Time to Shop for Valentine’s Day: Shopping Occasions and Sequential

Recommendation in E-commerceJianling Wang, Raphael Louca*, Diane Hu*, Caitlin Cellier*,

James Caverlee and Liangjie Hong*Texas A&M University

* Etsy Inc.

Recommender Systems usually:

● Capture users' intrinsic preference from their long-term behaviorpatterns.

● Infer users’ current needs by emphasizing recent actions.

However, intrinsic user behavior may be shifted by occasions, such asbirthdays, anniversaries, or gifting celebrations (Valentine's Day orMother's Day).

In E-commerce

In E-commerce

Often, these occasion-based purchases deviate from long-termpreferences and are not related to recent actions.

Idea: Let’s incorporate occasion signals!● Recommend more time or season-aware candidates, which may

alleviate the cold-start problem.● Reduce the noise in modeling users' intrinsic preferences.● Recommend relevant items to the user for upcoming reoccurring

occasions.

Global Occasions● Happen at the same time for a large number of users.● Encourage or lead to similar shopping decisions for crowds of users.● Examples: Christmas, Valentine’s Day, …

Personal Occasions● Happen at different times for different users.● Occur in a periodic and repeated pattern for a specific user.● Examples: Birthdays (for you or your friends) and anniversaries.

To Illustrate: Temporal Shopping TrendsUsers' shopping preferences are dynamic and can reflect reoccurring occasions (festivals, holidays, seasonal activities).

Valentine’s Day

Mother’s Day

Father’s Day

Thanksgiving Christmas

(a) (b)

Time-sensitive Most Popular V.S. General Most Popular

To Illustrate: From Personal PerspectiveThere are occasions that may re-occur within a certain period (e.g. annually or monthly) over the course of multiple years and trigger relevant purchase.

⇒ ⇒ traceable patterns in personal occasion signals.

Time Gap between Purchases for Wedding and Anniversary within a year. More than 50% of purchases for anniversary are near the date of wedding purchase within a time window less than 30 days.

Occasion-Aware Recommendation (OAR)Intrinsic Preference +

Personal Occasion Signals +

Global Occasion Signals

Our Proposal:

Occasion-Aware Recommendation (OAR)● Model the repeated personal occasion signals with attention layers.● Model the global occasion signals by memorizing the temporal trends

of shopping behaviors.● With a gating component, we balance the global and local effects of

different occasions. ● Experiments on Etsy and Amazon.

Attention Mechanisms● Query

● Key-Value Pairs

● Scoring Function - Calculating similarity/relationship between a Query & a Key

Keys ValuesQueries… ……

Attention Weights Outputs

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Softm

ax

Intrinsic Preference ModelingModel users' dynamic intrinsic preferences based on the correlation between the most recent purchase and the personal historic purchases.

● Query = embedding of current item

● Keys, Values = sequence of previous items

Ref: Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In ICDM.

…

+ + +Item Embedding

Positional Embedding

+ … Query

Self-Attention

Intrinsic Preference Modeling

Shopping History

Intrinsic Preference

Personal Occasion ElicitationElicit personal occasion signals by tracing the user's previous shopping behavior in the neighboring days.

● Query = timestamp of interest

● (Key, Value) = timestamp embedding of previous purchase, corresponding item embedding

…

Item Embedding …

Timestamp Embedding

…

Query

Personal Occasion Next Shopping

Personal Occasion Elicitation

Shopping History

Global Occasion MemorizationMemorize the shopping behavior of the crowd under different global occasions.

● Query = current timestamp embedding

● (Key, Value) = (timestamp embedding, memory slot embedding)

Time Key Embedding

Memory Slots

Query

Next Shopping

…

… Global OccasionGlobal Occasion

Memorization

Global Table

Gating LayerUtilize an attention (gating) layer to balance a user's intrinsic preferences with different occasion signals for personalization.

…

Personal Occasion Next Shopping

Query

x

Predicted Preference

Global Occasion

Gated Output

User EmbeddingShopping History

Gating Layer

Intrinsic Preference

Experiment: Data

Etsy: November 2006 to December 2018

Amazon: May 1996 to July 2014

Dataset # Users # Items # Purchases Density Cutting Time

Etsy 118,668 80,214 5.3M 0.0561% 2018/1/1Amazon 84,191 100,946 1.0M 0.0124% 2013/8/1

Experiment: Metrics

● Normalized Discounted Cumulative Gain (NDCG@K)● Hit Rate (HR@K)● Mean Reciprocal Rank (MRR)

Experiment: Baselines

● Most Popular● MF-BPR: Matrix Factorization with Bayesian Personalized Ranking● Fossil: Fusing Similarity Models with Markov Chains● GRU4Rec+: Recurrent Neural Networks with Top-k Gains● TCN: A Simple Convolutional Generative Network for Next Item

Recommendation● HPMN: Lifelong Sequential Modeling with Hierarchical Periodic

Memory Network● SARec: Self-attentive sequential recommendation

ModelEtsy Amazon

NDCG HR MRR NDCG HR MRRK=5 K=10 K=5 K=10 K=5 K=10 K=5 K=10MP 0.1531 0.1919 0.2304 0.3511 0.1673 0.2129 0.2509 0.3020 0.4199 0.2195

MF-BPR 0.4519 0.5001 0.5947 0.7434 0.4376 0.2663 0.3012 0.3619 0.4698 0.2668Fossil 0.4946 0.5354 0.5511 0.7630 0.4746 0.2160 0.2483 0.2967 0.3969 0.2221TCN 0.5199 0.5726 0.6698 0.8059 0.5090 0.2632 0.3029 0.3664 0.4893 0.2650

GRU4Rec+ 0.5346 0.5771 0.6830 0.8136 0.5126 0.2763 0.3169 0.3828 0.5087 0.2770HPMN 0.5480 0.5883 0.6962 0.8201 0.5245 0.2820 0.3216 0.3881 0.5109 0.2819SARec 0.5665 0.6047 0.7102 0.8278 0.5433 0.3009 0.3385 0.4085 0.5251 0.2984OAR 0.6078* 0.6415* 0.7425* 0.8462* 0.5847* 0.3200* 0.3580* 0.4301* 0.5476* 0.3165*

Table 2: Comparison of Di�erent Models. ⇤ indicates that the improvement of the best result is statistically signi�cant com-pared with second best result with p < 0.01.

Model Etsy AmazonNDCG@5 MRR NDCG@5 MRR

Global (G) 0.1816 0.1953 0.2238 0.2294Intrinsic (I) 0.5665 0.5433 0.3009 0.2984Personal (P) 0.5791 0.5582 0.3069 0.3047

I + G 0.5885 0.5642 0.3099 0.3063I + P 0.5916 0.5677 0.3136 0.3108

Remove Gate 0.5859 0.5618 0.3074 0.3039OAR 0.6078 0.5847 0.3200 0.3165

Table 3: Ablation Test Results.

methods is over {Adam, Adagrad, SGD}. We also �ne-tune all themodel-speci�c hyperparameters and report the best performancein the following sections.

4.3 Model ComparisonWe summarize the best performance of all the baseline models andthe proposed model in Table 2. We can see that OAR achieves thebest performance under di�erent metrics in both datasets. It gains7.62% and 6.06% MRR improvement in Etsy and Amazon comparedwith the state-of-the-art.

Compared with the basic general MP, we can see that MF-BPRwhich represents users and items with static latent factors canachieve a 177.43% and 39.67% improvement on average in Etsy andAmazon. Then by introducing the Markov Chains to capture thetransition of users among di�erent items, we �nd that Fossil worksbetter than MF in Etsy but performs worse in Amazon. Presumablyit is because the Amazon data is extremely sparse and results in anunstable factorized Markov Chains component in Fossil.

Comparing the recent neural-based sequential models, we �ndthat GRU4Rec+ works slightly better than TCN, which is based ondilated CNN. And HPMN utilizing hierarchical multi-layer memorynetworks outperforms GRU4Rec+ in both data, which proves thatthere are periodic pattern in users’ shopping behaviors. However,HPMN model assumes that the period of shopping behavior isconstant for all the users along the time and thus lack of �exibilityto handle the real-world scenarios. We �nd that SARec, which isutilizing self-attention to model users’ intrinsic preference, workseven better than HPMN. This shows that attention mechanismsare a good �t for modeling sequential behaviors. And by carefullyeliciting the occasion signals and combining them with the intrinsic

Figure 7: Similarity between di�erent calendar days.

preferences, OAR achieves the best performance in the next-itemprediction via an accurate user model.

4.4 Evaluation of OARTo examine whether each component in OAR achieves its goaland to understand how it contributes to the recommendation, weanalyze their impacts with an ablation test (in Table 3).

The Global occasion component (G), in which we set up a certainnumber of memory slots to record the crowd behavior in di�erentoccasions, does not provide personalized recommendation individ-ually. It can outperform the general Most Popular (MP) model by17.17% and 4.89% in Etsy and Amazon, which demonstrates thatit can capture the temporal global occasion signals hidden in thecrowd behavior. Additionally, we can infer that the users in Etsyare more likely to follow the temporal global trends in shopping.Both Intrinsic and Personal components can provide personalizednext-item recommendation. In Intrinsic (I), it maps the most re-cent purchase to the items purchased before to infer the “relevant”items in the future. While in Personal (P), the main idea is to traceback to the previous behaviors in the related time periods. We cansee that P performs slightly better than I, which means that in e-commerce, it is important to predict the shopping occasion and paymore attention to the items purchased around similar occasionswhile inferring the next purchase. While combining the I and Gor I and P, we can see the joint models can improve each of theindividual components. Thus we �nd that in e-commerce platforms,

OAR achieves the best performance on both Etsy and Amazon

OAR vs Baselines

6%~8% improvement

ModelEtsy Amazon

NDCG HR MRR NDCG HR MRRK=5 K=10 K=5 K=10 K=5 K=10 K=5 K=10MP 0.1531 0.1919 0.2304 0.3511 0.1673 0.2129 0.2509 0.3020 0.4199 0.2195

MF-BPR 0.4519 0.5001 0.5947 0.7434 0.4376 0.2663 0.3012 0.3619 0.4698 0.2668Fossil 0.4946 0.5354 0.5511 0.7630 0.4746 0.2160 0.2483 0.2967 0.3969 0.2221TCN 0.5199 0.5726 0.6698 0.8059 0.5090 0.2632 0.3029 0.3664 0.4893 0.2650

GRU4Rec+ 0.5346 0.5771 0.6830 0.8136 0.5126 0.2763 0.3169 0.3828 0.5087 0.2770HPMN 0.5480 0.5883 0.6962 0.8201 0.5245 0.2820 0.3216 0.3881 0.5109 0.2819SARec 0.5665 0.6047 0.7102 0.8278 0.5433 0.3009 0.3385 0.4085 0.5251 0.2984OAR 0.6078* 0.6415* 0.7425* 0.8462* 0.5847* 0.3200* 0.3580* 0.4301* 0.5476* 0.3165*

Table 2: Comparison of Di�erent Models. ⇤ indicates that the improvement of the best result is statistically signi�cant com-pared with second best result with p < 0.01.

Model Etsy AmazonNDCG@5 MRR NDCG@5 MRR

Global (G) 0.1816 0.1953 0.2238 0.2294Intrinsic (I) 0.5665 0.5433 0.3009 0.2984Personal (P) 0.5791 0.5582 0.3069 0.3047

I + G 0.5885 0.5642 0.3099 0.3063I + P 0.5916 0.5677 0.3136 0.3108

Remove Gate 0.5859 0.5618 0.3074 0.3039OAR 0.6078 0.5847 0.3200 0.3165

Table 3: Ablation Test Results.

methods is over {Adam, Adagrad, SGD}. We also �ne-tune all themodel-speci�c hyperparameters and report the best performancein the following sections.

4.3 Model ComparisonWe summarize the best performance of all the baseline models andthe proposed model in Table 2. We can see that OAR achieves thebest performance under di�erent metrics in both datasets. It gains7.62% and 6.06% MRR improvement in Etsy and Amazon comparedwith the state-of-the-art.

Compared with the basic general MP, we can see that MF-BPRwhich represents users and items with static latent factors canachieve a 177.43% and 39.67% improvement on average in Etsy andAmazon. Then by introducing the Markov Chains to capture thetransition of users among di�erent items, we �nd that Fossil worksbetter than MF in Etsy but performs worse in Amazon. Presumablyit is because the Amazon data is extremely sparse and results in anunstable factorized Markov Chains component in Fossil.

Comparing the recent neural-based sequential models, we �ndthat GRU4Rec+ works slightly better than TCN, which is based ondilated CNN. And HPMN utilizing hierarchical multi-layer memorynetworks outperforms GRU4Rec+ in both data, which proves thatthere are periodic pattern in users’ shopping behaviors. However,HPMN model assumes that the period of shopping behavior isconstant for all the users along the time and thus lack of �exibilityto handle the real-world scenarios. We �nd that SARec, which isutilizing self-attention to model users’ intrinsic preference, workseven better than HPMN. This shows that attention mechanismsare a good �t for modeling sequential behaviors. And by carefullyeliciting the occasion signals and combining them with the intrinsic

Figure 7: Similarity between di�erent calendar days.

preferences, OAR achieves the best performance in the next-itemprediction via an accurate user model.

4.4 Evaluation of OARTo examine whether each component in OAR achieves its goaland to understand how it contributes to the recommendation, weanalyze their impacts with an ablation test (in Table 3).

The Global occasion component (G), in which we set up a certainnumber of memory slots to record the crowd behavior in di�erentoccasions, does not provide personalized recommendation individ-ually. It can outperform the general Most Popular (MP) model by17.17% and 4.89% in Etsy and Amazon, which demonstrates thatit can capture the temporal global occasion signals hidden in thecrowd behavior. Additionally, we can infer that the users in Etsyare more likely to follow the temporal global trends in shopping.Both Intrinsic and Personal components can provide personalizednext-item recommendation. In Intrinsic (I), it maps the most re-cent purchase to the items purchased before to infer the “relevant”items in the future. While in Personal (P), the main idea is to traceback to the previous behaviors in the related time periods. We cansee that P performs slightly better than I, which means that in e-commerce, it is important to predict the shopping occasion and paymore attention to the items purchased around similar occasionswhile inferring the next purchase. While combining the I and Gor I and P, we can see the joint models can improve each of theindividual components. Thus we �nd that in e-commerce platforms,

Impact of each component?The global occasion memorization component performs better than recommending general most popular items.

Impact of each component?Personal occasion component works better than the intrinsic preference component.

ModelEtsy Amazon

NDCG HR MRR NDCG HR MRRK=5 K=10 K=5 K=10 K=5 K=10 K=5 K=10MP 0.1531 0.1919 0.2304 0.3511 0.1673 0.2129 0.2509 0.3020 0.4199 0.2195

MF-BPR 0.4519 0.5001 0.5947 0.7434 0.4376 0.2663 0.3012 0.3619 0.4698 0.2668Fossil 0.4946 0.5354 0.5511 0.7630 0.4746 0.2160 0.2483 0.2967 0.3969 0.2221TCN 0.5199 0.5726 0.6698 0.8059 0.5090 0.2632 0.3029 0.3664 0.4893 0.2650

GRU4Rec+ 0.5346 0.5771 0.6830 0.8136 0.5126 0.2763 0.3169 0.3828 0.5087 0.2770HPMN 0.5480 0.5883 0.6962 0.8201 0.5245 0.2820 0.3216 0.3881 0.5109 0.2819SARec 0.5665 0.6047 0.7102 0.8278 0.5433 0.3009 0.3385 0.4085 0.5251 0.2984OAR 0.6078* 0.6415* 0.7425* 0.8462* 0.5847* 0.3200* 0.3580* 0.4301* 0.5476* 0.3165*

Table 2: Comparison of Di�erent Models. ⇤ indicates that the improvement of the best result is statistically signi�cant com-pared with second best result with p < 0.01.

Model Etsy AmazonNDCG@5 MRR NDCG@5 MRR

Global (G) 0.1816 0.1953 0.2238 0.2294Intrinsic (I) 0.5665 0.5433 0.3009 0.2984Personal (P) 0.5791 0.5582 0.3069 0.3047

I + G 0.5885 0.5642 0.3099 0.3063I + P 0.5916 0.5677 0.3136 0.3108

Remove Gate 0.5859 0.5618 0.3074 0.3039OAR 0.6078 0.5847 0.3200 0.3165

Table 3: Ablation Test Results.

methods is over {Adam, Adagrad, SGD}. We also �ne-tune all themodel-speci�c hyperparameters and report the best performancein the following sections.

4.3 Model ComparisonWe summarize the best performance of all the baseline models andthe proposed model in Table 2. We can see that OAR achieves thebest performance under di�erent metrics in both datasets. It gains7.62% and 6.06% MRR improvement in Etsy and Amazon comparedwith the state-of-the-art.

Compared with the basic general MP, we can see that MF-BPRwhich represents users and items with static latent factors canachieve a 177.43% and 39.67% improvement on average in Etsy andAmazon. Then by introducing the Markov Chains to capture thetransition of users among di�erent items, we �nd that Fossil worksbetter than MF in Etsy but performs worse in Amazon. Presumablyit is because the Amazon data is extremely sparse and results in anunstable factorized Markov Chains component in Fossil.

Comparing the recent neural-based sequential models, we �ndthat GRU4Rec+ works slightly better than TCN, which is based ondilated CNN. And HPMN utilizing hierarchical multi-layer memorynetworks outperforms GRU4Rec+ in both data, which proves thatthere are periodic pattern in users’ shopping behaviors. However,HPMN model assumes that the period of shopping behavior isconstant for all the users along the time and thus lack of �exibilityto handle the real-world scenarios. We �nd that SARec, which isutilizing self-attention to model users’ intrinsic preference, workseven better than HPMN. This shows that attention mechanismsare a good �t for modeling sequential behaviors. And by carefullyeliciting the occasion signals and combining them with the intrinsic

Figure 7: Similarity between di�erent calendar days.

preferences, OAR achieves the best performance in the next-itemprediction via an accurate user model.

4.4 Evaluation of OARTo examine whether each component in OAR achieves its goaland to understand how it contributes to the recommendation, weanalyze their impacts with an ablation test (in Table 3).

The Global occasion component (G), in which we set up a certainnumber of memory slots to record the crowd behavior in di�erentoccasions, does not provide personalized recommendation individ-ually. It can outperform the general Most Popular (MP) model by17.17% and 4.89% in Etsy and Amazon, which demonstrates thatit can capture the temporal global occasion signals hidden in thecrowd behavior. Additionally, we can infer that the users in Etsyare more likely to follow the temporal global trends in shopping.Both Intrinsic and Personal components can provide personalizednext-item recommendation. In Intrinsic (I), it maps the most re-cent purchase to the items purchased before to infer the “relevant”items in the future. While in Personal (P), the main idea is to traceback to the previous behaviors in the related time periods. We cansee that P performs slightly better than I, which means that in e-commerce, it is important to predict the shopping occasion and paymore attention to the items purchased around similar occasionswhile inferring the next purchase. While combining the I and Gor I and P, we can see the joint models can improve each of theindividual components. Thus we �nd that in e-commerce platforms,

Impact of each component?Replace the Gating layer with simple addition ⇒ The performance drops

ModelEtsy Amazon

NDCG HR MRR NDCG HR MRRK=5 K=10 K=5 K=10 K=5 K=10 K=5 K=10MP 0.1531 0.1919 0.2304 0.3511 0.1673 0.2129 0.2509 0.3020 0.4199 0.2195

MF-BPR 0.4519 0.5001 0.5947 0.7434 0.4376 0.2663 0.3012 0.3619 0.4698 0.2668Fossil 0.4946 0.5354 0.5511 0.7630 0.4746 0.2160 0.2483 0.2967 0.3969 0.2221TCN 0.5199 0.5726 0.6698 0.8059 0.5090 0.2632 0.3029 0.3664 0.4893 0.2650

GRU4Rec+ 0.5346 0.5771 0.6830 0.8136 0.5126 0.2763 0.3169 0.3828 0.5087 0.2770HPMN 0.5480 0.5883 0.6962 0.8201 0.5245 0.2820 0.3216 0.3881 0.5109 0.2819SARec 0.5665 0.6047 0.7102 0.8278 0.5433 0.3009 0.3385 0.4085 0.5251 0.2984OAR 0.6078* 0.6415* 0.7425* 0.8462* 0.5847* 0.3200* 0.3580* 0.4301* 0.5476* 0.3165*

Table 2: Comparison of Di�erent Models. ⇤ indicates that the improvement of the best result is statistically signi�cant com-pared with second best result with p < 0.01.

Model Etsy AmazonNDCG@5 MRR NDCG@5 MRR

Global (G) 0.1816 0.1953 0.2238 0.2294Intrinsic (I) 0.5665 0.5433 0.3009 0.2984Personal (P) 0.5791 0.5582 0.3069 0.3047

I + G 0.5885 0.5642 0.3099 0.3063I + P 0.5916 0.5677 0.3136 0.3108

Remove Gate 0.5859 0.5618 0.3074 0.3039OAR 0.6078 0.5847 0.3200 0.3165

Table 3: Ablation Test Results.

methods is over {Adam, Adagrad, SGD}. We also �ne-tune all themodel-speci�c hyperparameters and report the best performancein the following sections.

4.3 Model ComparisonWe summarize the best performance of all the baseline models andthe proposed model in Table 2. We can see that OAR achieves thebest performance under di�erent metrics in both datasets. It gains7.62% and 6.06% MRR improvement in Etsy and Amazon comparedwith the state-of-the-art.

Compared with the basic general MP, we can see that MF-BPRwhich represents users and items with static latent factors canachieve a 177.43% and 39.67% improvement on average in Etsy andAmazon. Then by introducing the Markov Chains to capture thetransition of users among di�erent items, we �nd that Fossil worksbetter than MF in Etsy but performs worse in Amazon. Presumablyit is because the Amazon data is extremely sparse and results in anunstable factorized Markov Chains component in Fossil.

Comparing the recent neural-based sequential models, we �ndthat GRU4Rec+ works slightly better than TCN, which is based ondilated CNN. And HPMN utilizing hierarchical multi-layer memorynetworks outperforms GRU4Rec+ in both data, which proves thatthere are periodic pattern in users’ shopping behaviors. However,HPMN model assumes that the period of shopping behavior isconstant for all the users along the time and thus lack of �exibilityto handle the real-world scenarios. We �nd that SARec, which isutilizing self-attention to model users’ intrinsic preference, workseven better than HPMN. This shows that attention mechanismsare a good �t for modeling sequential behaviors. And by carefullyeliciting the occasion signals and combining them with the intrinsic

Figure 7: Similarity between di�erent calendar days.

preferences, OAR achieves the best performance in the next-itemprediction via an accurate user model.

4.4 Evaluation of OARTo examine whether each component in OAR achieves its goaland to understand how it contributes to the recommendation, weanalyze their impacts with an ablation test (in Table 3).

The Global occasion component (G), in which we set up a certainnumber of memory slots to record the crowd behavior in di�erentoccasions, does not provide personalized recommendation individ-ually. It can outperform the general Most Popular (MP) model by17.17% and 4.89% in Etsy and Amazon, which demonstrates thatit can capture the temporal global occasion signals hidden in thecrowd behavior. Additionally, we can infer that the users in Etsyare more likely to follow the temporal global trends in shopping.Both Intrinsic and Personal components can provide personalizednext-item recommendation. In Intrinsic (I), it maps the most re-cent purchase to the items purchased before to infer the “relevant”items in the future. While in Personal (P), the main idea is to traceback to the previous behaviors in the related time periods. We cansee that P performs slightly better than I, which means that in e-commerce, it is important to predict the shopping occasion and paymore attention to the items purchased around similar occasionswhile inferring the next purchase. While combining the I and Gor I and P, we can see the joint models can improve each of theindividual components. Thus we �nd that in e-commerce platforms,

Visualization of Occasion-driven ShoppingPreferences for Occasion-related items are dynamics in a year.

Summary● Propose an occasion-aware sequential recommender● Model the repeated personal occasion signals with attention layers,

while modeling the global occasion signals by memorizing the temporal trends of shopping behaviors

● Experiments on Etsy and Amazon show the positive impact of incorporating occasion signals

● Next…

Thank you!