do they work BETTER?
Empirical results using the small-scale Yahoo! Movies dataset show that BOTH HEURISTIC APPROACHES OUTPERFORM the corresponding traditional single-rating collaborative filtering technique by up 3.8% in terms of precision-in-top-N mertric.
Multi-Criteria Recommender Systems - Specialists for managing information systems based on the semantic manipulation of information - University of Bari
Aggregation function
It finds r0 = f(r1,..,rk) relation btw overall and multi-criteria ratings.
Step 1. Estimate k individual ratings using any raccomandation tecnique.Step 2. f is choosen using domain expertize, statistical tecniques (linear
regression) or machine learning technique.Step 3. Overall rating of each unrated item is computed based on the k
predicted individual criteria rating and the choosen aggregation function f.
up 0.3-6.8% in termsof precision-in-top-Nmertric.(Yahoo Movies)
Prediction-phase : MODEL-BASED (1/2)
Multi-Criteria Recommender Systems - Specialists for managing information systems based on the semantic manipulation of information - University of Bari
PERFORMANCE
Other Approaches:
• Probabilstic Modeling Approach (Sahoo et all.)(Yahoo Movies!; Precision/Recall-in-top-N mertric - maximum of 10% increase)
•Multi singular value decomposition(MSVD) approach (Li et all.)(Collaborative filtering; context of restaurant recommender systems, Precision-in-top-N mertric - maxiumum of 5% increase).
Prediction-phase : MODEL-BASED(2/2)
Multi-Criteria Recommender Systems - Specialists for managing information systems based on the semantic manipulation of information - University of Bari
Recommendation-phaseWhen overall ratings are included as part of the model , the raccomandation process is verystraightforward, essentially the same as in single-criteria RS.
Without an overall rating the recommandation process becomes more complex.
Approaches for Multi-criteria optimization :
- Finding Pareto optimal solutions; - …..
Multi-Criteria Recommender Systems - Specialists for managing information systems based on the semantic manipulation of information - University of Bari
Using Multi-Criteria ratings as RECOMMENDATION FILTERS
Multi-criteria ratings can be used as recommendation filters in RS.
Story : 8Actors: 7
Multi-Criteria Recommender Systems - Specialists for managing information systems based on the semantic manipulation of information - University of Bari
Story: 9; Actors:10
Story: 8; Actors:8
Story: 10; Actors:7
DATASET
• Yahoo Movies!
• Trip Advisor
Multi-Criteria Recommender Systems - Specialists for managing information systems based on the semantic manipulation of information - University of Bari
FRAMEWORK
• Single-rating
• Multi-rating : NO ONE!
Multi-Criteria Recommender Systems - Specialists for managing information systems based on the semantic manipulation of information - University of Bari
OPEN ISSUES & CHALLENGES
• Managing Intrusivness
• Reusing existing single-rating
recommendations technique
• Costructing the item evaluation criteria
• Dealing with missing multi-criteria ratings
• Developing new MCDM modeling approach
• Collecting large-scale multi criteria rating data
Multi-Criteria Recommender Systems - Specialists for managing information systems based on the semantic manipulation of information - University of Bari
REFERENCES
• Accuracy Improvements for Multi-Criteria Recommender Systems (Dietmar J., Zeynep K., Fatih G.)
• Multi-Criteria User Modeling in Recommender Systems (Kleanthi L., Nikolaos F., Alexis T.)
• Multi Criteria Recommender Systems (Adomavicius, Manouselis, Kwon)
• New Recommendation Techniques for Multi-Criteria Rating Systems (Adomavicius, Kwon)
Multi-Criteria Recommender Systems - Specialists for managing information systems based on the semantic manipulation of information - University of Bari
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