Evan Schuman's StorefrontBacktalk

Subramaniam, who used to work on the eBay search engine team, said this illustrates some of the differences in the type of search engine issues that exist for Google, Bing or Yahoo (everything from everywhere) to those that apply to Amazon or eBay (limited to E-Commerce inquiries, but for all of their and their third-party sellers’ products) to those that apply to a Walmart or Target (limited to the items offered by a single merchant).

Subramaniam argued that the single-retailer engine is actually far more challenging because of the fewer products it can offer. That’s somewhat counter-intuitive, in that mastering a smaller database would seem easier. But the Polaris goal is to correctly answer the question.

If a user, for example, is asking for a specific type of running shoe, Google can simply return any results referencing that shoe. But if Walmart.com doesn’t carry that specific shoe, it needs to have a database of umpteen products it does not sell so that it can recognize those brand names and be able to offer a similar product it does sell. From a programmer’s perspective, that’s a much more difficult task.

“Even if we have relatively fewer items compared with Amazon or eBay, it’s a harder problem for us to satisfy the end user. Google can find those exact pages,” Subramaniam said. “The challenge is more on the interpretation of the user query, not on delivering literally what they ask for.”

And doing that is more difficult because Walmart.com’s control over the product descriptions it has is not that much stronger than Amazon. On the plus side, it only sells its own products. But it still has to deal with whatever product descriptions and information its huge number of suppliers delivers. Keeping those descriptions uniformly consistent—let alone comprehensive, delivering all the datapoints Walmart’s new search engine wants—is not a lot of fun.

Other challenges exist, many of which are based on shopper psychology. When trying to personalize an engine, that becomes crucial but also very tricky. Let’s say the system has learned over time that this shopper is a man who is an electronics geek. It then interprets inquiries for that person. But what if this geek is buying a gift for his girlfriend or wife? “You don’t really know what the user means,” Subramaniam said.

That’s some of the rationale for holding off personalization for the engine’s next version. “What we don’t have yet is, ‘What did you type in the session?’ That is on our roadmap, factoring in the user’s history,” he said, adding that for the initial version of Polaris, “we decided to play it safe and keep it anonymous and aggregate at first.”