Tag Archives: Faceted Search

Feeling Lucky Is the Future of Search

If you visit the Google homepage on your desktop, you’ll see a rare, prehistoric specimen – one that most Google users don’t see the point of: the “I’m Feeling Lucky” button.

Google has already removed it from most of its interfaces, and even here it only serves as a teaser for various Google nitwit projects. And yet the way things are going, the “Feeling Lucky” ghost may just come back to life – and with a vengeance.

lucky

In the early years, the “I’m Feeling Lucky” button was Google’s way of boldly stating “Our results are so great, you can just skip the result lists and head straight to destination #1”. It was a nice, humorous touch, but one that never really caught on as users’ needs grew more complex and less obvious. In fact, it lost Google quite a lot of money, since skipping the result list also meant users saw fewer and fewer sponsored results – Google’s main income source. But usability testing showed that users really liked seeing the button, so Google kept it there for a while.

But there’s another interface rising up that prides itself on returning the first search result without showing you the list. Did you already guess what it is?

robots

Almost every demo of a new personal assistant product will include questions being answered by the bot tapping into a search engine. The demos will make sure to use simple single-answer cases, like “Who is the governor of California?” That’s extremely neat, and was regarded as science fiction not so many decades ago. Amazing work on query parsing and entity extraction from search results has led to great results on this type of query, and the quality of the query understanding, and resulting answers, is usually outstanding.

michelle

However, these are just some of the possible searches we want our bots to run. As we get more and more comfortable with this new interface, we will not want to limit ourselves to one type of query. If you want to be able to get an answer for “Give me a good recipe for sweet potato pie” or “Which Chinese restaurants are open in the area now?”, you need a lot more than a single answer. You need verbosity, you need to be able to refine – which stretches the limits of how we perceive conversational interfaces today.

Part of the problem is that it’s difficult for users to understand the limits of conversational interfaces, especially when bot creators pretend that there are no such limits. Another problem lies in the fact that a natural language interface may simply be a lousy choice for some interaction types, and imposing it on them will only frustrate users.

There is a whole new paradigm of user interaction waiting to be invented, to support non-trivial search and refine through conversation – for all of those many cases where a short exchange and single result will probably not do. We will need to find a way to flip between vocal and visual, manage a seamless thread between devices and screen-based apps, and make digital assistants keep context on a much higher level.

Until then, I guess we’ll continue hoping that we’re feeling lucky.

 

siri-physics

Searching for Faceted Search

Just finished reading Daniel Tunkelang’s recently published book on Faceted Search. I read Daniel’s blog (“The Noisy Channel“) regularly, and enjoy his good mix of IR practice with emphasis on Human-Computer Interaction (HCI). With faceted search tasks on the roadmap at work, I wanted to better educate myself on the topic, and this one looked like a good read, with the cover promising:

“… a self-contained treatment of the topic, with an extensive bibliography for those who would like to pursue particular aspects in more depth”

With 70 pages, the book reads quickly and smoothly. Daniel provides a fascinating intro to faceted search, from early taxonomies, to facets, to faceted navigation and on to faceted search. He adds an introductory chapter on IR, which is a worthwhile read even for IR professionals with some interesting insights. One is how ranked retrieval that we all grew so accustomed of, blurred the once clear border of relevant vs. non-relevant that set retrieval enforced. Daniel suggests that this issue is significant for faceted search, being a set-retrieval oriented task, and a pingback on his blog led me to a fascinating elaboration on this pain in another fine search blog (recommended read!).

With such elaborate introductory chapters and more on faceted search history, not much is left though for the actual chapters on research and practice, and as a reader I felt there could be a lot more there. But then, it is reasonable to leave a lot to the reader and just give a taste of the challenges, to be later explored by the curious reader from the bibliography.

However, that promise for extensive bibliography somewhat disappointed me… With 119 references, and only about a quarter being academic publications from the past 5 years, I felt a bit back to square one. I was hoping for more of a literature survey and pointers when discussing the techniques for those tough issues, such as how to choose the most informational facets for a given query or how to extract facets from unstructured fields. Daniel provide some useful tips on those, but reading more on these topics will require doing my own literature scan.

In any case, for a newcomer with little background in search in general and faceted in particular, this book is an excellent introduction. Those more versed with classic IR moving into faceted search, will find the book an interesting read but probably not sufficient as a full reference.