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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
Centre for Data Analytics
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
Centre for Data Analytics
Search SpaceUser Profile
Recommend 10 movies to a user…
Movie Recommendation System
7
The Diversification Problem
Comedy
ActionDrama
Centre for Data Analytics
What happens if the user is no longer interested in Action
movies?
Organize by relevance…
8
The Diversification Problem
User Profile
Comedy
ActionDrama
Centre for Data Analytics
VS.
Diversity-Variety-Balance-Disparity
Relevance
Relevance
Diversity
In response to user profile ambiguity and the redundancy among results…
9
The Diversification Problem
Centre for Data Analytics
• 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
Centre for Data Analytics
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 -
13
Centre for Data Analytics
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 - - - - -
Analysis of Diversification Techniques
14
Control of diversity vs. relevance trade-off
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 -
15
Control of Exploitation vs. Exploration trade-off
Encourages Discovery
Current solutions are mostly inspired by work in Information Retrieval
Centre for Data Analytics
XPLODIV
Diversification
Approach
Conclusion and
Future Work
Experimental
Validation
Literature
Review
16
Introduction
Agenda
Centre for Data Analytics
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
Centre for Data Analytics
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
Centre for Data Analytics
XPLODIV 𝑖,𝕌,ℝ = 𝛼 ∙ 𝑟𝑒𝑙 𝑖 + 1 − 𝛼 ∙ 𝑑𝑖𝑣 𝑖,ℝ ∙ 𝛽 ∙ 𝑥𝑝𝑙𝑜𝑖𝑡(𝑖,𝕌) + 1 − 𝛽 ∙ 𝑥𝑝𝑙𝑜𝑟𝑒 𝑖,𝕌
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
Centre for Data Analytics
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
Centre for Data Analytics
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.
Centre for Data Analytics
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
Centre for Data Analytics
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.
Centre for Data Analytics
24
XPLODIV
𝜶
XPLODIV 𝑖,𝕌,ℝ = 𝛼 ∙ 𝑟𝑒𝑙 𝑖 + 1 − 𝛼 ∙ 𝑑𝑖𝑣 𝑖,ℝ ∙ 𝛽 ∙ 𝑥𝑝𝑙𝑜𝑖𝑡(𝑖,𝕌) + 1 − 𝛽 ∙ 𝑥𝑝𝑙𝑜𝑟𝑒 𝑖,𝕌
𝜷
Centre for Data Analytics
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
XPLODIV 𝑖,𝕌,ℝ = 𝛼 ∙ 𝑟𝑒𝑙 𝑖 + 1 − 𝛼 ∙ 𝑑𝑖𝑣 𝑖,ℝ ∙ 𝛽 ∙ 𝑥𝑝𝑙𝑜𝑖𝑡(𝑖,𝕌) + 1 − 𝛽 ∙ 𝑥𝑝𝑙𝑜𝑟𝑒 𝑖,𝕌
Normalized Predicted Rating
Centre for Data Analytics
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
Centre for Data Analytics
• 100,000 ratings • 943 users • 1682 movies
DatasetQuantitative Tests
28
Experimental Set-Up
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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
Centre for Data Analytics
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
30
XPLODIV 𝑖,𝕌,ℝ = 𝛼 ∙ 𝑟𝑒𝑙 𝑖 + 1 − 𝛼 ∙ 𝑑𝑖𝑣 𝑖,ℝ ∙ 𝛽 ∙ 𝑥𝑝𝑙𝑜𝑖𝑡(𝑖,𝕌) + 1 − 𝛽 ∙ 𝑥𝑝𝑙𝑜𝑟𝑒 𝑖,𝕌
Normalized Predicted Rating
Centre for Data Analytics
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
31
XPLODIV 𝑖,𝕌,ℝ = 𝛼 ∙ 𝑟𝑒𝑙 𝑖 + 1 − 𝛼 ∙ 𝑑𝑖𝑣 𝑖,ℝ ∙ 𝛽 ∙ 𝑥𝑝𝑙𝑜𝑖𝑡(𝑖,𝕌) + 1 − 𝛽 ∙ 𝑥𝑝𝑙𝑜𝑟𝑒 𝑖,𝕌
Normalized Predicted Rating
Centre for Data Analytics
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
Centre for Data Analytics
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
Centre for Data Analytics
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