In part 1 of Ranking at eBay, I explained what makes the eBay search problem different to other online search problems. I also explained why there’s a certain kinship with Twitter, the only other engine that deals with the same kinds of challenges that eBay does. To sum it up, eBay’s search problem is different because our items aren’t around for very long, the information about the items changes very quickly, and we have over 300 million items and the majority are not products like you’d find on major commerce web sites like Walmart or Amazon.
In this post, I explain how we think about using data in the eBay ranking problem. In the next post, I’ll explain how we combine all of that data to compute our Best Match function, and how it’s all coming together in a world where we are rebuilding search at eBay.
Ranking Factors at eBay
Let’s imagine that you and I work together and run the search science team at eBay. Part of our role is to help make sure that the items and products that are returned when a customer runs a query are ordered correctly. Correctly means that the most relevant item to the customer’s information need is in the first position in our search results, the next most relevant is in the second position, and so on.
What does relevant mean? In eBay’s case, you could abstract it to say that the item is great value from a trusted seller, it matches the intent of the query, and it’s something that buyers want to buy. For example, if the customer queries for a polaroid camera, our best result might be a great, used, vintage Polaroid camera in excellent condition. Of course, it’s subjective: you could argue it should be a new generation Polaroid camera, or some other plausible argument. In a general sense, relevance is approximated by computing some measure of statistical similarity — obviously, search engines can’t read a user’s mind, so they compute information to score how similar an item is to a query, and add any other information that’s query independent and can help. (In a future post, I’ll come back and explain how we understand whether we’ve got it right, and work to understand what the underlying intent is behind a query.)
Let’s agree for now that we want to order results from most- to least-relevant to a query, when the user is using our default Best Match sorting feature. So, how do we do that? The key is having information about what we’re ranking: and I’ll argue that the more, different information we have, the better job we can do. Let’s start simply: suppose we only have one data source, the title of the item. I’ve shown below an item, and you can see it’s title at the top, “NICE Older POLAROID 600 Land Camera SUN AUTO FOCUS 660″.
Let’s think about the factors we can use from the item title to help us order results in a likely relevant way:
- Does the title contain the query words? The rationale for proposing this factor is pretty simple: if the words are in the title, the item is more relevant than an item that doesn’t contain the words.
- How frequently are the query words repeated in the title? The rationale is: the more the words are repeated, the more likely that item is to be on the topic of the query, and so the more relevant the item.
- How rare are each of the query words that match in the title? The rationale is that rarer words across all of the items at eBay are better discriminators between relevant and irrelevant items; in this example, we’d argue that items containing the rarer word polaroid are probably more likely to be relevant than items containing the less rare word camera.
- How near are the query words to the beginning of the title? The argument is that items with query words near the beginning of the title are likely more relevant than those containing the query words later in the title, with the rationale that the key topic of the item is likely mentioned first or early in the title. Consider two examples to illustrate: Polaroid land camera 420 1970s issued still in nice shape retro funk, and PX 100 Silver Shade Impossible Project Film for Polaroid SX-70 Camera. (The former example is a camera, the latter example is film for a camera.)
Before I move on, let me just say that these are example factors. I am not sharing that we do or don’t use these factors in ranking at eBay. What I’m illustrating is that you and I can successfully, rationally think about factors we might try in Best Match that might help separate relevant items from irrelevant items. And, overall, when we combine these factors in some way, we should be able to produce a complete ordering of eBay’s results from most- to least-relevant to the query.
So far, I’ve given you narrow examples about text factors from the title. There are many other text factors we could use: factors from the longer item description, category information, text that’s automatically painted onto the item by our algorithms at listing time, and more. If we worked through these methodically, we could together write down factors that we thought might intuitively help us rank items better. At the end of process, I’m guessing we’d have written downs tens of factors for the text alone we have at eBay.
You can see my argument coming together: if you used just one or two of these factors, you might do a good, basic job of ranking items. But if you use more information, you’ll do better. You’ll be able to more effectively discern differences between items, and you’ll do a better job of ranking the items. Net, the more (new, different, and useful) information you have, the better.
What’s key here is that we need different factors, and we need factors that actually do the right thing. There are some simple ways we can test the intuition about a factor before we use it. For example, we could ask a simple question: do users buy more of items that have this factor than those that don’t? In practice, there’s much more sophisticated things we can do to validate a factor before we decide to actually build it into search (and I’ll leave that discussion to another time).
The Factor Buckets
I believe in a five bucket framework of factors to build our eBay Best Match ranking function:
- Text factors (discussed above)
- Image factors
- Seller factors
- Buyer factors
- Behavioral factors
Pictures or images are an important part of the items and products at eBay. Images are therefore an interesting possible source of ranking factors. For example, we know that users prefer pictures where the background is a single color, that is, where the object of interest is easily distinguished from the background.
The seller is an important part of the buyer’s decision to purchase. You can likely think of many factors that we could include in search: how long have they been selling? How’s their feedback? Do they ship on time? Are they a trusted seller?
Buyer factors is an interesting bucket. If you think about the buyer, there’s many potential factors you might want to explore. Do they always buy fixed price items? What are the categories they buy in? What’s the shoe size they keep on asking for in their queries? Do they buy internationally?
Behavioral factors are also an exciting bucket. Here’s a few examples we could work on: does this item get clicks from buyers for this query? What’s the watch count on the item? How many bids does the auction have? How many sales have their been of this fixed price item, given it’s been shown to users that many times? If you want to dig deeper into this bucket, Mike Mathieson wrote a super blog post on part of our behavioral factor journey.
Where we are on the factors journey
We formed our search science team in late 2009, when Mike Mathieson joined our team. We’ve built the team from Mike to tens of folks in the past couple of years, and we’re on a journey to make search awesome at eBay. Indeed, if you want to join the team — and have an awesome engineering or applied science background, you can always reach out to me.
Right now, we use several text factors in Best Match, we have released a few seller factors and behavioral factors, and we have begun working on image and buyer factors. All up, we have tens of factors in our Best Match ranking function. You might ask: all of these factors seem like they’d be useful, so why haven’t you done more? There’s a few good reasons:
- Our current search engine doesn’t make it easy to flexibly combine factors in ranking. (that’s one good reason why we’re rewriting search at eBay.)
- It takes engineering time to develop a factor, and make it available at query time for the search ranking process. In many cases, factors are extremely complex engineering projects — for example, imagine how hard it is to process images and extract factors when there’s 10 million new items per day (and most items have more than 1 image), and you’re working hard to get additions to the index complete within 90 seconds. Or imagine how challenging it is to have real-time behavioral factors available in a multi-thousand computer search grid within a few seconds. (If you’ve read Part #1 of this series, you’ll appreciate just how real-time search is at eBay.)
- Experimentation takes time. Intuition is the easy part, building the factor, combining it with other factors, testing the new ranking function with users, and iterating and improving takes time. I’ll talk more about experimentation and testing in my next post
In the third and final post in this series, I’ll explain more about how we combine factors and give you some insights into where we are on the search journey at eBay. Thanks for reading: please share this post with your friends and colleagues using the buttons below.