Future Directions and References

Table of Links

Abstract and 1 Introduction

2. Literature Review
3. Model
4. Experiments
5. Deployment Journey
6. Future Directions and References

6 FUTURE DIRECTIONS

Buy It Again recommendations help users to quickly complete their shopping missions. Traditional approaches tend to model guest personalized behavior at item granularity. In this paper, we present the case for a coarse grained model which can capture the customer behavior at item category level.

The proposed Personalized Category (PC) model combined with Items-within-Category (IC) model outperform existing BIA and NBR models on standard public datasets. The PCIC model also scales well for large retailers with millions sized product catalogs and millions of active guests. The A/B tests on the site show a significant improvement in guest shopping experience and guest spends by using the model.

In the future, we would recommend that retailers explore models that combine the insights from Personalized Category features with Personalized Item features. Moreover, we would recommend considering mutual excitation among items and categories as simultaneous consumption has some inherent relationship with repeat consumption.

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