OFFER PERSONALIZATION USING TEMPORAL CONVOLUTION NETWORK AND OPTIMIZATION
Authors: Ankur Verma
Venue:  (2021)
Keywords: Machine Learning
Deep Learning
Optimization
Time-Series
Offer Personalization

Abstract: Lately, personalized marketing has become important for retail/e-retail firms due to significant rise in online shopping and market competition. Increase in online shopping and high market competition has led to an increase in promotional expenditure for online retailers, and hence, rolling out optimal offers has become imperative to maintain balance between number of transactions and profit. In this paper, we propose our approach to solve the offer optimization problem at the intersection of consumer, item and time in retail setting. To optimize offer, we first build a generalized non-linear model using Temporal Convolutional Network to predict the item purchase probability at consumer level for the given time period. Secondly, we establish the functional relationship between historical offer values and purchase probabilities obtained from the model, which is then used to estimate offer-elasticity of purchase probability at consumer item granularity. Finally, using estimated elasticities, we optimize offer values using constraint based optimization technique. This paper describes our detailed methodology and presents the results of modelling and optimization across categories.

Under review as a conference paper at ICLR 2021
OFFER PERSONALIZATION USING TEMPORAL CONVO- LUTION NETWORK AND OPTIMIZATION
Anonymous authors
Paper under double-blind review
ABSTRACT
Lately, personalized marketing has become important for retail/e-retail firms due significant rise in online shopping and market competition. Increase in online shopping and high market competition has led to an increase in promotional ex- penditure for online retailers, and hence, rolling out optimal offers has become imperative to maintain balance between number of transactions and profit. In this paper, we propose our approach solve the offer optimization problem the in- tersection of consumer, item and time in retail setting. To optimize offer, we first build generalized non-linear model using Temporal Convolutional Network to predict the item purchase probability at consumer level for the given time period. Secondly, we establish the functional relationship between historical offer values and purchase probabilities obtained from the model, which is then used to esti- mate offer-elasticity of purchase probability at consumer item granularity. Finally, using estimated elasticities, we optimize offer values using constraint based opti- mization technique. This paper describes our detailed methodology and presents the results of modelling and optimization across categories.
INTRODUCTION
In most retail settings, promotions play an important role in boosting the sales and traffic of the organisation. Promotions aim enhance awareness when introducing new items, clear leftover inventory, bolster customer loyalty, and improve competitiveness. Also, promotions are used on daily basis in most retail environments including online retailers, supermarkets, fashion retailers, etc. A typical retail firm sells thousands of items in week and needs to design offer for all items for the given time period. Offer design decisions are of primary importance for most retail firms, as optimal offer roll out can significantly enhance the business' bottom line.
Most retailers still employ a manual process based on intuition and past experience of the category managers to decide the depth and timing of promotions. The category manager has to manually solve the promotion optimization problem at consumer-iten granularity, ie., how to select an optimal offer for each period in a finite horizon as maximize the retailer's profit. It is difficult problem to solve, given that promotion planning process typically involves a large number of decision variables, and needs to ensure that the relevant business constraints or offer rules are satisfied. The high volume of data that is now available to retailers presents an opportunity to develop machine learning based solutions that can help the category managers improve promotion decisions.
In this paper, we propose deep learning with multi-obective optimization based approach to solve promotion optimization problem that can help retailers decide the promotions for multiple items while accounting for many important modelling aspects observed in retail data. The ultimate goal here is to maximize net revenue and consumer retention rate by promoting the right items at the right time using the right offer discounts at consumer-item level. Our contributions in this paper include a) Temporal Convolutional Neural Network architecture with hyperparameter configurations to predict the item purchase probability consumer level for the given time period. b) Design and implementation of F--maximization algorithm which optimises for purchase probability cut-off at consumer level. e) Methodology to estimate offer elasticity of purchase probability at consumer item granularity. d) Constraint based optimization technique to estimate optimal offers at consumer-ites granularity.

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2 RELATED WORK
There has been a significant amount research conducted on offer-based revenue management over the past few decades. Multiple great works have been done to solve Promotion Optimiza- tion problem. One such work is Cohen et al. (2017), where the author proposes general classes of demand functions (including multiplicative and additive), which incorporates effect, and uses Linear integer programming to solve Promotion Optimization problem. In one of the other work [Cohen & Perakis (, the author lays out different types of promotions used in retail. and then formulates the promotion optimization problem for multiple items. In paper (Co-) hen & Perakis (2018), the author shows the application of discrete linearization method for solving promotion optimization. Gathering learnings from the above papers, we create our framework for offer optimization. The distinguishing features of our work in this field include (i) the use of a non- parametric neural network based approach to estimate the item purchase probability at consumer level, (ii) the establishment of the functional relationship between historical offer values and pur- chase probabilities, and (iii) the creation of new model and efficient algorithm to set offers by solving a multi-consumen consumer-iten promotion optimization that incorporates offer-elasticity of purchase probability reference offer value
dip
3
METHODOLOGY
We built seperate models for each category, as we understand that consumer purchase pattern and personalized marketing strategies might vary with categories.
3.1 MODELLING
In our model setup, we treat each relevant consumer-item individual object and shape them into bi-weekly time series data based on historical transactions, where the target value at each time step (2 weeks) takes binary input, 1/0 (purchased/non purchased). Relevancy of the consumer- item is defined by items transacted by consumer during training time window. Our Positive samples (purchased/1) are time steps where consumer did transact the item, whereas Negative samples (non purchased/0) are the time steps where the consumer did not buy that item. We apply sliding windows testing routine for generating out of time results. The time series is split into parts train (48 weeks), validation (2 weeks) and test (2 weeks). All our models are built in multi-object fashion for individual category, which allows the gradient movement to happen across all consumer-item combinations split in batches. This enables cross-leannns leasning to happen across consumers/items, A row in time series represented by
Yen h(h,, Crn ic .., iCra di den)
where Yeu is purchase prediction for consumer "c" for item 'i' at time 't'. 'n' the number of time lags. denotes attributes of item 'i' like category, department, brand, color, size, etc at time 't'. c denotes attributes consumer 'c' like age, sex and transactional attributes at time 't', C-n denotes the transactional attributes of consumer "c' at lag of 't-n' time steps. ic, denotes transactional attributes such as basket size, price, offer, etc. of consumer 'c" towards item 'i' at time 't' d, is derived from datetime to capture trend and seasonality time 't'.
3.1.1 FEATURE ENGINEERING
Based on available dataset, we generate multiple features for the modelling activity. Some of the feature groups we perform our experiments are:
Datetime: We use transactional metrics at various temporal cuts like week, month, etc. Datetime related features capturing seasonality and trend are also generated. Consumer-Item Profile: We use transactional metrics at different granularities like consumer, item, and consumer-item. We also create features like Time since first order, Time since last order, time gap between orders, Reorder rates, Reorder frequency, Streak user purchased the item in a row, Average position in the cart, Total number orders. Price/Promotions: We use relative price and historical offer discount percentage to purchase propensity varying price, and discount values. Lagged Offsets: We use statistical rolling operations like mean, median, variance, kurtosis and skewness over temporal regressors for different lag periods generate offsets.
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3.1.2 LOss FUNCTION
Since we are solving Binary Classification problem, believe that Binary Cross-Entropy should be the most appropriate loss function for training the models. We use the below formula to calculate Binary Cross Entropy:
H, -
+ (1 y)--gy) p(y))
(2)
here Hp represents computed loss, y is the target value (label), and p(y) is the predicted probability against the target. The BCELoss takes non-negative values. We can infer from Equation that Lower the BCELoss, better the Accuracy.
3.1.3 MODEL ARCHITECTURE
Traditional machine learning models may not be a suitable choice for modelling h (Equation due to non-linear interactions between the features. Sequence to Sequence (Sutskever et al. (014))
neural network architectures seems to be sound choice for tackling our problem. Hence, we use
Entity Embeddings [Guo & Berkhahn [2016)] + Temporal Convolutional Network (TCN) (Figure T architecture for building all the models across categories. Originally proposed in (Lea et al. (016) J.
TCN can take sequence of any length and map to an output sequence of the same length. For this to accomplish, TCN uses ID fully-convolutional network (FCN) architecture, where each hidden layer is the same length as the input layer, and zero padding length (kernel size-1) is added to keep subsequent layers the same length previous ones. Also, the convolutions in the architecture are causal, meaning that there is no information leakage from future to past. To achieve this, TCN uses causal convolutions [Bai al. (, convolutions where output at time is convolved only with elements from time I and earlier in the previous layer. For 1-D sequence input x and filter f the dilated convolution operation DC element k of the sequence is defined as:
(3) where d the dilation factor, n the filter size, and k-d, accounts for the direction of the past. When d = 1, dilated convolution reduces to regular convolution. Using larger dilation enables an output at the top level to represent a wider range of inputs, thus effectively expanding the receptive field of ConvNet.
DC(k)= = (xr af)(k) = END1 f(i) Fud where x E RR and f (O...., n- - - R
As can be seen in Figure [ Our network architecture comprises of dilated Convolutions com- bined with entity embeddings [Guo & Berkhahn (2016)]. Post Convolutions and concatenation with embedding tensor, the created tensor flows through fully connected ReLU layers yielding to sig- moid dense layer. To seggregate static and temporal features, group input tensor into 4 seperate tensors, as can be seen in [
These categorical features that do not vary with time. This includes sumer attributes like sex, marital status and location along with different item attributes like category, department and brand. Temporal Categorical: These are categorical features that vary with time. includes all the datetime related features like week, month of year, etc. Static Continuous: These features are static but continuous. This includes certain consumer attributes like age and weight, item attributes like size, and certain derived features like target encoded features. Temporal Con- tinuous: These are time varying continuous features. All consumer and item related traditional attributes like number of orders, add to cart order, etc. falls under this bucket.
Static Categorical:
3.1.4 HYPERPARAMETER TUNING
We use documented best practices along with our experimental results to choose model hyperpa- rameters. Hyperparameter Optimization is performed over validation dataset. We list some of the hyperparameters along with the values tune for Deep neural network models.
Optimizer Parameters: RMSProp [Bengio & CA (015)) and Adam are used as optimizers across model runs. The learning rate is experimentally tuned to le-3, We also have weight decay of le-5 which helps a bit in model Regularization. Scheduler Parameters: CyclicLR [Smith] (017)) and ReduceLRROnPlttaat [Zaheer et al. (2118) Learning rates used as schedulers across model runs. we use 1e-3 as max Ir and 1c-6 as base Ir for cyclical learning rate along with the step size being the function of length of train loader. ReduceI.ROnPP ROnPlateau tuned le-6 min Ir. SWA: Stochastic
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Figure 1: Temporal Convolutional Network (TCN)
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Rulyy Connected Layer (ReLUU)
Fully Connaced Layer (Pell
Concalentiion
Fully Concared Lagens polL Lapere 3:
Denpu Dengo Layer (Sigmoid)
Weight Averaging (SWA) [zzmallov et al. (2018)] is used to improve generalization across Deep Learning models. SWA performs an equal average of the weights traversed by SGD with modified learning rate schedule. We use le-3 as SWA learning rate. Parameter Average: This is a method to average the neural network parameters of best model checkpoints post training, weighted by validation loss of respective checkpoints.
Apart from these parameters we also iterate to tune network parameters like number of epochs, batch size, number of Fully Connected Layers, convnet parameters (kernel size, dilations, padding) and embedding sizes for the categorical features. Binary Cross-Entropy 3 is used as loss function for all the models trained across categories. Neural Network models are built using deep learning framework Py MoTchh [aaszee et al. (2017)), and are trained on GCP instance containing 6 CPUs and single GPU.
3.2 F2 Fi-MAXIMIZATION
Post stacking, we optimize for purchase probability threshold based on probability distribution at a consumer level using Fy-Maximization. This enables optimal thresholding of consumer level probabilities to maximize F, measure (Lipton et al.) (2014)]. To illustrate the above, let us say we generated purchase probabilities for "n" items out of "b" actually purchased items by consumer 'c'. Now, let visualize the actuals and predictions 44) of 'n' predicted items for consumer 'c'.
A. = [ui, a22 .., (n] V 0j E {0.1} P. = [p1,P2...,Pb) Pm] V Dj [0,
(4)
A. represents the actuals for consumer 'c', with aj being 1/0 (purchased/non purchased). P. repre- sents the predictions for consumer "c" for the respective item, with Pj being probability value, 'n' represents total items for which the model generated purchase probabilities for consumer 'c'. Now we apply Decision rule DO) which converts probabilities to binary predictions, as described below in Equation 5
[1
Pi 2 Pr.
D(Pr.) : Pe 1xn + P''' 1xn :
(0 Otherwise
(5)
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P..
Vp E {0.1) k k= ==
Pr. is the probability cut-off be optimized for maximizing F1 measure [Lipton et al. (2014) for consumer ''., Decision rule DO) converts probabilities P. to binary predictions P such that if pj is less than Pr. then Pj equals O, otherwise 1. 'k' is the sum of predictions generated post applying Decision rule DO. Now we solve for Fj measure using equations and formulae described below.
Vp = = x A = (
Pn )
x
Gn
Precision:
Pt k
Recall,
VP
Fi.
Precisione Recalle Precistoni +
2
VP 1+D
(8)
Vpt. represents the number of items with purchase probabilities greater than Pr. which were actually purchased (True Positives). As can be seen, Formulae 8 is used to calculate Precision, Recall and Fy-score for consumer c'.
V Pre +
max Pr
subject to:
Pre E (0, 1)
Equation 9 represents the optimization function we solve to generate purchase predictions (1/0) for each consumer. Figure 5 Section shows the predicted probability distributions.
3.3 ELASTICITY FRAMEWORK
After modelling, establish the functional relationship between historical offer values and pur- chase probabilities obtained from the model, which is then used to estimate offer-elasticity of pur- chase probability at consumer item granularity. Given that our output layer of deep net is sigmoid and we are modelling for probability values. sigmoid function (Figure 2) seemed to us as an apt choice to study the variation of purchase probability with offer percent. We also perform multiple experiments as described in Figure Section to see the goodness of fit of sigmoid curve over our dataset across different categories. The average R² value for categories seen to approximately 75 percent.
f(x)
f(x== - (l-
(10)
1 + e- (ax++)
Since the functional relationship might vary with categories, learn seperate parameters of sigmoid for each category. We then use sigmoid curve to estimate elasticities, the x-elasticity of measures the fractional response of y fraction change in x, which can be written as:
dy/y dx/x
elasticity of y e(x
(11)
We incorporate equation [I determine the offer-elasticity of purchase probability. We use his- torical offer percent values consumer-item granularity, identified using following criteria in that order: a) average of last 4 weeks non-zero offer percent values of the consumer-item combination b) average of historical non-zero offer percent values of the consumer-iten combination e) average of last 4 weeks non-zero offer percent values of all same age consumer-item combinations within that category. Using Equations 10 and []] we establish offer-elasticity purchase probability (equation 12) as shown below, k being the offer percent and f(k) being purchase probability.
b. f(k)
f(k)) = x
e(k, = =
(12)
3.4 OFFER OPTIMIZATION
Post estimation of effer-elasticity of purchase probability, for each category, we solve the below op- timization function (Equation) 13 maximize Net Revenue, with Consumer Retention Rate greater
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Figure 2: Purchase probability vs. Offer percentage
PURCHASE PROSABLIIT
y f(x) 1 e
dy = f (x) dx
ae (ax+bb)
(1+ e (ax+b)))
0.5
f(x))
k
Elasticity
+ ((k) x
f(k)
50%
PERCENTAGE
than category threshold (R,z).
Ip
max Σ P 100
x (k: + 7h x k:)] x 1l Pr, (f(k)) + m x ((k))) x (k:))
s.t.
: 1 = 0.05 jj
4 j E z and j E (-20,20)
(Ai + mi x ki)
E 02)
(13)
1
1 Pr. (f(k)) + m e(ky, (kk)) x f(k)) >= Ry
n
1 Pr. (z) { x 2 Pre
Otherwise
In the equation above, is the total consumer-item samples modelled for a particular category. Ip is the price of the item, k; and f(k;) being the offer percent and purchase probability for the jth: consumer-item. 7i is the change in offer percent k,. 1p. () denotes the Indicator function at Pre. which is the optimal probability cut-off obtained from F, Ta-Maximization algorithm for consumer 'c' (Equation 9). cAki, (ki)) denotes the k; percent offer-elasticity of f(k,) purchase probability. Rı denaotes the Retention rate cut-off of category c. O₁ and 02 refers to offer range for that category. This is determined using the latest 2 weeks of consumer-item samples. We solve the Equation 13 using Linear Programming approach to compute optimal offer at consumer-item granularity. We observe that there is variance in the optimal offers generated from optimization engine across categories. We have shown the distribution of offers across categories in Figure --l--etioon// 4
EXPERIMENTS AND RESULTS
We use kaggle dataset available at AmExpert 2019. Figure 7 shows the schema of the data we have used to build our models. As mentioned in Section we use maximum of year data aggregated bi-weekly level at consumer-itens granularity. We split the data into three part train, validation and test based on our validation strategy. We generate consumer item bi-weekly data with purchase/ non purchase being the target, and use this data to train all our models.
4.1 EXPERIMENT SETUPS
We start with exploratary data analysis, looking the data from various cuts. We study the vari- ations of different features with our target (purchase/ non purchase). Some of our studies include category-wise sales distribution, density of transactions with varying offer percent and density of

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Figure 3: Exploratory Data Analysis
Graph-3
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1.0
Groph
Desile Franeecer
Desiby of Trreecoo
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Graph-1: Category-wise distribution, Graph-2:
density transactions varying percent, Graph-3: density of transactions with varying basket size
transactions with varying basket size (Figure 3). We observe that Grocery and Pharmaceutical con- tributes to approximately 77 percent of total sales. We also look at order probability variations at different temporal cuts like week, month and quarter, transactional metrics like total orders, total reorders, recency, gap between orders, at both consumer and item levels. We then perform multiple experiments with the above mentioned features and different hyperparameter configurations to land at reasonable hyperparameters to perform final experiments and present our results.
Figure 4: Offer- purchase probability
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Day
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Category level visnalizations describing the sensitivity of change in offer with purchase prohability
Figure 5: Probability Distributions
CON
CON
com
Chart-1: Predicted probability distribution actual label equals Chart-2: Predicted probability distribution actual label equals and Chart-3: cut-off probability distribution post F Fr-Maximization
4.2 RESULTS AND OBSERVATIONS
Figure shows the functional relationship of offer percetage with purchase probability obtained across different categories. It also contains the equations and goodness of fit in the form of R² for each category. Seafood and Bakery the top best fitted categories with R² values of 0.83 and 0.82 respectively. [igureB]Chart. and Chart-2 shows the predicted probability distribution when actual label equals 0 and 1 respectively the validation data split. Chart-3 shows the cut-off probability
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distribution post F1 -M-ximizzaiinn, and, we observe that highest density of cut-off probability lies between 0.2 to 0.3. Table [ presents the accuracy values post Modelling and F--Maximization. also has the average clasticity values along with weighted offer percent computed post optimization. From model performance perspective it is observed that Grocery category has least BCEL Loss of 0.0283. Pharmaceutical and Meat categories followed Grocery with BCELoss of 0.0296 and 0.0299 respectively, Also, Grocery has the best F₁ score of 0.512 followed by Meat and Pharmaceutical scoring 0.511 and 0.509 respectively. Vegetables is the most elastic category with elasticity value of 1.53, whereas Packaged meat and Skin and Hair care are the least elastic categories with elasticity value of 0.62. From Figure we can see the graphical representation of distribution of optimal offer calculated through optimization. We find Skin and Hair care along with Pharmaceutical to be left skewed, whereas Vegetables as well as Flowers and Plants to be right skewed.
Figure 6: Consumer- percent distribution with offer percent across categories
Sert,
Category level visualizations frequency distribation of offers generaled by optimization engine
Table 1: Model Results and Elasticity
Category
Sample size
BCEL Loss
Precision
Recall
Fy-Score
Avg Elasticity
Weighted Offer Percent
Gimcery
28.950
0,0283
0.524
0.501
0.512
1.13
23.37
Bakery
1,628
0.0415
0,346
0.391
0.367
1.06
22.13
Pharnaceutical
20.492
0.0296
0.538
0.483
0.509
0.73
17.89
Packaged Meat
1,473
0.0329
0.473
0.452
0.462
0.62
19.13
Seafood
539
0.0382
0.497
0.379
0.43
0.89
16.23
Natural Products
2,399
0.0319
0.431
0.524
0.485
0.95
21.78
Dairy. Juices, Snacks
2,060
0.0396
0,394
0.492
0.438
1.04
22.02
Prepared Food
1,407
0.0472
0,338
0.295
0.315
1,03
28.27
Skin, Hair Care
1.906
0.0391
0.429
0.453
0.441
0.62
7.93
Meat
1,767
0.0299
0.498
0.524
0.511
0.94
18.92
Flowers, Plants
491
0.0483
0.275
0.318
0.295
1.43
30.24
Vegetables (cut)
124
0.04669
0,364
0.267
0.308
1.53
37.92
5 CONCLUSION
We have presented our detailed methodology to solve the offer optimization problem at the intersec- tion of consumer, item and time in retail setting. We have also presented the results of our models and optimization in the form of model accuracies and graphical representations at a category level. At the same time we understand that computation strategy is a key aspect in modelling millions of consumers, and we intend to further explore this aspect by building Transfer Learning framework Yosinski et al. (2014). We are also working to further improve our Sequence to Sequence neural network architectures to improve accuracy and decrease computation time.
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REFERENCES
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Yoshua Bengio and MONTREAL CA. Rmsprop and equilibrated adaptive learning rates for non- convex optimization. corr abs/1 502.0439. 2015.
Maxime Cohen and Georgia Perakis. Promotion optimization in retail. Available SSRN 3194640, 2018.
Maxime C Cohen, Ngai-Hang Zachary Leung, Kiran Panchamgam, Georgia Perakis, and Anthony Smith. The impact of linear optimization promotion planning. Operations Research, 65(2): 446 468, 2017.
Kris Johnson Ferreira, Bin Hong Alex Lee, and David Simchi-Levi. Analytics for an online retailer: Demand forecasting and price optimization. Manufacturing & Service Operations Management, 18(1):69-88, 2016.
Cheng Guo and Felix Berkhahn. Entity embeddings of categorical variables. arXiv preprint 16410:1604.066777 2016.
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wil- son. Averaging weights leads to wider optima and better generalization. arXiv preprint arXiv:1803.05407, 2018.
Colin Lea, Rene Vidal, Austin Reiter, and Gregory D Hager. Temporal convolutional networks: A unified approach to action segmentation. In European Conference on Computer Vision, pp- 47-54. Springer, 2016.
Zachary C Lipton, Charles Elkan, and Balakrishnan Naryanaswamy, Optimal thresholding of clas- sifiers to maximize fl measure. In Joint European Conference on Machine Learning and Knowl- edge Discovery Databases, pp. 225-239. Springer, 2014.
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. Automatic differentiation in pytorch. 2017.
Leslie N Smith. Cyclical learning rates for training neural networks. In 2017 IEEE Winter Confer- on Applications of Computer Vision (WACV), pp. 464 472. IEEE, 2017.
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Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson. How transferable are features in deep neural networks? In Advances in neural information processing systems, pp. 3320-3328, 2014.
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A APPENDIX
Figure 7: Data Schema
Campaign Data
campeign_ ld
campaign_L id campaign_tyy end date
Item Data
Train
Item_ _id
brand
campaign_ id coupon ld
coupon_id
brand_typ
category
selling_price
customer id
redemption_stane status
Coupon Item Mapping
ilem_id _d
coupon _in
Item id
customer_jt jd
customer_ id
Customer Demographics
Customer Transaction Data
customerr_t id
age range
marital_status
customer_id
customer id
rented
itemm
quantity
family_size children
other discount
Income_braeee bracket
coupon
coupon_discoont discount
10


Reviews:
[{"review_id": "pdrFMuLKyHg", "reviewer": "AnonReviewer1", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "##########################################################################\n\nSummary:\n- This work proposed a framework to optimize and personalize deal-based promotion strategy. The proposed framework is composed of a Temporal Convolutional Network to predict purchase probability, a function to approximate price elasticity, a constraint based utility function to optimize promotion strategy. The proposed method is tested on a public dataset.\n\n##########################################################################\n\nStrength:\n- This work is trying to address a real-world problem and has posed a reasonable framing\n- Indeed all three components of this framework are important research areas and the overall problem is also a critical application scenario\n- The overall presentation is easy to follow\n\n##########################################################################\n\nWeakness:\n- Limited technical novelty of the proposed methods\n- The overall execution needs significant improvements\n- A lot of related work is missing\n\n##########################################################################\n\nDetailed Comments:\n\nDespite the importance of the problem, unfortunately the overall technical novelty and execution of this work does not reach the standard of an ICLR research paper - therefore I vote for a clear *rejection*.\n\n- From the methodology perspective, as mentioned in the previous comments, all three components in this problem framing are important research areas - while the proposed framework in this paper is more like a combination of three existing methods. In this regard, I didn't find significant technical contributions out of this.\n\n- Following this, a lot of previous work in the abovementioned three areas, especially the first two areas need to be acknowledged in the related work. To name a few (some work I'm aware of but not limited to)\n - a) Modeling purchase probability with price sensitivity\n [1] \"Modeling Consumer Preferences and Price Sensitivities from Large-Scale Grocery Shopping Transaction Logs\", WWW'17\n [2] \"Shopper: A probabilistic model of consumer choice with substitutes and complements\", Annals of Applied Statistics (2017)\n [3] \"Price-aware Recommendation with Graph Convolutional Networks\", ICDE'20\nand potentially many papers in the recommender system area leveraging the Temporal Convolutional Network\n - b) Causal inference for price elasitisty (unbiased characterization between demand and price)\n [4] \"Estimation and Inference about Heterogeneous Treatment Effects in High-Dimensional Dynamic Panels\" Semenova, Goldman, Chernozhukov, Taddy (2017)\nAdditional work from economics and marketing community can also be considered.\n\n- Unfortunately the execution of this work needs many further improvements. The most critical problem is the lack of rigorous, quantitative evaluation of the proposed method. Current experiment execution is more like a data analysis on a public dataset - without evaluating against baselines (for example, some of the previous mentioned papers), it is very difficult to justify the effectiveness of the proposed method.\n\nOverall I'd suggest the authors carefully conduct literature reviews in this domain, improve the execution and evaluation, and possibly consider an application-driven venue (e.g. an ecommerce workshop etc.) for this work.\n\n##########################################################################\n\nTypos:\n- Period is missing at the end of Related work\n- Left quotation marks are not correctly rendered (e.g. in Section 3.1, consider using ` in latex instead of ')\n", "extra_scores": {}, "extra_text": {}}, {"review_id": "mNZUxZC8Ue", "reviewer": "AnonReviewer2", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "This paper uses TCN structure and try to understand consumer purchase pattern for personalized marketing strategies. The corresponding optimization optimizes the purchase probability. However, there are several concerns.\n\n1, The motivation is not clear. The contribution of the proposed model doesn't support personalization capacity. There is no definition of personalization in the promotion settings. I didn't understand how you involve the personalization in either eq1 or section 3.3-2.4. \n\n2, The problem formulation is not clear, the goal of the designed model is targeted at max Revenue or supporting the personalization or max F1? If both, how to evaluate and compare that the proposed method is able to provide more personalized results?\n\n3, Some conceptions and statements are not clear, what is consumer purchase pattern? what is personalized market strategies? how to formulate these conceptions?\n\n4, why you choose BCE loss function instead of Hinge loss function?\n\n5, It seems the sensitivity of change in offer with purchase probability you provided in figure 4 has take a dominate role in maximizing the revenue. In order to consider the personalization, does it associate with different consumers because of potential different purchase patterns even within same category?\n\n\n", "extra_scores": {}, "extra_text": {}}, {"review_id": "FlEQFBbgxM3", "reviewer": "AnonReviewer3", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "This paper is about optimizing discount offers to individual customers to maximize business value. A temporal convolutional network (TCN) is used to model the customer's purchase probability. This network is then used to estimate the customer's offer-elasticity. Finally a linear program is used to optimize offers across all customers subject to a retention-rate constraint. The method is demonstrated on a public dataset and a detailed analysis is performed on its results.\n\nStrengths of paper:\n+ The application of the paper, to optimize personalized offers, has an important real-world business impact.\n+ To my knowledge, their framework to use customer modeling to identify offer elasticity is novel.\n+ The methods are described in much detail which will help in replication.\n\nWeaknesses of paper:\n- The approach is not compared against baseline methods to demonstrate superior performance.\n- The paper has limited novelty as the authors use known techniques such as TCN and linear programming. \n- The writing can be improved. More detail in comments.\n\nOverall this technique seems to be a good example of how to solve a real-world problem with standard machine learning and optimization tools. However, it needs to be compared against other baselines to really demonstrate value. It would also help to put this technique in greater context to prior work. The \"related works\" section is quite brief and only cites three other works.\n\nComments:\n1. Notation is overloaded so that the symbol may have different meanings in different sections of the paper. For example, d is both a dilation factor and a date-time feature. \n2. It would also be more standard to signify variables by using an appropriate font rather than quotes. ($d$ vs 'd')\n3. On page 3, it says \"as can be seen in 1:\". Is this a broken latex reference?\n4. Some mis-spelled words -- \"seperate\", \"denaotes\", \"percetage\"\n5. I am unfamiliar with retention rate cutoffs. This could use more explanation.\n6. In figure 4, it would be better to use the same y-axis across graphs for easier comparison. As is, they all look to have similar offer elasticity.\n7. Why did you choose to optimize for F-1 rather than some other metric?\n8. This is a nitpick, but some of your features are \"numeric\" rather than \"continuous\" since they can only take on integer values.\n\n", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: Reviews are somewhat mixed, but all are below the acceptance threshold. Reviewers praise the overall application and the presentation (though there is some variance in response to this aspect), but have concerns about lack of certain comparisons and technical novelty.
Decision: Reject