Download - XPLODIV: An Exploitation-Exploration Aware Diversification Approach for Recommender Systems

XPLODIV: An Exploitation-Exploration Aware Diversification

Approach for Recommender Systems Andrea Barraza-Urbina, Benjamin Heitmann, Conor Hayes, Angela Carrillo-Ramos

The 28th International FLAIRS Conference

May 18-20, 2015

Hollywood, Florida, USA

Pontificia Universidad Javeriana

Facultad de Ingeniería

Maestría en Ingeniería de Sistemas y

Computación

Bogotá, Colombia

The Insight Centre for Data Analytics

Unit for Information Mining and Retrieval (UIMR)

National University of Ireland

Galway, Ireland

Centre for Data AnalyticsAgenda

Introduction

XPLODIV

Diversification

Approach

Conclusion and

Future Work

Experimental

Validation

Literature

Review

2

Centre for Data Analytics

IntroductionConclusion and

Future Work

Experimental

Validation

Literature

Review

3

XPLODIV

Diversification

Approach

Agenda

4

>50% of Job Applications

are due to

Recommendation

~75% of Watched Movies

are due to

Recommendation

Tools that help users identify interesting products by means of personalized

suggestions.

Discovery

Recommender Systems

5

User-Item Matrix

Rating

Recommender Systems


The task of selecting a subset of k elements from a broader set S in order tomaximize an objective function that considers both the relevance anddiversity of the k elements.

DiversityRelevance

A set is diverse if there is a high level of heterogeneity(dissimilarity) between the items in the collection.

6

The Diversification Problem


Search SpaceUser Profile

Recommend 10 movies to a user…

Movie Recommendation System

7


Comedy

ActionDrama


What happens if the user is no longer interested in Action

movies?

Organize by relevance…

8


User Profile

Comedy

ActionDrama


VS.

Diversity-Variety-Balance-Disparity

Relevance

Relevance

Diversity

In response to user profile ambiguity and the redundancy among results…

9



• Offering items representative of the variety of the user’s tastes.

• Offering novel products to explore unknown user preferences.

• Novelty can be achieved depending on how far or diverse an item is from the user’s past experience.

Discovery

Exploitation of the User Profile

Exploration of novel products

10

Exploitation vs. Exploration


Design a diversification technique that:

Research Goal


XPLODIV

Diversification

Approach

Conclusion and

Future Work

Experimental

Validation

Literature

Review

12

Introduction

Agenda

Centre for Data AnalyticsAnalysis of Diversification Techniques

Information Retrieval Recommender Systems

[Carb98] [Agra09] [Sant10] [Zhen12] [Smyt01] [Zieg05] [Varg12] [Adom09]

Type of Solution

Greedy Optimization -

Explicit Approach -

Implicit Approach -

Trade-off diversity vs. relevance

Control of diversity vs.

relevance trade-off ? ?

Trade-off exploitation vs. exploration

Encourages Discovery ? ? ? ?

Control of exploitation vs.

exploration trade-off -

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Type of Solution

Greedy Optimization -

Explicit Approach -

Implicit Approach -



relevance trade-off ? ?


Encourages Discovery ? - - - ? ? - ?


exploration trade-off - - - - -

Analysis of Diversification Techniques

14

Control of diversity vs. relevance trade-off

Centre for Data AnalyticsAnalysis of Diversification Techniques



Type of Solution

Greedy Optimization + + + + + + + -

Explicit Approach - + + + - - + -

Implicit Approach + - - - + + - -



relevance trade-off + - + + + + ? ?


Encourages Discovery ? ? ? - ?


exploration trade-off -

15

Control of Exploitation vs. Exploration trade-off

Encourages Discovery

Current solutions are mostly inspired by work in Information Retrieval


XPLODIV

Diversification

Approach

Conclusion and

Future Work

Experimental

Validation

Literature

Review

16

Introduction

Agenda


Traditional

Recommendation

Algorithm

Candidate

ItemsFinal Diversified

Recommendation

List

User

Profiles

Item

Profiles

XPLODIV: Exploitation-Exploration

Diversification Technique

Diversification

Technique

XPLODIV

We formulate our approach as a:

• Post-Filtering Technique• Greedy optimization problem

17


XPLODIV 𝑖,𝕌,ℝ = 𝛼 ∙ 𝑟𝑒𝑙 𝑖 + 1 − 𝛼 ∙ 𝑑𝑖𝑣 𝑖,ℝ ∙ 𝛽 ∙ 𝑥𝑝𝑙𝑜𝑖𝑡(𝑖,𝕌) + 1 − 𝛽 ∙ 𝑥𝑝𝑙𝑜𝑟𝑒 𝑖,𝕌

XPLODIV has four core dimensions:

Relevance

𝑟𝑒𝑙 𝑖

Diversity

𝑑𝑖𝑣 𝑖, ℝ

Exploitation

𝑥𝑝𝑙𝑜𝑖𝑡 𝑖, 𝕌

Exploration

𝑥𝑝𝑙𝑜𝑟𝑒 𝑖, 𝕌

• Each dimension must be normalized to return a value in the range [0,1].• 1 is the highest desirable value.

18

XPLODIV



The approach has two control parameters:

• The parameter 𝜶 controls the trade-off between relevance and

diversity.

• The parameter 𝜷 controls the trade-off between exploitation

and exploration.

19

XPLODIV


The relevance dimension gives priority to items that have high predicted rating.

How relevant is the item we are evaluating?

20

XPLODIV: Relevance Dimension

Normalized Predicted Rating


Average pairwise dissimilarity of an element i to a set ℝ

Minimum distance of an element i to a set ℝ

How distant is the item being evaluated from those previously

selected?

21

XPLODIV: Diversity Dimension

The diversity dimension measures how diverse an item i is in relation to a set of items ℝ.


The exploitation dimension gives priority to items that exploit known user preference information.

Probability of high rating of similar items

How representative is the item being evaluated of items found in

the user profile?

22

XPLODIV: Exploitation Dimension


Diversity of item i to user profile 𝕌 Average pairwise dissimilarity

Minimum dissimilarity

How novel is the item being evaluated for the user?

23

XPLODIV: Exploration Dimension

The exploration dimension gives priority to items that allow the user to discover and explore the unknown.


24

XPLODIV

𝜶


𝜷


XPLODIV

RELEVANCE DIVERSITY EXPLOITATION EXPLORATION

Average Dissimilarity

Minimum Dissimilarity

Dimension

Instantiation Alternatives

Importance of Associated Preference

KNN Importance of Associated Preference

User Profile Novelty

Neighborhood Novelty

25




Introduction

XPLODIV

Diversification

Approach

Conclusion and

Future Work

Experimental

Validation

Literature

Review

26

Agenda

Centre for Data AnalyticsExperimental Validation

Claim I

XPLODIV can be tuned towards different configurations of relevance, diversity,

exploitation and exploration.

Claim II

XPLODIV produces results comparable to baseline techniques in terms of

relevance and diversity.

27


• 100,000 ratings • 943 users • 1682 movies

DatasetQuantitative Tests

28

Experimental Set-Up


Baselines

29

Experimental Set-Up

• No Diversity: returns the top k of candidate items.

• Random Diversity: returns a random selection of k items from

candidate items.

• Maximal Marginal Relevance (MMR) with α=0.5 : returns k items

selected with the technique MMR.

• Representative of implicit diversification approaches.

• Proposed by Carbonell et al. 1998.

• Has served as foundation for many related more recent approaches.

Quantitative Tests


XPLODIV




Dimension






30




XPLODIV




Dimension






31




No Bias

• 𝛼 = 0.5, 𝛽 = 0.5.

Relevance Bias

• 𝛼 = 0.8, 𝛽 = 0.5.

Exploitation Bias

• 𝛼 = 0.2, 𝛽 = 0.7.

Exploration Bias

• 𝛼 = 0.2, 𝛽 = 0.3.

Pure Exploitation

• 𝛼 = 0.0, 𝛽 = 1.0.

Pure Exploration

• 𝛼 = 0.0, 𝛽 = 0.0.

XPLODIV Test Cases

32

Experimental Set-Up

Exploitation Bias

Exploration Bias

𝜷

1.0

0.0

Relevance Bias

Diversity Bias

𝜶

1.0

0.0

33

Candidate

ItemsFinal Diversified

Recommendation

List

User

Profiles

Item

Profiles

Recommendation Algorithm User-User Collaborative FilteringApache Mahout

Size 100

MatrixUser - Movie

MatrixMovie - Genre

Traditional

Recommendation

Algorithm

Jaccard similarity coefficient to measure similarity between Movie Items

Prototype


Metrics

DIVERSITY

RELEVANCE

Exploration

Perspectives

Pairwise Intra-list Dissimilarity

nDCG

Dissimilarity Threshold Percentage

Metrics

User Profile ExploitationExploitation

34

How well each item from the User

Profile is represented by the set of

selected items?

How different are selected items

from each other?

How relevant are selected items

considering their rank position?

What is the percentage of novel

items in the set of selected items?

3535

Tendency Graph

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

No Diversity RandomDiversity

MMR XploDivNo Bias

XploDivRelevance

Bias

XploDivPure

Exploitation

XploDivExploitation

Bias

XploDivPure

Exploration

XploDivExploration

Bias

Relevance Diversity Exploitation Exploration

3636

-3.86% -0.57%

-14.56%-7.10%

-13.24%-9.33%

-14.19%-7.30%

137.52%

27.23%

4.78% 7.67%3.07%

-0.75%

19.33%

-8.17%

-31.28%-27.82%

82.80%

33.25%

-97.61%

37.83%

113.65%

98.86%

-100%

-75%

-50%

-25%

0%

25%

50%

75%

100%

125%

150%

MM

R

Xp

loD

ivR

elev

ance

Bia

s

Xp

loD

ivP

ure

Exp

loit

atio

n

Xp

loD

ivEx

plo

itat

ion

Bia

s

Xp

loD

ivP

ure

Exp

lora

tio

n

Xp

loD

ivEx

plo

rati

on

Bia

s

Relevance Diversity Exploitation Exploration

Loss-Gain Graph relative to “No Diversity”

Loss

/Gai

n P

erce

nta

ge

37

Our solution:

• Generates results comparable to baseline and state-of-the-art techniques.

• Can be tuned towards more explorative or exploitative recommendations.

Claim I Claim II

Summary


Introduction

XPLODIV

Diversification

Approach

Conclusion and

Future Work

Experimental

Validation

Literature

Review

38

Agenda

Centre for Data AnalyticsConclusion

39

Contributions:

1. Analytical comparison of related work.

2. Exploitation-Exploration Diversification Technique XPLODIV.

• Generates comparable results to baseline and state-of-the-art techniques.

• Explicitly considers the factor of exploration.

• Can be tuned to offer "exploitative diversity" or "explorative diversity" with

controlled sacrifice over relevance.

Centre for Data AnalyticsFuture Work

• Dynamically learn values for the control parameters 𝛼 and 𝛽 to adapt XPLODIV to different

user profile and dataset characteristics.

• The use of XPLODIV as an aggregation strategy for results generated by different

recommendation algorithms (Hybrid Recommendation Systems).

• Design diversification strategies, based on XPLODIV, to enhance a Traditional

Recommendation algorithm.

• Example. Adapt XPLODIV to select a diverse set of neighbors in a Collaborative Filtering

Recommendation System.

40

41

Acknowledgements: This research was made possible by

funding from Science Foundation Ireland under grant number

SFI/12/RC/2289 (Insight) and by the Master's Program of the

Computer Science Department at the Pontificia Universidad

Javeriana, Bogotá.

Download - XPLODIV: An Exploitation-Exploration Aware Diversification Approach for Recommender Systems

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