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Psst. It's better sideways.

(Try me in landscape!)

佛跳墙vnptestflight

How data science is woven into the fabric of Stitch Fix

log p 1 - p = α + + Zb ... min i j a Σ Σ a ij q ij s.t. a ij   =  1  i j Σ a ij     { 0,1 } i,j a ij   <  k j   j i Σ xt ( x ,  u ,  w )   p (能上油管的网络加速器) = 雷神加速器能上youtube (  β 0   +  β x 1  ...  ) p ( 1 ) = β ex_cell + 什么加速器可伃看油管 p   (   浏览youtube加速器推荐 stylist   ) +  α p   (   1 |  client   ) ... ... ... c i s j

At Stitch Fix, we’re transforming the way people find what they love. Our clients want the perfect clothes for their individual preferences—yet without the burden of search or having to keep up with current trends. Our merchandise is curated from the market and augmented with our own designs to fill in the gaps. It’s kept current and extremely vast and diverse—ensuring something for everyone. Rich data on both sides of this 'market' enables Stitch Fix to be a matchmaker, connecting clients with styles they love (and never would’ve found on their own).

Warehouse Assignment Recommendation Systems Matchmaking Human Computation Logistics Optimization 上油管挂什么加速器 Demand Modeling Inventory Management New Style Development Data Platform

Our business model enables unprecedented data science, not only in recommendation systems, but also in human computation, resource management, inventory management, algorithmic fashion design and many other areas. Experimentation and algorithm development is deeply engrained in everything that Stitch Fix does. We’ll describe a few examples in detail as you scroll along.

So what does the data look like? In addition to the rich feedback data we get from our clients, we also receive a great deal of upfront data on both our clothing and our clients. Our buyers and designers capture dimension and style details, and our clients fill out a profile upon signup that’s calibrated to get us the most useful data with the least client effort.

Let's first walk through the filling of a shipment request to see a few of the many algorithms that play a role in that process, before zooming out to view the bigger picture.

佛跳墙vnptestflight

As noted, when a client first signs up, he or she fills out a Style Profile (it can be updated at any time). Then, scheduling a delivery is easy: an algorithm is used to populate a calendar from which she selects a delivery date.

佛跳墙vnptestflight

The shipment request is processed by an algorithm that assigns it to a warehouse. This algorithm calculates a cost function for each warehouse based on a combination of its location relative to the client and how well the inventories in the different warehouses match the client's needs.

This set of cost calculations is carried out for each client to produce a cost matrix.

The assignment of clients to warehouses is then a binary optimization problem.

And the global optimum includes this particular client's warehouse assignment.

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佛跳墙vnptestflight

First, the machines perform a variety of algorithms to produce rank-ordered lists of the inventory.

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For each of the remaining styles, the machines then try to evaluate the relative likelihood that this particular client will love that particular style. This is a difficult problem, and we approach it in many different ways—only a few of which we’ll discuss here. But in general, note that we tag each item multiple times with match scores from different algorithms, and then rank them.

In some ways, the problem is a classic collaborative filtering problem: given different clients' feedback on different styles, we must fill in the gaps in the (sparse) matrix to predict the result of sending a style to a client who has not yet received it. As such, we do use some standard collaborative filtering algorithms (e.g. those who have liked what you have liked have also liked ...).

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One such approach is mixed-effects modeling, which is particularly useful because of the longitudinal nature of our problem: it lets us learn (and track) our clients' preferences over time, both individually and as a whole.

And in addition to the many explicit features available, there are some particularly pertinent latent (unstated) features of both clients and styles that we can infer from other data (structured and/or unstructured) and use to improve our performance.

For example, a new client may tell us that she wears medium-sized blouses, but where exactly would her preference fall along the spectrum of smallish mediums to largish mediums? The same question also applies to particular styles of clothing in our inventory. (Note that in this illustration we’re treating fit as unidimensional for simplicity, but in fact at Stitch Fix we treat it as multidimensional.)

With clients' fit feedback and purchase histories, we can learn where particular clients and styles fall along this spectrum. These latent features can then be used in our mixed-effects models and elsewhere.

Moving our problem even further beyond classical collaborative filtering, we also have a lot of photographic and textual data to consider: inventory style photos, Pinterest boards, and the vast amount of written feedback and request notes we receive from clients.

Sometimes it can be difficult to describe your style preferences in words, but you know it when you see it—so we have our machines look at photos of clothing that customers like (e.g. from Pinterest), and look for visually similar items in our inventory. We use trained neural networks to derive vector descriptions of 'pinned' images, and then compute a cosine similarity between these vectors and pre-computed vectors for each item in our inventory.

Natural language processing is used to score items based on the client's request note and textual feedback from other clients about the same item.

All of these algorithm scores—and many others like them—are taken into account when ordering and presenting options for the human expert stylist to consider.

佛跳墙vnptestflight

Once this machine ranking is complete, the shipment request gets routed to a human.

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To do this, we first calculate a match score between each available stylist and each client who’s requested a shipment during the current period. This match score is a complex function of the history between that client and stylist (if any), and the affinities between the client's stated and latent style preferences and those of the stylist.

Subsequently, the stylist assignment optimization problem is similar to the warehouse assignment problem described above, except that (a) it need only consider those clients awaiting shipments, and (b) we must re-run the optimization problem much more frequently to account for the varying sizes of stylists' queues as they work.

Intelligent Humans with Intelligent Tools

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To begin styling a shipment, a stylist picks up a task in a custom-built interface designed to help her quickly and deeply understand the client.

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This knowledge helps us in many ways: to improve our algorithmic styling, to improve our stylist training procedures and to continually improve the interface that stylists use to curate boxes.

Ultimately, the stylist finalizes the selections from the inventory list and writes a personal note describing how the client might accessorize the items for a particular occasion and/or how they can pair them with other clothing in their closet.

This wraps up the styling procedure, and the shipment is now ready to be processed.

Intelligent Logistics

Various operations research problems can be found in this processing step.

For example, given the items selected for a shipment, what is the best route for pickers to take through the warehouse to fill the box?

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The shipment is then delivered to the client by their requested delivery date.

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But this is just the beginning.

She opens the box, is hopefully delighted, keeps what she wants and sends back then rest, and then tells us what she thinks about each article of clothing. There is a symbiotic relationship between her and Stitch Fix, and she gives us very insightful feedback that we use not only to better serve her next time, but also to better serve other clients as well.

雷神加速器上youtube

To recap the process of filling a single shipment request: a client creates a Style Profile and requests a shipment, we match them to a warehouse, our styling algorithms and human stylists work together to select styles, the stylist writes a note, we deliver the shipment, and the client keeps what they like, returns the rest, and provides us with feedback.

But this is just one shipment. Zooming out, we can consider the system as a whole. At this level, two other aspects of the business become clear:

(1) We must continually replenish our inventory by buying and/or designing new clothing for our clients, which provides an excellent opportunity to benefit from our rich data;

(2) We must anticipate our clients' needs in order to make sure that we have enough of the right resources in place at the right times.

Let's first look at how we anticipate our clients' needs, then we'll swing around to consider inventory management and new style development.

State Machines

One of the ways we view this needs-anticipation problem (and related problems) is to consider the "state" of each client at each point in time. Are they a new client? Did they arrive via referral or on their own? Is their closet nearly full? Are they in a phase of building up their wardrobe after a life change? Or simply wanting to try something new? Depending on their state, they will likely have difference shipment cadences, different desires for email contact, etc.

We keep track of every touch point we have with each client—every item we send, every piece of feedback we get, every referral, every email, etc.

With this data, we try to understand clients' states and their needs when in those different states. We can then detect changes in state and consider possible triggers. This process by itself can lead to insights that help us keep our clients happier.

And once we define and understand states, and detect and understand clients' transitions between them, we can develop state transition matrices and Markov chain models that allow us to study system-level effects.

Demand Modeling

One of the many uses of these Markov chain models is to anticipate future demand, which is important because we often need to buy inventory months before it arrives at the warehouses. We must also ensure that we have the right number of resources and human stylists available at the right times.

Inventory Management

Inventory depletion through customer demand must ultimately be offset by purchases of new inventory. One of the challenges is in getting the timing of purchases right, so that we maintain adequate inventory availability for stylists while minimizing the sum of ordering costs and carrying costs (the operation costs and opportunity costs of capital associated with the area under the inventory curve).

Meeting future demand is just one of our inventory management challenges: we must also allocate inventory appropriately to different warehouses, and occasionally donate old inventory to make room for new styles. We can use algorithms to help us with these processes.

(Note that the situation is more complex than this simple illustration, since we must drill down to look at the availability of different types and styles of clothing in each of the warehouses. But we'll stick to the simple illustrations here for cleanliness.)

How much of what styles to purchase? Which items should go to which warehouse? What inventory should be donated when?

We answer these questions by using a model of the system dynamics, fitting it to historical data and using it for robust optimization given quantified uncertainties in our forecasts.

New Style Development

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And designing new styles for our Stitch Fix exclusive brands provides a great opportunity to tailor new designs for particular client segments that tend to be underserved by other brands.

We approach this opportunity with inspiration from genetic algorithms: we use recombination and mutation along with a fitness measure—the same mechanism used by mother nature in evolution by natural selection.

The first step is to think of each style as a set of attributes ("genes").

Then consider our vast set of styles this way, and consider the client feedback ("fitness") we have available for each of them.

Now consider creating new styles by recombining attributes from existing styles and possibly mutating them slightly. Note that the number of possible combinations is very large (∏ki).

In the next step, we deviate somewhat from a canonical genetic algorithm: instead of simply selecting based on fitness and then unleashing random recombinations and mutations as the next generation of styles, we’re somewhat more picky about what makes it into our inventory.

We first develop a model of how well a given set of attributes is likely to suit the target clients. We then use this model to highlight a variety of attribute-sets that we think have a high probability of love.

We then work with our human designers to vet and refine this collection, and ultimately to produce the next generation of styles.

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There is indeed a lot going on in our Algorithms team.

Thus far we have touched on some of the projects in our three vertically-aligned teams: Styling Algorithms, Merch Algorithms & Client Algorithms.

海外加速器YouTube

The Data Platform team provides the data and compute infrastructure—along with a collection of internal SaaS products—that allow the vertically-aligned data scientists to effectively and efficiently carry out analysis, write their algorithms, and put them into production. The platform nicely encapsulates properties like data distribution, parallelization, auto-scaling, failover, etc. This allows the data scientists to focus mostly on the science aspect yet still enjoy the benefits of a scalable system. And, the data platform engineers focusing on building… well, platforms. That is, they are not burdened with business logic and requirements for which they don’t have the context. That is the job of the full-stack data scientists. At its heart is the idea that "Engineers Shouldn't Write ETL." Our Data Platform team enables data scientists to carry algorithm development all the way from concept to production.

能上油管的网络加速器 lda2vec s j (1) , s j (2) , s j (3) s j clothing vector j c i (1) , c i (2) , c i (3) c i i customer vector A B TX c 1 feb14 client data shipment requests item selections stylist notes client feedback warehouse assignment clients (1) New Style DevelopmentInventory Management (2) State MachinesDemand Modeling 能上facebook的加速器 上油管挂什么加速器MatchmakingRecomm. Systems YouTube免费加速器 min i j x Σ Σ d ij  x ij s.t. x ij     { 0,1 } x ij   ≤ |S|  - 1    V, S    什么加速器可伃看油管j Σ x ij   =  2   i j Σ 求上油管的加速器 d ij  = | p i (y)  -  p j (y) | + | p i (x)  -  p j (x) | +  β ij if    k  s.t. (  p i (x)  >  γ k 雷神加速器上youtube p i (x)  <  γ k+1  ) &         (  p j (x)  >  γ k  ) & (  p j (x)  <  γ k+1  ) then  浏览youtube加速器推荐 ij   = 2  min ( (  p i (x)  -  γ k  ), (  γ k+1  -  p i (x)  ), x浏览器看YouTube YouTube免费加速器 p j (x) 浏览youtube加速器推荐 γ k  ), (  γ k+1  -  p j (x)  ) ) else    β ij   = 0 and 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 where x k   =   我装了 88 款 Chrome 扩展的浏览器里都有啥? - 少数派:2021-6-12 · 要想企码没 bug,除了祭祖女装 996 外,好看实用的浏览器扩展也能增分不少。在本节,我就为大家推荐 4 款程序员必备的 Chrome 扩展,希望能帮助你码途一路顺风。 GayHub 作为全球最大的同性交友平台,GitHub 是不少程序员的工作必备网站,而 GayHub 就 p   ( trans. i → j  )   =  logit  (  β 0   +  β 1   x 1   +  β 2   x 2   +  ...  ) client states i Σ demand t   = n i,t  p   ( demand |  ) q ij   =  f  ( shipping_cost ij , YouTube免费加速器 ij , inventory_match ij , ... ) 0.235 0.172 什么加速器可伃看油管 0.382 0.832 clients i warehouses j costs q ... 0.347 0.825 x浏览器看YouTube ... 0.533 0.874 0.193 ... ... TX CA WI assignments a 0 1 0 ... 1 0 0 ... 0 0 1 ... ... TX CA WI min i j a Σ Σ a ij q ij s.t. a ij = 能上facebook的加速器 i j Σ a ij { 0,1 } , i,j ... a ij <  k j j i Σ COST MATRIX OPTIMIZATION SOLUTION solve for missing elements in this client's row ... ? 浏览youtube加速器推荐 ? 0.54 ? 什么加速器可伃看油管 ? 0.92 ? 0.13 ? ? 0.85 0.76 ? matchfeedback 0.47 0.23 ... 0.59 0.14 ... 0.62 0.90 ... explicitclient features 0.21 0.74 上油管挂什么加速器 0.26 0.85 ... ... ... ... ... explicitstyle什么加速器可伃看油管 c i s j ? ? ? ? ? ... ... ... ... ... latentstylefeatures ? ? ... ? ? ... ? ? ... latentclient features m ij = f ( fix_history ij , feedback ij , style_profile_alignment ij YouTube免费加速器 ) match scores m 0.235 0.624 0.172 ... 雷神加速器能上youtube 0.347 x浏览器看YouTube ... 0.193 0.874 0.533 ... ... stylists j clients i assignments a 0 1 0 ... 1 0 0 ... 0 0 1 ... ... min i j a Σ Σ a ij m ij 上油管挂什么加速器 a ij = 1 i j Σ a ij { 0,1 } , i,j ... a ij < k j j i Σ P   (   correct_prediction   )   =  β  雷神加速器上youtube  α P   (   1 |  stylist   )   +  α P   (   1 |  client   ) A B log          =    α x浏览器看YouTube Xβ    +    Zb    ,         ~  N 海外加速器YouTube p YouTube免费加速器p { { fixedeffects randomeffects MATCH SCORES BY collaborative filtering 什么加速器可伃看油管 convolutional neural nets 浏览youtube加速器推荐 求上油管的加速器 S M L 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 prod 雷神加速器上youtube API load_my_data(params…) run_my_algorithm(params..) deploy_to_production(params...) cosinesimilarity 0.62 pinnedimageshe likes ... vector雷神加速器上youtube imageof clothingx浏览器看YouTube ... vectordescription 17 42 35 86 59 17 42 35 86 59 雷神加速器能上youtube什么加速器可伃看油管 I have that 雷神加速器上youtubecoming up hm. my closet is getting full datetime event state 1 state 2 state 3 state 2 time demand warehouses CA warehouse TX warehouse WI warehouse PA warehouse GA warehouse replenish allocate donate deliver return (1) New Style DevelopmentInventory Management model xt ( x ,  u ,  w )   什么加速器可伃看油管x = inventory levelsu = purchase, allocation and clearance海外加速器YouTubew = demand forecast total cost TC carrying cost h ordering cost K order quantity 海外加速器YouTubeper unit TC* Q* Q*   上油管挂什么加速器 h 1/2 ( ) 2.14 h/w 1.27 1.32 h/w 9.53 1.87 h/w 3.93 1.52 h/w 8.42 1.17 h/w 2.55 2.83 h/w 2.84 雷神加速器上youtube h/w 4.42 1.19 h/w 1.93 海外加速器YouTube h/w 6.14 1.32 h/w 9.23 海外加速器YouTube h/w 7.26 2.31 h/w 海外加速器YouTube 海外加速器YouTube h/w 能上facebook的加速器 1.52 h/w 7.23 1.52 h/w 上油管挂什么加速器 closet warehouses Initial Population Selection Recombination Mutation Next Population