Time To Shop For Valentine’s Day: Shopping Occasions And .

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

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

In E-commerceOften, these occasion-based purchases deviatepreferences and are not related to recent actions.fromlong-term

Idea: Let’s incorporate occasion signals! Recommend more time or season-aware candidates, which mayalleviate the cold-start problem. Reduce the noise in modeling users' intrinsic preferences. Recommend relevant items to the user for upcoming reoccurringoccasions.

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 reoccurringoccasions (festivals, holidays, seasonal activities).

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 relevantpurchase. traceable patterns in personal occasion signals.Time Gap between Purchasesfor Wedding and Anniversarywithin a year. More than 50% ofpurchases for anniversary arenear the date of weddingpurchase within a time windowless than 30 days.

Our Proposal:Occasion-Aware Recommendation (OAR)Intrinsic Preference Personal Occasion Signals Global Occasion Signals

Occasion-Aware Recommendation (OAR) Model the repeated personal occasion signals with attention layers. Model the global occasion signals by memorizing the temporal trendsof shopping behaviors. With a gating component, we balance the global and local effects ofdifferent occasions. Experiments on Etsy and Amazon.

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Intrinsic Preference ModelingModel users' dynamic intrinsic preferences based on the correlationbetween the most recent purchase and the personal historic purchases. Query embedding of current item Keys, Values sequence of previous itemsShopping History IntrinsicPreferenceModelingItem EmbeddingPositionalEmbedding Self-AttentionRef: Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In ICDM.QueryIntrinsic Preference

Personal Occasion ElicitationElicit personal occasion signals by tracing the user's previous shoppingbehavior in the neighboring days. Query timestamp of interest (Key, Value) timestamp embedding of previous purchase,corresponding item EmbeddingItem Embedding Shopping HistoryPersonal OccasionNextShopping

Global Occasion MemorizationMemorize the shopping behavior of the crowd under different globaloccasions. Query current timestamp embedding (Key, Value) (timestamp embedding, memory slot embedding)Global TableGlobalOccasionMemorizationMemory Slots Time KeyEmbedding Global OccasionQueryNextShopping

Gating LayerUtilize an attention (gating) layer to balance a user's intrinsic preferenceswith different occasion signals for personalization.Gating LayerGlobal OccasionPersonal Occasion QueryNextShoppingShopping HistoryPredictedPreferenceUser EmbeddingIntrinsic PreferenceGated Outputx

Experiment: DataEtsy: November 2006 to December 2018Amazon: May 1996 to July 2014Dataset# Users# Items# 1

Experiment: Metrics Normalized Discounted Cumulative Gain ([email protected]) Hit Rate ([email protected]) 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 ItemRecommendation HPMN: Lifelong Sequential Modeling with Hierarchical PeriodicMemory Network SARec: Self-attentive sequential recommendation

OAR vs BaselinesOAR achieves the best performance on both Etsy and AmazonEtsyAmazonModelNDCGHRNDCGHRMRRMRRK 5K 10K 5K 10K 5K 10K 5K 8930.2650GRU4Rec 2780.54330.30090.33850.40850.52510.2984OAR0.6078* 0.6415* 0.7425* 0.8462* 0.5847* 0.3200* 0.3580* 0.4301* 0.5476* 0.3165*e 2: Comparison of Di erent Models. indicates that the improvement of the best result is statistically signi cant cd with second best result with p 0.01.6% 8% improvement

470.71020.82780.54330Impact of eachOARcomponent?0.6078* 0.6415* 0.7425* 0.8462* 0.5847* 0Table occasion2: Comparisonof Di erentModels. indicatesthatthe improveThe globalmemorizationcomponentperformsbetterthanpared with second best result with p 0.01.recommending general most popular [email protected]@5MRRGlobal (G)0.18160.19530.22380.2294Intrinsic (I)0.56650.54330.30090.2984Personal (P)0.57910.55820.30690.3047I G0.58850.56420.30990.3063I P0.59160.56770.31360.3108Remove .3165Table 3: Ablation Test Results.Model

470.71020.82780.54330Impact of eachOARcomponent?0.6078* 0.6415* 0.7425* 0.8462* 0.5847* 0Table2: Comparisonof Di erentModels. indicatesthe improvePersonaloccasioncomponentworks betterthan theintrinsic thatpreferencepared with second best result with p [email protected]@5MRRGlobal (G)0.18160.19530.22380.2294Intrinsic (I)0.56650.54330.30090.2984Personal (P)0.57910.55820.30690.3047I G0.58850.56420.30990.3063I P0.59160.56770.31360.3108Remove .3165Table 3: Ablation Test Results.Model

470.71020.82780.54330Impact of eachOARcomponent?0.6078* 0.6415* 0.7425* 0.8462* 0.5847* 0TableComparisonof simpleDi erentModels. indicatesthat theimproveReplacethe2:Gatinglayer withaddition Theperformancedropspared with second best result with p [email protected]@5MRRGlobal (G)0.18160.19530.22380.2294Intrinsic (I)0.56650.54330.30090.2984Personal (P)0.57910.55820.30690.3047I G0.58850.56420.30990.3063I P0.59160.56770.31360.3108Remove .3165Table 3: Ablation Test Results.Model

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 thetemporal trends of shopping behaviors Experiments on Etsy and Amazon show the positive impact ofincorporating occasion signals Next

Thank you!

There 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