Today I’m happy to announce another book, Designing Search (by Greg Nudelman) that I also have a small contribution in. It’s a 3-page “sidebar” called: Social search can’t be solved by an algorithm — basically an updated version of the blog post with the same name that I wrote back in January 2010. Here’s an excerpt for your enjoyment:

“The benefits of “social search” come from using social network information in conjunction with search algorithms. Services can begin to “learn” which of your friends have expertise or knowledge about certain topics. Then when you search for those topics, people from your network with relevant knowledge could be made available to you…People may appear only as a search result listing, linking to their profile or email address; or they could appear as a direct contact, like through an instant messaging window on the same page as the search results…

Another area for social support is during search difficulties — or when people are struggling to find certain information. Anytime you can’t find what you’re looking for on the first try, or you rework your query over and over again, these are use cases that could benefit from…[tapping] your social network.”

——

Designing Search: UX Strategies for eCommerce Success
by Greg Nudelman

A glaring gap has existed in the market for a resource that offers a comprehensive, actionable design patterns and design strategies for ecommerce search—but no longer. With this invaluable book, user experience designer and user researcher Greg Nudelman shares his years of experience working on popular ecommerce sites as he tackles even the most difficult ecommerce search design problems. Nudelman helps you create highly effective and intuitive ecommerce search design solutions and he takes a unique forward-thinking look at trends such as integrating searching with browsing to create a single-finding user interface.

• Offers much-needed insight on how to create ecommerce search experiences that truly benefit online shoppers
• Juxtaposes examples of common design pitfalls against examples of highly effective ecommerce search design solutions
• Presents comprehensive guidance on ecommerce search design strategies for the Web, mobile phone applications, and new tablet devices
• Shares the author’s years of unique experience working with ecommerce from the perspective of the user’s experience

Designing Search is mandatory reading if you are interested in orchestrating successful ecommerce search strategies.

ISBN: 978-0-470-94223-9
Paperback
304 pages

They were building upon (among other things) my work on asking and answering behavior on social networks (“Do your friends make you smarter“) and wanted to know more details about things like: why do people post questions, answer questions, and what motivates people to go the social search route over traditional search.

I want to talk about their conclusion here first: where they discuss the tradeoffs between using social networks and search engines. This was the most interesting part of the paper and is the part that has the most practical take-aways for the UX community.

Basically, for questions requiring trustful and personalized responses like for recommendations or opinions, social networks were strongly preferred over search engines. People occasionally perceived a delay in response time (but sometimes did not!) — and either way, this delay was not a downer since it saved people the effort of making multiple attempts on a search engine before getting the right answer back (if they ever did). Also, subjective questions got faster responses on social networks than objective questions!

Additionally, they talk about how being on social networks is fun for people. It’s too bad that they under-emphasize this point because it’s really important from a social interaction design perspective! People engage on social networks because it’s fun! There’s an emotional tie to the community that keeps bringing them back. When it comes time to ask a question, people turn to their friends for help because it keeps them connected with others, helps them to share information about themselves, and keeps them engaged in their community. Fun is an important element of design — and until Google, Facebook, Yahoo, Microsoft, or other search providers make the information seeking part fun, they will lose. It’s not enough to simply pipe in real-time Twitter results and expect that to be fun or more personalized!

That (above) is the juiciest part of the paper, but I do summarize the whole paper below for other nuggets of inspiration.

To start, the authors note that Q-A on social networks is unique (like unique from Q&A sites like Yahoo Answers) because:

• questions are posted with true identities (rarely anonymously)
• people’s audience is smaller (being only people’s direct contacts, or possibly friends-of-friends)
• status messages are short: so questions are succinct

Then they collected surveys from 624 Microsoft employees (1/4 were female) about their Q-A behavior on Facebook and Twitter mostly. The survey asked about:

Asking questions:

• whether they ever used their status messages to ask questions
• if yes, to share a recent example of a question they asked
• what kind of responses they received
• how often they logged onto social networking sites

And answering questions:

• whether they responded to someone else’s question (from a status update)
• if yes, to share a recent example of a question they answered

50% of people who replied to the survey did indeed use their status messages to ask questions! Sometimes they were silly questions (“Why are men so stupid?”) and sometimes they were serious (“Point and shoot camera just died — need to replace it asap. What should I buy? Think under $200″).

On asking questions

Questions took many shapes and forms. Some were targeted to “Anyone” in the social network; others were general statements put out to the network to interpret. Most questions were asking about recommendations (29%) or opinions (22%). Some were about facts (17%) and some were rhetorical (14%).

Questions also related to everything from technology (29%) to entertainment (17%), places (8%), current events (5%), and ethics and philosophy (2%). People said they’d generally avoid asking their network about topics like health, pornography, religion, and financial issues because those are too personal.

Another finding was that Twitter users were more likely to ask questions about technology, while Facebook users more likely to ask about home and family issues. This isn’t too surprising, but does show that the makeup of people’s networks affects what kinds of questions people will ask.

One looming question in this research is: why do people choose to ask their friends over searching on Google? In this study, people reported having more trust in their friends’ responses or that they thought traditional search wouldn’t work for them with this particular question. Sometimes it was obvious that their friends would know their history, family situation, and other preferences better than a search engine.

People also generally knew the makeup of their networks (meaning they could get targeted responses) and people wanted to advertise their current interests by way of asking questions of their friends.

What about answer speed or answer quality? There were mixed results, but often questions asked on social networks were not urgent so any delay was considered acceptable. It was also easier than sorting through tons of results on search engines and quite often more fun to engage with friends.

On answering questions

Almost everyone has answered a question on one occasion or another. And of the questions offered up in this survey — nearly every one received an answer! That’s kind of amazing considering Q-A sites in general have a much lower response rate, but it’s also likely due to the intimate make up of people’s social networks. Not to be an under-appreciated finding in this paper, however!

About one-quarter of questions got an answer in 30 minutes or less, and 90% were answered within one day. (I’d LOVE to see how Aardvark’s data compares here. I have several questions out to Aardvark that have never been answered; but of the ones that do get answered, it’s typically faster than 30 minutes.)

Yet people don’t seemed to be bothered by the 1-day response lag. Perhaps this is because replies from trustworthy friends are more valuable than junk search engine or Yahoo Answers responses?

Why did people respond to questions by others? Most often they were just trying to be helpful (37%) or had expertise in the area (32%). Those seem like good reasons to me, and ones that are often overlooked when thinking about how valuable personal social networks can be to people. At other times, people replied because they had a good relationship with the asker, were connected socially, or had some notion of social currency (that answering now may result in a favor later).

When people didn’t respond to questions by others, it was mostly because they didn’t know the answer! That’s pretty neat. But some said they’d prefer a personal request and ignored questions that were asked to the network as a whole. In fact, my research on Q-A in social networks revealed the very same thing — that people gave great responses when asked privately or one-on-one, even when they weren’t very knowledgeable about the topic.

Another interesting, but not very surprising finding, is that people who were more frequent social networks users received quicker responses! Why duh! People who are engaged with their communities have developed rapports and back-and-forths and social capital with folks — what goes around comes around. But it is cool to see this intuition confirmed in real data!

Unfortunately, this paper is weak on design recommendations. My colleagues and I have already suggested the very same ideas (e.g., surface your friends status messages on the results page — what I call “friend-filtered search” — or have a search engine post a question as a status message on your behalf.) Even as I say this, there are serious social interaction design issues with this approach. Expectations about search engine behavior, expectations about social network intrusion, privacy (obviously), interactions with online agents, etc. all come into play. I very much hope that these issues are being considered by the big search engine companies, but somehow I’m afraid they’re not.

Detailed information and registration can be found here: http://socialsear.ch

Why Enterprise Social Search?

Knowledge management and information retrieval in large organizations is a huge problem. A number of orgs are making efforts to address these issues; and leveraging social data — or information that people within the company hold — is one promising route.

Why a design workshop?

Although the premise of our workshop is social search in the enterprise, we won’t be satisfied by just writing or thinking about it. We want to bring together the sharpest minds in the enterprise world and mix them with designers, researchers, and IT professionals to come up with some practical solutions that actually could be implemented.

What next?

After our workshop in San Francisco, we’ll take our findings and carry them onto future workshops in Australia and Washington DC — to build upon our ideas and find ways to develop them even further.

Where can I learn more?

You can read more about who we are and what the workshop will entail over at http://socialsear.ch.

Also don’t miss…

…this presentation that we gave at the Enterprise Search Summit last Fall. This presentation gives a flavor of that topics that will be covered in our Enterprise Social Search Design Workshop in San Francisco.

We hope to see you at the workshop!

• me
• Max Ventilla (Aardvark)
• Ash Rust (OneRiot)
• Scott Prindle (Crispin Porter)
• Marc Vermut (fabulous moderator!)

It was in a pretty large room (Ballroom A), but wasn’t filled to capacity. There were perhaps 300 people present?

I wanted to write up a quick review of what we covered in the panel, although several people were live blogging the event. Their archived posts are here: Danny Sullivan from Search Engine Land, Jon McCartie, Michael Leis, Dawn Foster, Jonti Bolles from the SEM Blog. Sean Branagan also live tweeted the event. Thank you guys!

The panelists began by giving short presentations of our background and perspectives on social search (our slides). This lasted about 20 minutes before we dove into our discussion period. Below are a few key phrases and quotes by each of us, which essentially make up the nuggets of the panel:

From me:

• Think of search as a process over time, with a beginning, middle, and an end. Interactions with friends can happen anywhere along this path
• This also of how people want to interact with their friends during search?
• 3 kinds of social search: collective (about finding trends in a crowd), friend-filtered (what friends have shared), and collaborative (with a friend or Q&A)
• Social search is not about retraining users. It’s about tapping into what people are doing naturally. People actually want to talk to their friends for help.
• Challenge of social search is determining who has authority, who is relevant and how to index that
• Social networks mashed up with google would create relevancy.
• Social search will serve as a complement to google.
• People have two main strategies for interacting with friends during search: (1) ask the network, or (2) go it alone…and only then ask for help after you encounter difficulties.
• ~40% of searches are already involve asking friends for help, including informational and fact-based queries (not just subjective/opinion-based ones)
• Thrashing a sign of search failures — adding/removing just one keyword to your search and re-searching. Search engines could identify thrashing behavior and then present more relevant social information (or people)
• Several papers and related blog posts include: An Elaborated Model of Social Search; Do Your Friends Make You Smarter?; Towards a Model of Social Search; 3 Flavors of Social Search; Will social search topple Google?

From Max Ventila:

• A lot of untapped knowledge lives in your friends, but most people cannot easily access the knowledge in their social circle
• It’s hard to know who to ask, and there is social obligation associated with asking.
• Aardvark.com (launched last year at sxsw, recently bought by Google) is system for asking your network intelligently
• 85% answered questions in less than 5 minutes
• Most queries on web are “subjective queries” (e.g., about how people want to spend their time and money).
• And “subjective” searches generate most search revenue
• Average query length in Aardvark is 19 words versus 3 words on Google.
• Social intimacy facilitates trust in interactions better than authority
• Social context is not the same as social graph. This is important to consider when putting people together in social search tasks
• aardvark will give answers from sponsors, as means to monetize model. About 50% of users will opt to see sponsored answers!
• 50% of Ardvark users are content creators. Social norms trump market norms.
• Aardvark had a person behind the system (acting as the question router) for 6 months!
• Why? Because user experience should come before tech development (getting the social interaction right matters a lot)
• Check out their recent paper on the inner workings of the Aardvark engine

From Ash Rust:

• OneRiot.com mines popular topics across a whole bunch of social networks and social media sites
• 90% of OneRiot traffic comes from API
• What it delivers isn’t personally relevant; instead the focus is on source authority in delivering real time social web content
• People don’t care what the New York Times or MSNBC wants them to know. They want to know what the crowd knows
• 20% of searches can be solved better by social networks
• Their algorithm determines authority by seeing what people link to
• Check out the white paper on their algorithm

From Scott Prindle:

• Prindle heads up social search development at Crispin Porter (ad agency) — doing campaigns and app development for clients
• Digital marketing used to be about discrete campaigns; now it’s platforms for long-term conversations and towards on-going socially-driven brand platforms
• Creating buzz and conversation is the goal for most of Crispen Porter’s work
• Give customers something good to talk about in social media and they will talk
• New world of search marketing will be more about relevant content and engaging content than keywords alone
• Enterprises that open up direct customer access to staff create personal brand touch-points
• 50% of Crispin Porter’s content is for blog or conversational platforms = their social search stream

Unresolved issues/hard problems in social search:

• How is user authority measured?
• How is expertise measured? Who you are connected to is probably more important than how you describe yourself.
• How to deal with reputation, especially in changing social contexts?
• How important is a person’s social graph in ranking results and personalizing search?
• There is the issue of privacy (not wanting your social graph to have access to everything you say, do, or search for). People WANT granular control, but don’t want lots of clicks & buttons.

First, do you think search engines making use of social networks will improve search results and thus make our daily life a bit easier?

Yes! A lot of fact finding and information discovery already comes from friends, colleagues, and even acquaintances. Online social networks organize our personal relationships in a way that reduces the barriers to information exchange. At the moment, social networking sites aren’t set up for search per se—Facebook and Twitter want to get into search, but with Facebook, they can only reveal what your friends have OK’d to be public, and with Twitter, there’s enough noise, spam, and unknown people making claims that the results can be hard to trust. But, there’s already evidence that people are turning to these online social networking sites to ask their friends questions. On Twitter, these have been dubbed “lazy tweets”:

“Wondering where Bowtie saves it’s themes… Anyone? #LazyTweet” –@iphone360

“Looking for social media trends in the healthcare industry. Anyone out there have resources they can share? #lazytweet #healthcare” –@chrismevans

“Anyone know if there are Brocade SAN and Cisco MDS simulators? #lazytweet #healthcare” –@sloane

The real value for social search is making the search experience more personalized. If search engines can make use of existing ties, relationships, and data coming from social networks, they can use that data to bubble up results that come from a trusted friend network that may be as relevant (if not more relevant due to the trust factor) as traditional search results. The risk, however, is that our personal networks are narrow and not every search we perform may have counterpart results from social networks. This is why search algorithms will continue to play a large role in search, regardless of how “social” it gets.

Another benefit of using social network information in conjunction with search is that a services can begin to “learn” which of your friends have expertise or knowledge about certain topics. Then when you search for those topics, people from your network who may have relevant knowledge could be made available to you. It’s still unknown how visible searchers want other people to be in the search interface. People may only appear as a search result listing, linking to their profile or email address; or they could appear as a direct contact, like through an instant messaging window on the same page as the search results. Either way, the point is that direct person-to-person conversations can greatly supplement an information discovery process (as we pointed out in the “Do your friends make you smarter?” paper).

Another interesting area for social networking support during search is for searches that present difficulties. Anytime you can’t find what you’re looking for on the first try, or you rework your query over and over again — these are use cases that could benefit from asking a friend a question, pinging your social network, or finding a colleague/acquaintance who may have experience with this particular problem.

And second, do you expect these social search engines to become available in any near future?

Yes and no. Yes, in that there are a number of services that provide human answering — but these mostly do not include a search algorithm component, meaning that results are only driven by a direct human contribution. Such services include Aardvark, Cha Cha, Hunch, Quora, and Mahalo…

Another popular class of social search services only makes use of aggregated social data from large networks. I call this “collective social search” since it’s like the wisdom of crowds effect, in that you can see trends from the collective that might be useful in guiding your search. Google Search Suggest is an example of this — it shows you the common search phrases for a given few words. Twitter’s Trending Topics and OneRiot are similar. I think the popularity of this approach is that it’s algorithmic, so you can throw more programmers at it and hopefully improve the results. But it’s quite limiting in its utility since searchers will trust people they know or people who can be vouched for, whereas trends across an entire network have no intrinsic relationship to the searcher. Such results may help in the early stages of search when you’re still trying to formulate an ill-formed query, but won’t be as useful when you want to narrow down to a specific answer to your question.

Thus, the kind of “social” component I want to see in search will require combining both of the approaches I mentioned above. This is not trivial, and there are a lot of unknowns about how people will respond to a service that does this. How will people react if their search results are shared with their social network? Will it be different if we see how valuable it is when our networks’ search results are shared with us (the reverse case)? How will reputation and obligation come into play? How will reactions differ by personalities? By location? By past history? By the political climate?

If Facebook’s Beacon experiment taught us anything, it’s that “social” can’t be solved by an algorithm. We’re still a ways off from really solving social search.

I consider this to be a work-in-progress of methods for studying cultural and social behaviors in online, mediated spaces. Please share the methods that you have used or found to be useful so that we can continue the conversation around best practices.

What are you going to buy this holiday season? Gift cards aren’t very personal, but friends’ recommendations can be.

Richard MacManus recently covered the trends in e-commerce over the past decade. He noted that Amazon and eBay have dominated the online retail market with their model of using implicit user data to generate recommendations for others. Although this model will surely remain a centerpiece of the online retail experience, it may soon face competition as “social shopping” takes off.

What is social shopping? This is similar to the question of what is social search, which I addressed previously by describing the three flavors of social search: what they are, why they’re relevant and how they will help you search better. The Amazon and eBay model of online retail tapped into what I refer to as “collective social search.” Social shopping, on the other hand, is more like “friend-filtered social search.”

In social shopping, you see recommendations and reviews that your friends have shared. You see items that your friends have purchased or brands that your friends have shopped with. This matters a lot when you’re shopping for a digital camera and are stuck deciding between three different models. Of course, the last 10 years’ worth of people’s purchasing histories and written reviews on Amazon may help you narrow your choice — if you can filter out the noise. But those reviewers are entirely anonymous to you, even though they may use a real name and have a rating history with the site.

The decision you are making, as with most decisions, will carry consequences going forward, which is a part of the reason why collective intelligence can’t provide the necessary emotional “spark” in quite the way that a personal recommendation can. Patricia Mejia, a commenter on Richard’s e-commerce trends post, explained why she wants this in shopping: “I want to be inspired, intrigued and entertained when I shop online.”

Algorithms don’t provide that emotion. But a recommendation from a friend just might.

Plus, users increasingly expect this, and the larger and more connected our networks become, the more powerful this social shopping model will be. What are the social shopping services that do this best today? (Hint: not Amazon.)

Sites like ProductWiki are devoted to product comparisons, but the user base is most likely not your peer network. ThisNext and Kaboodle lie closer to the intersection of social media and e-commerce. They are predominantly social networks dedicated to sharing products and personal reviews.

Kaboodle’s user profile for “aplyler” closely resembles other social networking sites, and the site provides functionality for creating product lists, commenting on items and, of course, adding friends.

On ThisNext, users’ recommendations are featured front and center on their profiles. Here, “rjax” has been promoted to “Expert Maven” because of her extensive collection of recommended items. Unfortunately, the collection’s range is so vast that you probably wouldn’t care about the Christmas ornaments if you liked her review of the Macbook art decal.

Thus, the limitation with sites like ThisNext and Kaboodle is that you, your friends and the products you’re going to buy all exist on those sites. In other words, the sites are social shopping silos.

RunToShop, on the other hand, brings a distributed social networking model to social shopping. A small Finnish startup, RunToShop aims to bring social recommendations to you wherever you may be, and from the friends in your network who you trust.

This means that if you’re shopping for golf clubs on Smart Golf, recommendations will be embedded on the site through the RunToShop widget. Currently, all user reviews are shown, but in the next release, recommendations from friends will be prioritized. (You can pull in your friends with Facebook Connect.)

RunToShop also integrates with Facebook. So, if Facebook is where you spend most of your time, you can browse product offerings and friends’ recommendations directly through the RunToShop Facebook app.

Finally, its distributed social networking platform allows your product reviews to percolate out to other sites where those products exist. If your long-lost sister, for example, discovers one of these products through LinkedIn, she can view your recommendation right there.

Based on most of the services I’ve seen to date, including RunToShop, the implementation and user experience around social shopping still has a long way to go. In the meantime, keep this in mind the next time you’re shopping for the right social shopping service: will it provide the emotional spark you need?

This is the description of the presentation:
Social search has the potential to improve search practices beyond what is possible with traditional informational retrieval algorithms. Two different models of social search should be incorporated into enterprise and conventional search systems today. Collective Search involves aggregating social metadata, trends, and previous tags, bookmarks, or information shared by social networks. Collaborative Search, or question-answering, occurs when two or more participants actively engage in an information seeking task. Interactions include everything from replying to a one-time question to dually negotiating the query formation and relevancy of specific results to arrive at a shared consensus of best fit.

This talk will frame the relevant models of social search in the context of Brynn’s research, and discuss the potential benefits for both users as well as organizations. We will extend these trends and findings to concrete design considerations that we encourage system designers to consider in order to leverage social search capabilities within the enterprise.

Here are the annotated slides, also available on slideshare:

And finally we got a nice review in EContent Magazine!

Fittingly, the ESS West track ended on Thursday with “Designing for Sociality in Enterprise Search,” presented by Will Evans, director of experience design, Semantic Foundry and researcher and author Brynn Evans (no relation. The duo delivered a highly conversational presentation about social interaction design, or what they call “SxD,” in a truly interactive way. As a team, they explored the various stages or manifestations of social search and provided a graphic look into its potential impact in the enterprise, revealing ideas about a potential engine and how it might work; incorporating things like “friend filtered search,” “social scents,” and even a suggestion box that says something like “You seem to be having trouble, would you like to ask your network for help?”

With Google’s Social Search experiment, Bing’s integration with Twitter, and with Yahoo!’s partnership with One Riot—it’s clear that social search has both potential and momentum. But what will social search look like, and will it help us search better? And if it will, how?

I’ve written previously about how social search won’t replace traditional search, how social relevancy rank may be used to deliver good results, and why the concept of social search is a return to a familiar state rather than something to fear. Today I’ll get more specific with the three flavors of social search that will improve user search experiences.

Collective Social Search

“Collective Social Search” is similar in concept to the wisdom of crowds, in that search is augmented by trends shared on a network (a la Twitter Trends) or results ranked against the real-time buzz of a group. Why might this be useful? Well, in some instances, we can’t immediately find the information we’re looking for; and pooled, aggregated data from the collective may point us to new avenues that expand our discovery process.

As of yet, no major search systems are doing this very well — and we don’t know what type of interface would be optimal for sharing this information. The Cloudlet Plugin inserts tag clouds (based on keywords) into search results; but tag clouds are known to be more of a distraction than a utility. BingTweets has been touted as such a resource, but it really only offers Twitter and Bing results on two separate pages. OneRiot shows only collective data from the real-time stream, although it may be integrated with Yahoo! results soon. And we are still waiting to see how Google and Bing integrate the Twitter firehose into their traditional search results — as opposed to merely including them as additional document-like resources.

Equally important will be understanding when collective social data should be shared with users: while performing the search or after? And for which types of searches?

My research on search strategies begins to address this question. Collective guidance may be useful when users are exploring a search space, possibly because the search domain is not familiar to them (i.e. they lack knowledge of how to drill down to an answer), or because they are passively exploring a problem. I find myself doing this all the time when I prepare recipes to cook. I want to browse recipes from many different sources before I decide what my own recipe will consist of. I don’t have a specific recipe in mind (it’s not an urgent, active request), and therefore I don’t necessarily know when I’ve found what I’m looking for.

That said, it’s hard to determine from keyword strings how active or passive a user’s search is; i.e. it may be quite difficult to determine the type of search they’re performing or how far along they are in their search process (“exploring” or “narrowing”?). Furthermore, the utility of collective social data for mainstream consumers will be limited, mainly because it doesn’t come from trusted sources, unlike “friend-filtered social search” (see next section).

Friend-Filtered Social Search

“Friend-Filtered Social Search” is approximately what Google is doing with its Social Search experiment: providing social data that your peers, friends-of-friends, and wider “social circle” have shared. This data could appear alongside traditional search results (as with Google) or be exclusive results from within your peer network (as with TuneIn).

This is useful if your friends have shared relevant links, blog posts or tweets about a topic that you’re searching for. If you were gathering ideas about, say, “the future of the desktop,” you would see thought pieces, write-ups and links to projects from the main search algorithm, as well as stuff your friends are saying about applications they’ve encountered recently. If you trust your friends, they may serve as reliable filters, pointing you to relevant information.

The three major limitations of this approach are:

1. Your friends may have no archived social content that’s relevant (or available) to your query. Searching within your Facebook network quickly demonstrates this problem. For this reason, augmenting traditional algorithms with friend-filtered social data may be better, rather than relying exclusively on data from one person’s small exclusive network.
2. Current implementations are limited to keyword matching; whereas, searches that retrieve related posts based on topic, theme or timeframe might expose a wider set of results and combat the niche-social-network problem. This approach would be computationally harder than keywords alone, and exposing enough of the appropriate context remains a problem (see next item).
3. Understanding the context in which a post or link was shared is important. Without this, keyword- and even topic-matching might not convey to the user the relevance of a search result. Google provides limited context at the moment (showing only how you know a user, the source of the post and a short snippet). More testing is needed to learn how much and what kind of context is appropriate for social search content.

Similarly there is the issue of when friend-filtered social search would be relevant during a search. My instinct is that it will be useful throughout a search and for many types of searches (it is, after all, just another type of search result). This is critically different from collective social search and collaborative search.

Collaborative Search (aka Question-Answering)

“Collaborative Search” is when two or more users work together to find the answer to a problem. This could look like IM-based question-answering (a la Aardvark), Yahoo! Answers (which is relatively passive and asynchronous), or over-the-shoulder two-person search. In all of these cases, people speak to each other using natural language, which is incredibly useful for open-ended queries (e.g. “What is ‘design thinking’?”) or queries about unfamiliar domains (e.g. law, health, business, depending on your background). Such conversations, even not real-time ones, can assist people who don’t know the right keywords to use (what’s known as the ”vocabulary problem.”

My research has looked at the benefits of question-answering and at people’s processes and preferences during search. Many users report that they want to attempt to search on their own first, or don’t wish to interrupt their colleagues before they have given it a shot independently. This suggests that early social support should be passive (as with presenting collective or friend-filtered social data).

But later in the process, if the searcher gets stuck on a problem, they often turn to a colleague for help. If systems had a way of identifying difficult queries or search-process inefficiencies, they could offer more explicit social support to searchers. Perhaps the system could identify a domain-specific expert from the user’s extended social circle. Information that this person has shared could be presented to the user, or this person could be suggested as a resource to chat with or email (depending on availability and preferences).

It should be clear by now that these three flavors of social search are complementary. Each has its pros and cons and is appropriate for different kinds of searches and during different stages of the search process. A powerful “social search engine” would be “smart” by making use of all three, while also exploiting the value of traditional algorithms.

Photos by: Who Wants to Be?, Claudia Lim and brewbooks.