# Client Time Series Model: a Multi-Target Recommender System based on Temporally-Masked Encoders

DevFeed: [Client Time Series Model: a Multi-Target Recommender System based on Temporally-Masked Encoders](<https://devfeed.tech/articles/client-time-series-model-a-multi-target-recommender-system-based-on-temporally-masked-encoders-29339.md>)

Original publisher: [Read original article](<https://multithreaded.stitchfix.com/blog/2022/10/14/client-time-series-model/>)

Published: 2022-10-14T06:00:00Z

Content type: article

Language: en

Sources: [Stitch Fix](<https://devfeed.tech/sources/stitch-fix.md>)

Topics: [client](<https://devfeed.tech/topics/client.md>), [Time Series](<https://devfeed.tech/topics/time-series.md>), [recommendations](<https://devfeed.tech/topics/recommendations.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [model architecture](<https://devfeed.tech/topics/model-architecture.md>), [maintenance](<https://devfeed.tech/topics/maintenance.md>), [systems](<https://devfeed.tech/topics/systems.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [data](<https://devfeed.tech/tags/data.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [maintenance](<https://devfeed.tech/tags/maintenance.md>), [model-architecture](<https://devfeed.tech/tags/model-architecture.md>), [recommendations](<https://devfeed.tech/tags/recommendations.md>), [systems](<https://devfeed.tech/tags/systems.md>), [time-series](<https://devfeed.tech/tags/time-series.md>)

## AI overview

Stitch Fix describes its Client Time Series Model, a sequence-based recommender that estimates the probability of client-item purchases. The model uses a unified client embedding and incorporates the time dimension of client interactions to reduce duplicated models, improve maintainability, and share learning across business lines, regions, and channels.

## Source excerpt

Introduction The foundation of our recommendation stack is a scoring model we call p(sale), which estimates the probability that any given client will purchase any given item. This model has gone through many iterations over the years, from a mixed effects model, to a matrix factorization model, and now to a novel sequence-based model. Internally we call this the Client Time Series Model (aka CTSM) because of its focus on the time-domain of client interactions. This post details our new model, which is a significant improvement for both the quality of our recommendations and the maintainability of our systems. Motivation Before setting out to develop our new model, it was clear that the evolution of our business necessitated a change to our modeling approach. First, the growing variety of recommendations we serve led to an explosion in the number of models we needed to maintain. Each time the business expanded, such as adding Mens, or serving the UK, or adding direct shopping with Freestyle, we responded by forking a new model to serve the new channel. This was necessary because a single domain-agnostic model could not serve the new channels as well as tailored models, but over time it has increased our maintenance burden and cost of iteration. In addition to the system complexity, we also knew we had an opportunity to make better use of important signals. With data and models separated by business line, region, and channel, we had a limited ability to leverage learning across these boundaries. With a unified model, we can more seamlessly use data from US clients to improve recommendations for UK clients, or data from Fixes to improve recommendations in Freestyle. Finally, our previous approaches modeled clients via tabular data. Although they are trained on purchase events that take place in the context of a particular point in time, they did not explicitly consider the time dimension in their understanding of the client's interactions. We believed that there was s