Daniel Rose about Product Search

Posted on November 10, 2011


We had Dan Rose from A9.com (search subsidy of Amazon.com) as a visitor in CIIR today, who gave a talk titled ‘Lessons and Challenges from Product Search’. Throughout his talk, he listed the factors makes product search different from typical web search problem, how A9 approaches some of these issues, and future challenges. Here I intend to summarize several interesting points:

  • People do product search with many different intentions, and there are as many non-buying intent searches as buying intent searches. Within buying intents, users can be in different position in buying process (awareness / desire / interests / purchase)
  • People tend to browse more in product search environment than in typical web search. He didn’t mention specific reason, yet rich interface with many controls and visual elements, different search intent (searching for fun) were suggested.
  • Search context (product category) matters a lot. Search queries take different characteristics, so are post-query behaviors. For instance people go much deeper down the ranked list when buying clothes, with many more side-by-side comparisons. Ranking, user interface, even spell corrections should be customized accordingly.
  • Amazon is also a marketplace with lots of vendors, and the realtime update of all the product information including availability is critical. This also causes many complexities. For instance, how can we set the price for sorting the list of items?
  • Rich structure inherent for product search is a blessing, yet should be used with care. Each field has different characteristic. For instance, how would you incorporate both title and full-text of a book for ranking? Also, structured information can be missing or incorrect in many cases, both for manual and automated generation.
  • Behavioral data is useful, yet again requires caution. Product rating can suffer from individual bias and small sample size. Click count cannot be equated with signal of relevance. Can we say that multiple clicks are always better than single or no click?

It was certainly an interesting talk, with many food for thoughts. At the last part of his talk, he presented the idea of information-seeking funnel (more on his workshop paper), which drew an analogy from product shopping. (reminding me of information foraging theory ) The point here is that users should go through with different process in getting to the information desired, and the system should be able to identify and support each step appropriately.

In this framework, many modes of search behavior are combined into the goal-completion. The first challenge would be identifying user’s current stage without the interruption of flow, yet given the amount of information web search engines collect per user, this wouldn’t be an impossible goal. Another challenge would be transferring the context across different stages. For instance, how can we condition user’s search in ‘Asking’ stage on what we learned from ‘Exploring’ stage? This wouldn’t be trivial even after we know that they belong to the same information goal.

p.s.  I could found slides from his previous talk with the same title at SIGIR’10.
Posted in: HCI, IR