The audience was very engaged and questioned just about all of the TunkRank model’s assumptions. I’m hopeful that as Jason Adams and Israel Kloss work on making a business out of TunkRank, they’ll bridge some of the gap between simplicity and realism.
A research study I like enough to have blogged about it a few times is Princeton sociologist Matt Salganik‘s dissertation work on music preferences and social contagion. For those unfamiliar with this work, here is the abstract of his Science article “Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market” (co-authored with Peter Dodds and Duncan Watts):
Hit songs, books, and movies are many times more successful than average, suggesting that “the best” alternatives are qualitatively different from “the rest”; yet experts routinely fail to predict which products will succeed. We investigated this paradox experimentally, by creating an artificial “music market” in which 14,341 participants downloaded previously unknown songs either with or without knowledge of previous participants’ choices. Increasing the strength of social influence increased both inequality and unpredictability of success. Success was also only partly determined by quality: The best songs rarely did poorly, and the worst rarely did well, but any other result was possible.
The result is hardly surprising to anyone familiar with the history of pop music. But I’m intrigued by the possibility that technology is simultaneously pulling music as a social phenomenon in two opposite directions.
On one hand, YouTube and social networks may actually be amplifying the positive feedback of music popularity. The recent story of YouTube sensation Greyson Chance (yes, a 13-year old with his own Wikipedia entry) becoming a national phenomenon in a couple of weeks attests to the power of social contagion. I don’t mean to take anything away from Chance’s talent, but I feel safe asserting that his talent was necessary but hardly sufficient to achieve his popular success.
On the other hand, Internet radio services like Pandora and Last.fm, despite their social features, offer the possibility of drastically reducing the effect of social influence. Both of these services require users to provide some representation of their musical tastes as initial inputs, whether by selecting preset stations or using particular artists or songs as seeds. Presumably those tastes are in large part the product of social influence. But the subsequent interaction between users and these services is relatively buffered from social influence. Users hear songs while listening privately through headphones–in many cases at work or while commuting. No one else is around when those users decide how to rate what they are listening to.
Granted, social context will always seep in–I don’t think I could give a thumbs-up to a Justin Bieber song even in the privacy of my own Pandora profile. But much of the music I discover is from artists I’ve never heard of–and thus evaluate without the explicit social influence of preconceptions about those artists.
As it turns out, I often discover after the fact that a number of the artists I like have achieved popular success. I can’t tell whether that reflects on their objective music quality, my own conformity of musical taste, or skew on the part of the recommendation system (cf. does everything sounds like Coldplay?). Still, I’m quite sure that I’m not favoring music based on prior knowledge of its popularity –for the most part, I don’t have that information at the time that I decide whether I like a song. Indeed, I hear new music almost exclusively through Pandora.
I don’t know how exceptional I am as a media consumer, but I suspect my case is increasingly common. Perhaps we are heading into a world where there will be a split between musical taste as social currency vs. musical taste as purely personal pleasure. It’s harder for me to imagine books or feature-length movies becoming so divorced from social context, if only because consuming them is a much larger and concentrated investment.
Still, I think it’s a big deal that this is happening in music. It’s a welcome counterpoint to the winner-take-all dynamic that has dominated the past decades of pop music. I can’t say that it will make the music industry more of a meritocracy–or that I even know what that would mean. But I think it’s a welcome step away from the caricature of conformity demonstrated by Salganik’s research.
Earlier this week, Peter Morville and Mark Burrell presented a UIE virtual seminar on “Leveraging Search & Discovery Patterns For Great Online Experiences“. It sold out! And I thought Pete Bell and I had done well with our seminar on faceted search!
But I’m hardly surprised. Although I wasn’t able to attend it myself, I gather from Twitter and the blogosphere that it was a great presentation. I enjoyed serving as a reviewer for Peter’s new book on Search Patterns, and I contributed a bit to Endeca’s UI Design Pattern Library while I was there and Mark’s team was developing it.
In reading reactions to the seminar, I was particularly intrigued by a post entitled “Search and Browse” by Livia Labate on her fantastically named blog, “I think, therefore IA“. She raised a question that I think needs to be asked more often: when is (or isn’t) faceted search appropriate?
Her conversation with readers in a comment thread offered some possible answers:
- Faceted search helps users who think in terms of attribute specifications as filtering criteria.
- Faceted search supports search by exclusion, as opposed to by discovery.
- Faceted search requires a set of useful facets that is neither too small nor too large.
I’d like to propose my own answers. Here are the conditions for which I see faceted search being most useful:
- Faceted search supports exploratory use cases, in contrast to known-item search. For known-item search, users are better served by a search box to specify an item by name, or a non-faceted hierarchy to locate it. In contrast, faceted search optimizes for cases where users are either unsure of what they want or of how to specify it.
- Faceted search helps users who need or want to learn about the search space as they execute the search process. Facets educate users about different ways to characterize items in a collection. If users do not need or want this education, they may be frustrated by an interface that makes them do more work.
- The search space is classified using accurate, understandable facets that relate to the users’ information needs. As I’ve discussed before, data quality is often the bottleneck in designing search interfaces. Offering users facets that are either unreliable or unrelated to their needs is worse than providing no facets at all.
Given the above criteria, it’s not surprising that faceted search has been a huge success in online retail: shopping is often an exploratory learning experience, and retailers tend to have good data.
But the success of faceted search in retail overshadows other domains where faceted search may be even more valuable. My favorite example is faceted people search, most recently demonstrated by LinkedIn. I would love to see other entities (locations, businesses, etc.) receive similar treatment, at least in contexts where exploration is a common use case.
I think Livia is right to be skeptical about any interface that introduces complexity–and facets do introduce complexity. I hope that my guidelines help answer her question as to when that complexity is worthwhile and perhaps even necessary to help users satisfy their information needs.
For me, 2009 marked the end of a decade-long run at Endeca, where I focused on bringing HCIR to enterprises. I’m particularly proud of two professional accomplishments: writing a book on faceted search, and organizing the SIGIR 2009 Industry Track.
But past is prologue. I spent the last several weeks of 2009 as a Noogler, and I launch into 2010 living and breathing search on the open web.
What’s on my mind? Here are some top-of-mind questions to which I hope to have better answers by this time next year:
- Exploratory Search: how should we determine that users want a more exploratory search experience, rather than one that minimizes time to a best-effort result? How should we respond to queries that clearly don’t have a single best answers, such as queries of the form [category] or [category location]?
- Mobile Search: should it be just like non-mobile search with a few tweaks to accommodate the device form factor? Or does / should mobile search fundamentally change the way we interact with information?
- Real-Time Search: is it more than real-time indexing plus emphasizing recency as a query-independent relevance factor? What are the use cases, and how should we be addressing them?
- Social / Collaborative Search: should we be looking to microblogging or other social media signals to augment (or even supplant!) link-based citations as authority cues? Should we be supporting mediated search by linking people to people, rather than directly to information?
- Transparency: is it possible to offer more transparency in relevance ranking without losing ground in the battle against spam and black-hat SEO?
To be clear, these are simply the questions that are on my mind–I’m speaking as an individual and not as a Google employee. That said, a great thing about being at Google is that there are people working on all of these areas. So I expect 2010 to be an exciting year!
Curious to hear what problems are on other people’s minds as we enter the new year. Comment away!
Researchers from Microsoft say it’s very challenging. Google is trying, but there’s a long way to go. And Eric Iverson just wrote me to describe his own preliminary efforts to build faceted search on top of Yahoo! BOSS.
I believe there’s a clearly established business case for faceted search inside the enterprise, for site search (especially for retail and media / publishing sites), even for vertical search on the open web. In all of these cases, relevance-ranked results are insufficient to meet a large subset of users’ more exploratory information needs, and HCIR approaches like faceted search are an easy sell.
But it seems much harder to make this case for general web search. The track record of startups in this space isn’t very encouraging. That could be because no one has done it right, but Clayton Christensen’s theory of disruptive innovation would suggest that a successful entrant wouldn’t have to have parity across the board, but would simply need to win on an underserved market segment. Perhaps the increasing use of faceted search for vertical search is how this process is playing out, and faceted search for general web search may end up being a slow agglomeration of verticals.
I’m curious if others have been pursuing efforts like Eric’s. Are the available APIs powerful enough to prototype your own faceted web search engine? If they aren’t, then is this a potential business opportunity for one of the major (or non-major) search engines to promote innovation by offering an open system? Or, if Yahoo! BOSS already offers such an open system, what should we make of the scale of its impact?
The idea of trust as social currency is appearing in more articles, conferences, and books. This is all highly consistent with the TIP thesis on Innovation Economics which describes the necessity of a vetting mechanism among the knowledge inventory as a means for the emergence of a currency in a market – that is, a conversational currency. People need to trust the currency if they are to trade the currency.