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