sistemas de recomendação hibridos baseados em mineração de preferências “pairwise”

6/10/14

27th Canadian Conference on Artificial Intelligence

Sistemas de Recomendação Hibridos baseados em Mineração de Preferências “pairwise”

Data Mining AULA 19 – Parte II

Sandra de Amo

6/10/14


ReferênciaAI2014

Sandra de Amo, Cleiane Gonçalves: Towards a Tunable Framework for Recommendation Systems based on Pairwise Preference Mining Algorithms, 27th Canadian Conference on Artificial Intelligence, May 2014, Montreal, Canada.

Towards a Tunable Framework for Recommendation Systems based on Pairwise Preference Mining Algorithms

Cleiane Gonçalves Oliveira and Sandra de Amo Federal University of Uberlandia – Brazil

[email protected]

Federal University of Uberlandia Laboratory of Information Systems

General PurposePopular Recommendation Systems:

• Main goal: to predict user ratings for yet unseen items.

• Classifications techniques: ratings viewed as classes

• In some domains: users evaluate an item by comparing it with other items already evaluated.

• Drawback 1: Classifiers classify items isolatedly. Poor accuracy.

• Drawback 2: no high ratings are predicted no recommendations

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General Purpose

We argue:

More interesting: to predict a ranking of top-k items, whatever the ratings they may be given by the user.

Input data: a “preference graph” (pairs of items (a,b): a is preferred to b)

Preference Mining Task : given two new items c, d which one is preferred by the user ?

Preference graph ranking on the nodes

Main Contribution

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A general framework for implementing Recommendation Systems basedon Preference Mining and Preference Aggregation techniques

Phase 1 : Building the Recommendation Model (offline)

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Phase 2 : Making Recommendations (online)

Module 1: Preference Representation 6/

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Ratings provided by user u Preference matrix for user u

• Each user u is represented by its preference matrix Mu

• The element i,j in the matrix is the degree of preference of user u on item i over item j.

• Calculated as where h must satisfy certain conditions (Chiclana et al. 2001)

• The family verifies such conditions

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Module 2: Profiles Construction

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Mu1 Mu2

Mu3

Cluster 1

Mu4 Mu5

Mu6Cluster 2

Mu7 Mu8

Mu9

Cluster 3

Users inside a given clusterhave similar taste.

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Module 3: Preference Aggregation 6/

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Mu1 Mu2

Mu3 A consensus preference matrix θCluster of similar matrices

Aggregation Operator

A unique consensus matrix θ is associated to each cluster

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Module 4 : Preference Mining 6/

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A consensus preference matrix M

Mining Algorithm Preference Model

• A Preference Model is any function capable to predict, given two items i1 and i2 which one would be preferred by a user whose preference matrix is M

• Two Preference Mining algorithms have been tested : CPrefMiner (de Amo et al. ICTAI 2012) and CPrefMiner* (de Amo et al., 2014 to appear)

• The Preference Model produced by CPrefMiner and CPrefMiner* = set of preference rules of the form: IF <context> THEN I prefer `this’ to `that’

Ex. : IF Director = `Spielberg’ THEN I prefer Genre = Action to Genre = Drama

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Module 5: The Recommendation Process (online)

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Consensus θ1 + Preference Model 1



The Recommendation Model M

How M recommendsItems to a new user u ?

1. u must evaluate some few items i1, i2, …, in2. The preference matrix Mu for u (very sparse) is built3. Mu is compared to the consensus matrices θ1,…, θn4. The closest consensus is found : θ*5. The Preference Model associated to θ* is used to produce a ranking <i1, …, ik> of top-k most preferred items.

Experiments Set-up

•296 users ; 262 movies

•User-movie ratings from the Group Lens Project :

(userId, filmId, rating)

•Details on films from IDMB website

(filmId, Genre, Actors, Director, Year, Language)

•Total of evaluations: 67,971

Complete : 296 * 262 = 77,553

5-cross validation on users and on items

Datasets

Experiment Protocol

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The XPrefRec instantiation

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cosinecosine cosine

Some resultsPerformance

Execution Time

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Baseline: CBCF (Melville et al., AAAI 2002)

Hybrid recommendation system: content-based + collaborative filtering

Uses classification techniques for predicting user ratings

Conclusion and Future WorkIn this paper we proposed

• PrefRec : A general framework for implementing Recommendation Systems

• Hybrid Approach: content-based + collaborative filtering

• Preference Mining and Preference Aggregation techniques.

• Four modules

• Flexible – can incorporate new algorithms in each module

Future Work

• A more rigorous factor design of PrefRec

• To study the effects of the different factors involved at each module.

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sistemas de recomendação hibridos baseados em mineração de preferências “pairwise”

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