Anisio Lacerda

Transcription

Anisio Lacerda

Improving Daily Deals Recommendation Using
Explore-Then-Exploit Strategies
Anisio Lacerda
1
Origins of the Material
•
Lacerda, A.,Veloso, A., Ziviani, N. (2013). Exploratory and
interactive daily deals recommendation. ACM Recommender
Systems Conference
•
Lacerda, A., Santos, R.L.T.,Veloso, A., Ziviani, N. (2015).
Improving daily deals recommendations using explore-thenexploit strategies. Information Retrieval Journal
2
Daily-Deals Sites (DDSs)
Daily-Deal Site
(mediator)
Merchant
Customer
(seller)
(buyer)
3
Comissions to DDSs
Daily-Deal Site
(mediator)
Merchant
Customer
(seller)
(buyer)
4
Deal recommendation task
Email (communication channel)
Customer
Database
Feedback? What?
Selected
Customers
•
•
What should we offer to customers?
How can we use feedback to improve the
quality of subsequent recommendations?
5
Challenges
•
•
•
Deals have a short life period
Catalog is dynamic
Most customers are sporadic bargain hunters
•
•
Past preference data is extremly sparse
Customer taste may undergo temporal drifts
6
Catalog dynamicity
•
•
~30% of new deals on average (per day)
Maximum lifetime of 4-5 days
7
Deal recommendation: Method
•
Idea: get customer feedback on active catalog as
soon as possible
•
•
Pure exploration (get feedback)
Pure exploitation (use feedback)
•
•
epsilon-first strategy [Tran-Thanh et al. 2010]
Co-purchase network:
Customer
Common
purchase
8
Deal recommendation: Explore-then-exploit
(one day)
Exploration
Exploration
Co-purchase
Network
Building Co-Purchase
Network
Sorting
Sorting
Criterion
Splitting
Splitting
Strategy
Recommendation
Randomize
Algorithm
Exploitation
Feedback-based Re-ranking
Recommendation
Recommender
Algorithm
Exploitation
9
(one day)
Exploration
Exploration
Co-purchase
Network
Network
Sorting
Sorting
Criterion
Splitting
Splitting
Strategy
Recommendation
Randomize
Algorithm
Exploitation
Recommendation
Recommender
Algorithm
Exploitation
10
Sorting criteria
•
Explores customers that:
•
•
•
are likely to provide feedback
are close to the largest number of
customers in the co-purchased network
Exploits the gathered information:
•
with customers that are likely to benefit
from such information
11
Sorting criteria
•
Complex network metrics
A. Degree
B. PageRank
C. Betweenness
D. Eigenvector
E. Clustering coefficient
12
(per day)
Exploration
Exploration
Co-purchase
Network
Network
Sorting
Sorting
Criterion
Splitting
Splitting
Strategy
Recommendation
Randomize
Algorithm
Exploitation
Recommendation
Recommender
Algorithm
Exploitation
13
Splitting strategy
Splitting customers into two distinct sets:
a. from whom we will obtain feedback
b. for whom we will use gathered feedback
to improve recommendations
14
Splitting strategy
•
•
Full Explore (FE): follow the order imposed by corresponding metric
k-Way Merge (KWM): alternate central customers into exploration
and exploitation to avoid redundant feedback
Exploration
Exploration
Co-purchase
Network
Network
Sorting
Sorting
Criterion
Splitting
Splitting
Strategy
Recommendation
Randomize
Algorithm
Exploitation
Recommendation
Recommender
Algorithm
Exploitation
15
Splitting strategy
•
•
Full Explore (FE): follow the order imposed by corresponding metric
k-Way Merge (KWM): alternate central customers into exploration
and exploitation to avoid redundant feedback
C
D
B
E
Degree
A (3)
B (3)
C (1)
D (1)
E (1)
F (1)
F
(a)
(b)
Full
Explore
A
B
C
D
E
F
(c)
Chunks
A
B
C
D
E
F
K-Way
Merge
A
C
E
B
D
F
(d)
16
(per day)
Exploration
Exploration
Co-purchase
Network
Network
Sorting
Sorting
Criterion
Splitting
Splitting
Strategy
Recommendation
Randomize
Algorithm
Exploitation
Recommendation
Recommender
Algorithm
Exploitation
17
Deals recommendation
•
•
Exploration
•
Send randomized recommendations to
customers
•
Update co-purchased network
Exploitation
•
Re-ranking of deals recommendations
given by baseline recommenders
18
Experimental setup
•
•
•
Metric: MAP
Peixe Urbano dataset (2-months)
Baselines
•
•
•
Most Popular
WRMF [Hu et al. 2008]
CLiMF [Shi et al. 2012]
19
Related work
•
WRMF [Hu et al. 2008]
•
•
•
Collaborative-filtering
Implicit feedback
CLiMF [Shi et al. 2012]
•
•
•
Collaborative-filtering
Implicit feedback
Minimize Mean Reciprocal Rank (MRR)
20
Existing RecSys
•
RQ1: How do existing recommendation algorithms perform in a
DDS scenario?
epsilon
Most Popular
WRMF
CLiMF
Baseline (0.0)
0.01
0.05
0.10
0.20
0.30
0.40
0.50
0.90
0.199
0.214
0.224
0.240
0.258
0.264
0.255
0.252
0.207
0.185
0.199
0.212
0.231
0.245
0.249
0.242
0.240
0.188
0.169
0.184
0.196
0.220
0.239
0.240
0.233
0.227
0.179
21
Explore-then-exploit
•
RQ2: Can we improve the performance of existing recommendation
algorithms using our explore-then-exploit approach?
epsilon
Most Popular
WRMF
CLiMF
Baseline (0.0)
0.01
0.05
0.10
0.20
0.30
0.40
0.50
0.90
0.199
0.214
0.224
0.240
0.258
0.264
0.255
0.252
0.207
0.185
0.199
0.212
0.231
0.245
0.249
0.242
0.240
0.188
0.169
0.184
0.196
0.220
0.239
0.240
0.233
0.227
0.179
MAP values - Betweeness
22
•
RQ3: What is the impact of our proposed k-Way Merge splitting
strategy?
epsilon
FE
KWM
Gain (%)
Baseline (0.0)
0.01
0.05
0.10
0.20
0.30
0.40
0.50
0.90
0.199
0.201
0.202
0.206
0.213
0.224
0.231
0.228
0.204
0.199
0.211
0.225
0.241
0.258
0.250
0.249
0.247
0.208
5.1
11.6
16.9
21.0
11.3
7.9
8.1
1.9
MAP values - Most Popular+Degree
23
•
RQ3: What is the impact of our proposed k-Way Merge splitting
strategy?
epsilon
FE
KWM
Gain (%)
Baseline (0.0)
0.01
0.05
0.10
0.20
0.30
0.40
0.50
0.90
0.199
0.201
0.202
0.206
0.213
0.224
0.231
0.228
0.204
0.199
0.211
0.225
0.241
0.258
0.250
0.249
0.247
0.208
5.1
11.6
16.9
21.0
11.3
7.9
8.1
1.9
MAP values - Most Popular+Degree
24
Findings
•
Collaborative-filtering algorithms are not better
than the naive strategy of suggesting nonpersonalized deals (MP)
•
We show that alternating central customers into
exploration and exploitation is better than fully
exploring or exploiting central customers
•
The proposed exploration-then-exploitation
method is very effective and well-suited to DDS
recommendation scenario
25
@CMU
•
Paper-reviewer assignment
•
RecSys
26
Improving Daily Deals Recommendation Using
Explore-Then-Exploit Strategies
Anisio Lacerda
27
Explore-then-exploit approach
•
Actions are costly
•
•
Constrained by a fixed budget (per day)
•
•
•
each email can affect brand’s reputability
number of events == number of users
duration of the problem is finite
The ideal exploitation is not obtained by
pulling the optimal arm repeatedly, but by
combinations of arms that maximize the
reward within the budget
28
Explore-then-exploit approach
•
- first strategy: common within this
scenario
•
percentage of the budget is used to
learn arm’s rewards (exploration)
•
percentage of the budget is used
for exploitation
29
DDS tasks
Deal
Email
Selection Prioritization
Email
Content
Available
Deals
Customer
History
Deal
Size?
Catalog
Revenue
Maximization
Customer
History
Who?
Selected
Customers
Feedback? What?
Selected
Customers
Customer Satisfaction
30
Deal size prediction
Deal
Email
Email
Content
Available
Deals
Customer
History
Deal
Size?
Catalog
Revenue
Maximization
Customer
History
Who?
Selected
Customers
Feedback? What?
Selected
Customers
31
Deal size prediction
•
Predict how many coupons will be sold
32
Tackling catalog volatility
Name Manhattan Kayak
Title
Up to 51% Off ...
Highlight
Kayakers and standup
paddleboarders tour the
Hudson River ...
Description
Kayakers, unlike
pancakes or depressed
turtles, muster the will
to get ...
33
Catalog interaction
34
Related Work
•
Global Predictor (GLPR) [Byers et al. 2012]
•
•
•
Non-textual features
No market information
One Predictor per Market (OPBM) [Lappas
and Terzi 2012]
•
•
Description
•
No market interaction
Latent Dirichlet Allocation (LDA) +
Hierarchical Clustering
35
Deal size prediction: Method
1. Textual features extraction
2. Market clustering
3. Market prediction learning
4. Expectation Maximization prediction learning
36
Deal size prediction: Terms extraction
•
Field-based term extraction
37
Deal representation
•
Fields
1. Name
2. Title
3. Highlight
4. Description
5. BoW (1,2,3,4)
• Term weights
• TF, TF*IFF, TS, TS*IFF, where:
• Term Frequenty (TF)
• Term Spread (TS)
• Inverse Feature Frequency
(IFF)
6. Concatenation (1,2,3,4)
38
Deal size prediction: Market clustering
•
Latent Dirichlet Allocation (LDA)
•
latent topics = markets
39
Identifying markets
“Gym”
“Hair Salon”
“Sports”
class
salon
camping
fit
hair
week
body
look
sport
train
services
day
40
Deal size prediction: Market predictors
•
Support Vector Regressor
41
Deal size prediction: Predictor
•
Expectation-Maximization
Market interaction
42
Deal size EM predictor
Expectation
unknown parameter:
representativeness of
each market
Maximization
•
RMSE as loss function
43
Experimental setup
•
•
Interleaved Test-then-train [Bifet et al. 2010]
Datasets
•
•
•
•
Groupon (English)
LivingSocial (English)
Baselines
•
•
•
Peixe Urbano (Portuguese)
GPBM (Single market)
OPBM (Multiple markets)
RMSE
44
Overall effectiveness
•
RQ1: What is the effectiveness of our method?
RMSE
Ours GLPR Gain OPBM Gain
Groupon
1.13
1.37
17.6
1.35
16.3
LivingSocial
1.02
1.12
9.60
1.11
8.1
Peixe Urbano
1.14
1.29
11.7
1.25
9.1
45
Weighting schemes and deal representation
•
•
RQ2: What is the impact of different weighting
schemes and deal representation?
Groupon and 50 markets
RMSE
Name Title Highlight Description BoW Concat.
TS
1.173 1.152
1.201
1.167 1.148
1.422
TSxIFF 1.211 1.227
1.226
1.295 1.353
1.214
1.190 1.146
1.217
1.157 1.162
1.133
TFxIFF 1.198 1.216
1.236
1.458 1.461
1.213
TF
46
Market-aware predictors
•
•
RQ3: Is market clustering effective?
Difference between a single market predictor and per-market
predictor
Groupon
Living Social
47
Number of markets
•
RQ4: What is the impact of number of markets?
RMSE
# markets
5
10
20
30
40
50
100
Groupon LivingSocial
1.366
1.295
1.255
1.192
1.196
1.133
1.182
1.128
1.119
1.094
1.020
1.086
1.137
1.093
Peixe
Urbano
1.317
1.244
1.151
1.143
1.167
1.149
1.184
48
Findings
•
Taking into account deals interaction and
structure improve deal size prediction
•
Gains range from 8% to 17%
49
Email prioritization problem
Deal
Email
Email
Content
Available
Deals
Customer
History
Deal
Size?
Catalog
Revenue
Maximization
Customer
History
Who?
Selected
Customers
Feedback? What?
Selected
Customers
50
Email prioritization
40%
28.1
30%
21.1
20%
10%
0%
•
Abuse/Unsubscribe
Open
Click
0.14
3.01
Daily Deals
0.29
4.01
[Mailchimp]
Average
Lower open and click rates for DDSs
• Large number of unnecessary emails sent
• Optimize number of emails sent to customers
51
1=
2
Recommender
u3
u4
u1
u2
u5
Customers
email
Catalog
Deals
52
MAB: Advantages
•
Usage data rather than manually labeled
relevance judgments
•
•
•
Larger quantity
Lower cost
Online learning approach:
•
As training data is being collected, it
immediatly impacts effectiveness
53
Sorting criteria (Arms)
#
Criteria
C1
C2
C3
C4
C5
C6
C7
Number of purchases already performed
Days since the last purchase
Savings
Average price
Average discount percentage
Discount percentage
Recommender confidence
54
MAB algorithms
•
epsilon-greedy: at time t
epsilon
1-epsilon
•
.
.
.
N criteria
(Exploitation)
Best criterion
UCB1-Normal: at time t
•
upper confidence bound
(Exploration)
ucb1
.
.
.
ucb2
N criteria
ucbN
55
Experimental setup
•
•
•
Interleaved Test-then-train [Bifet et al. 2010]
Dataset: Peixe urbano (2 months)
Baselines:
•
•
Random criterion selection
Reward
•
1 / (# purchases current testing day)
56
Criterion usage
•
RQ1: What is the fraction of times a specific
criterion was chosen?
57
Cumulative reward
10% of emails sent
5.5
5
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
(0.10)
(0.50)
(0.90)
UCB
Random
1
5
10
Cumulative Reward
Cumulative Reward
RQ2: What is the performance of MAB
algorithms over time?
15
20
30-Day Period
25
30
50% of emails sent
20
18
16
14
12
10
8
6
4
2
0
(0.10)
(0.50)
(0.90)
UCB
Random
1
5
10
15
20
25
30
30-Day Period
58
Cumulative reward
RQ3: What is the trade-off between the cumulative
reward and the number of emails sent?
Cumulative Reward
30
Upper Bound
✏ = 0.1
UCB
✏ = 0.5
✏ = 0.9
Random
25
20
15
10
5
0
0.1
0.2
0.3
0.4 0.5 0.6 0.7
Fraction of e-mails sent
0.8
0.9
1.0
59
Findings
•
We reach a 10% reduction in terms of the number of
emails sent without affecting reward
•
We are able to avoid sending about 30-40% of the
emails, if a reduction in terms of reward is allowed
60
Future Work
•
Are non-textual features effective for market
identification?
•
Can learning to rank algorithms provide a better way
to sort customers?
•
Can we combine email prioritization and deal
recommendation solutions to select customers and
improve recommendation effectiveness?
61
Catalog interaction
Eye exam
Restaurants
Eyewear
62
Expectation
unknown parameter:
representativeness of
each market
deal size
Maximization
•
RMSE as loss function
63
Number of markets
•
RQ4: What is the impact of number of markets?
RMSE
# markets
5
10
20
30
40
50
100
Groupon LivingSocial
1.366
1.295
1.255
1.192
1.196
1.133
1.182
1.128
1.119
1.094
1.020
1.086
1.137
1.093
Peixe
Urbano
1.317
1.244
1.151
1.143
1.167
1.149
1.184
64
1=
2
Recommender
u3
Fraction of u4
sent emails u1
u2
20%
u5
C1
Customers
email
Catalog
Deals
65
1=
2
Recommender
u3
Fraction of u4
sent emails u1
u2
20%
u5
C1
u5
u1
u2
u3
u4
C2
Customers
email
Catalog
Deals
66
1=
2
MAB
Algorithm
Recommender
u3
Fraction of u4
sent emails u1
u2
20%
u5
C1
u5
u1
u2
u3
u4
C2
Customers
email
Catalog
Deals
67
1=
2
Reward
MAB
Algorithm
t1 = C2 = u5
u3
Fraction of u4
sent emails u1
u2
20%
u5
C1
u5
u1
u2
u3
u4
C2
Customers
Recommender
email
Catalog
Deals
68
1=
2
Reward
MAB
Algorithm
t1 = C2 = u5
t2 = C1 = u3
u3
Fraction of u4
sent emails u1
u2
20%
u5
C1
u5
u1
u2
u3
u4
C2
Customers
Recommender
email
Catalog
Deals
69
Contributions
•
The exploitation of Markets for tackling deal
interaction
•
•
A new method for Deal Size Prediction
•
•
A new method for Prioritizing Emails
•
•
A new method for Deal Recommendation
Criteria to sort customers according to their
probability of email click
The exploitation of Customer Feedback for Deal
Recommendation
Use of Peixe Urbano real-world dataset
70
•
Costa, T.F., Lacerda, A., Santos, R.L.T., and Ziviani, N. (2014).
Exploiting taxonomies for new item recommendation.
•
Menezes, D., Lacerda, A., Silva, L.,Veloso, A., Ziviani, N.
(2013). Weighted slope one predictors revisited. WWW
Companion.
•
Ribeiro, M.T., Lacerda, A.,Veloso, A., Ziviani, N. (2012).
Pareto-efficient hybridization for multi-objective
recommender systems. RecSys
•
Menezes, G.V., Almeida, J.M., Belém, F., Gonçalves, M.A.,
Lacerda, A., Moura, E.S., Pappa, G.L.,Veloso, A., Ziviani, N.
(2010). Demand-driven tag recommendation. ECML/PKDD.
71
•
Ribeiro, M.T., Lacerda, A., Moura, E.S., Hata, I.,Veloso, A., Ziviani,
N. (2014). Multi-objective pareto-efficient approaches for
recommender systems. ACM Transactions on Intelligent
Systems and Technology. (Accepted)
•
Guimarães, A., Costa, T.F., Lacerda, A., Pappa, G.L., Ziviani, N.
(2013). GUARD: A genetic unified approach for
recommendation. Journal of Information and Data Management.
•
Matos-Jr, O., Ziviani, N., Botelho, F.C., Cristo, M., Lacerda, A.,
Silva, A.S. (2012). Using taxonomies for product
recommendation. Journal of Information and Data Management.
•
Botelho, F.C., Lacerda, A., Menezes, G.V., Ziviani, N. (2010).
Minimal perfect hashing: A competitive method for indexing
internal memory. Information Science.
72
DDS literature
•
•
•
•
•
•
•
Merchant experience using Groupon [Dholakia 2010]
DDS business model [Byers et al. 2011]
Merchants repeatedly participants [Farahat et al. 2012]
Health of DDSs business model [Byers et al. 2012b]
DDSs as marketing channels [Kumar and Rajan 2012]
Customer loyalty [Krasnova et al. 2013]
Shopping behavior [Li and Wu 2013]
73
DDS literature
•
•
Deals propagation on Twitter [Park and Chung 2012]
Impact on Yelp rating
•
negative side effect [Byers et al. 2012a]
•
•
•
revision [Byers et al. 2012c]
Deal Selection/Scheduling [Lappas and Terzi 2012]
•
•
deal size prediction
deal size prediction
Deal size sequential prediction [Ye et al. 2012]
74
local
prediction
market size
unknown parameter which re-scales
the representativeness of each market
•
Minimize the RMSE as loss function
75
Example
Representa)veness+
Market'1'
20'
Market'2'
70'
Market'3'
10'
Ranking+ Local+ Market+ Market+Perc+ Size/Market+ Deal+size+
D1'
20'
M1'
20/90'
10'
(0.5'*'10)'+'(0.5'*'20)'='15'
D2'
30'
M2'
70/90'
70'
(0.5'*'70)'+'(0.5'*'30)'='50'
D3'
40'
M1'
20/90'
10'
(0.5'*'10)'+'(0.5'*'40)'='25'
76
Textual Analysis
•
•
RQ2: Are terms descriptive and discriminative?
Fernandes et al. [2007] and Figueiredo et al. [2009]
Descriptive
Discriminative
77
Multi armed bandits
•
Multi-armed bandits (MAB)
•
•
N independent gambling machines (armed bandits)
•
•
Rewards when playing machine i: Xi,1, Xi,2, ...
Each machine has an unknown probability
distribution for generating the reward
Algorithm A chooses next machine based on
previous sequence of plays and rewards
78
MAB algorithms
•
UCB algoritthm
•
At time t, you have an upper confidence bound
(UCB) on the expected rewards
•
•
play the arm having the largest UCB!
UCB1-Normal (Auer et al. [2002])
sum of squared rewards
obtained from arm ci
average reward
obtained from arm ci
number of times arm ci
has been played so far
79
Deal recommendation example
u1
u7
rank
u3
Exploration
u1
u2
u2
Exploitation
u7
Most Popular
1
i2
(10)
2
i4
(8)
3
i6
(4)
4
i8
(2)
i6
i4
i4
i2
i6
i8
80
Time-aware reward
[Mailchimp]
81
82
Future Work
•
Are non-textual features effective for market
identification?
•
Different weighting schemes can provide a better
deal representation?
•
What is the impact of a more flexible assignment of
deals to markets in deal size prediction effectiveness?
•
What is the impact of our deal size predicton
solution on deal selection and deal scheduling
problems?
83
Future Work
•
Can learning to rank algorithms provide a better
way to sort customers?
•
Can we use the current catalog to build a
contextual multi-armed bandit algorithm for both,
email prioritization and deal recommendation?
•
Can we combine email prioritization and deal
recommendation solutions to select customers and
improve recommendation effectiveness?
84
Email example
•
screenshot PU
85
Conclusions
•
Taking into account deals interaction and
structure improve deal size prediction
•
The proposed method to prioritize emails
enables a 10% reduction in terms of the
number of emails sent without affecting
reward
•
The proposed method to prioritize emails
may avoid sending about 30%-40% of the
emails, if a small reduction in terms of
reward is allowed
86
Conclusions
•
The recommendation performance of
existing algorithms is no better than the
naive strategy of suggesting nonpersonalized deals for all customers
•
The proposed exploration-exploitation
algorithms are very effective and well-suited
to DDS recommendation scenario
87
•
RQ4: What is the impact of different criteria for sorting
customers?
epsilon
Degree
0.0
0.01
0.05
0.10
0.20
0.30
0.40
0.50
0.90
0.199
0.201
0.202
0.206
0.213
0.224
0.231
0.228
0.204
Clustering
Betweenness Eigenvector
Coefficient
0.199
0.199
0.199
0.214
0.203
0.218
0.224
0.203
0.242
0.240
0.206
0.248
0.258
0.215
0.252
0.264
0.223
0.248
0.255
0.229
0.248
0.252
0.229
0.243
0.207
0.202
0.212
PageRank
0.199
0.203
0.204
0.213
0.222
0.237
0.237
0.240
0.205
88
Cumulative reward
C1
C2
C3
C4
C5
C6
C7
e = 0.1
e = 0.5
e = 0.9
UCB
10%
1.77
4.72
3.41
2.63
2.15
1.90
4.65
5.01
4.43
3.56
4.55
Fraction of emails sent
50%
90%
8.18
24.04
19.42
28.54
18.13
27.70
13.60
27.30
13.01
27.23
15.04
27.10
17.89
27.82
21.58
28.57
20.30
28.02
18.82
27.33
20.45
28.36
100%
30.00
30.00
30.00
30.00
30.00
30.00
30.00
30.00
30.00
30.00
30.00
89
Thesis content
•
Revenue Maximization
•
•
Deal selection
•
•
Deal recommendation
90
Descriptive and discriminative power
Name
Title
Highlight
Description
BoW
Concatenation
GP
3.11
2.46
1.84
1.14
2.14
2.01
AFS
LS
PU
2.38 3.48
1.93 2.55
- 1.68
0.95 0.35
1.86 2.28
0.88 2.14
GP
9.06
8.80
8.49
8.92
8.91
8.86
AIFF
LS
7.28
7.09
6.66
6.67
6.80
PU
7.49
7.05
6.93
6.85
6.81
6.97
91
Method Validation
7 arms:
• 1 perfect
• 6 inefective
Cumulative Reward
•
16
14
12
10
8
6
4
2
0
(0.10)
(0.50)
(0.90)
UCB
Random
1
5
10
15
20
25
30
30-Day Period
92
-Performance-based payment
-Deal description
Daily-Deal Site
-Customer information
-Preferences/tastes
(mediator)
- Performance Feedback
-Relevant Deals
- Convertions/clicks
Merchant
(seller)
Customer
-Brands
-Products/services
(buyer)
93
Non-textual features
•
•
•
•
•
•
•
•
•
•
•
deal location (city)
business reputation (from Alexa)
price
discounted price
tipping point
multiple days
featured
limited
day of week
category
seasonality (period of the year when the offer is made
available)
94
Results
Total Purchase
Less Recent
Savings
Average Price
Average Discount
Average Discount Percentage
Customer Preference
1.0
Arm Usage
0.8
0.6
0.4
0.2
0.0
1
2
•
•
3
4
5
6
7
8
9
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Testing Days
50% of emails sent
epsilon-greedy (0.10)
95
MAB recommenders
96
EM predictor example
97
Sorting criteria independence
•
RQ9: Are criteria independent?
•
Kendall Tau
C1
C2
C3
C4
C5
C6
C7
C1
C2
C3
C4
C5
C6
C7
1.000 0.032 0.070 0.025 -0.018 -0.021 0.014
0.032 1.000 0.042 -0.013 0.012 -0.022 0.008
0.070 0.042 1.000 0.033 0.016 -0.011 0.010
0.025 -0.013 0.033 1.000 0.025 0.018 0.012
-0.018 0.012 0.016 0.025 1.000 -0.012 0.009
-0.021 -0.022 -0.011 0.018 -0.012 1.000 -0.016
0.014 0.008 0.010 0.012 0.009 -0.016 1.000
98
Arms usage
•
all graphs
99
Sorting dependence
•
chapter 5 table
100
RecSys tasks
•
figura da tese
•
•
rating prediction
top-N recommendation
101
Re-ranking strategy
•
exemplo de como fazemos o re-ranking da
saida dos algoritmos de recomendacao
102
Working papers
•
Lacerda, A.,Veloso, A., Ziviani, N. (2014). Will it flop or
rock? improving deal size prediction by exploiting
market interplay and intra-market competition.
•
Lacerda, A.,Veloso, A., Ziviani, N. (2014). Adding value to
daily-deals recommendation: Matching customers and
deals with multi-armed bandits.
•
Costa, T.F., Lacerda, A., Santos, R.L.T., Ziviani, N. (2014).
Exploiting taxonomies for new item recommendation.
103
Textual features
Average number of distinct terms
GP
LS
PU
Name
2.63
2.44
2.38
Title
8.63
5.41
11.44
Highlight
11.67
-
52.56
Description
132.02
57.24
93.49
BoW
135.75
58.06
123.79
Concatenation
154.65
65.23
161.37
104
Sorting criteria
•
Separate customers that:
• are more likely to provide feedback
• share similar tastes with many others
Exploration
Exploration
Co-purchase
Network
Network
Sorting
Sorting
Criterion
Splitting
Splitting
Strategy
Recommendation
Recommender
Algorithm
Exploitation
Recommendation
Recommender
Algorithm
Exploitation
105
Method
Personalized
Mail
Reward
MAB
Algorithm
Target
User
Recommender
...
Sorted Users
(Arms)
•
•
Deals
Sorting customers by probability of click
Select the best sorting criterion
106
local
prediction
market size
unknown parameter which re-scales
the representativeness of each market
•
Minimize the RMSE as loss function
107

Anisio Lacerda

Transcription

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