Youlicit – Your Personal Discovery Engine!

February 26, 2008

As we release a new & updated version of the product, the biggest change you will notice is a shift in focus from being an item-based recommendation engine (giving you sites that others have liked based on the site you are on) to a more personalized discovery engine (providing you with relevant and compelling content based on your interest at any given time). We are confident that this is taking Youlicit one step closer towards our goal of connecting users to the information they care about in the most effortless manner.

Among the many improvements and changes included in this release, what I would like to highlight here is the fact that a toolbar download is no longer required to receive Youlicit’s personalized recommedations! You can now link your Youlicit account to your Digg, Delicious, StumbleUpon, or other social bookmarking account and your Youlicit profile will automatically update itself as you use your other accounts. Your youlicit recommendations can now be accessed in many different forms:

  • as an RSS feed that can be seen from your RSS reader
  • on your Youlicit page (as it has been traditionally)
  • with a “Discover!” bookmarklet that can be dragged onto your browser
  • right on your blog using the Youlicit blog widget
  • your Facebook profile (as we improve and release an updated Facebook app)

This seemless interaction allows users to receive Youlicit’s recommendations without any additional download or change in behavior. By continuing to use your third party web services of choice, you can receive relevant and compelling web content where your normally go to get their information.

Users who already have the toolbar or don’t have accounts with any of the services supported, can still download and use the Firefox extension and continue to receive and recommend great websites.

We urge you to check out the new site and service and as always welcome any feedback you have.


Search and Social Networking

November 5, 2007

With Hakia releasing a new social networking feature on their search engine (see entry in Read/Write Web) and Google sponsoring OpenSocial, clear strides are being made to integrate the search and social networking spaces. To many, including us, this almost begs the exclamation “About time!”

Social networking, for the most part, has been built around imitating (and hopefully positively effecting) the physical relationships we already have in the real world (think LinkedIn, Friendster, Facebook). Only very recently has progress being made in linking people based on their interests (think StumbleUpon to a degree and third party applications like uPlayMe, etc) and now with the recent announcement of Google’s OpenSocial we expect this to accelerate the development of a richer, more meaningful social networking experience. There is still significant work that needs to be done in terms of connecting us to people we ought to be connected to. This is why we at Youlicit are excited about the “Meet Others” feature on Hakia which is being called a “peer-to-peer transactional platform” and are further building upon this concept at Youlicit.

Richard McManus poses an almost rhetorical question on his blog entry asking if search and social networking go together. We believe that the purpose of a search tool is to help you find the information you need with the least effort possible (see Relevance/Effort metric). To this end, if there is someone who has, and is willing to share, the information or expertise you are looking for, then what better medium to connect you to him than that in which you already go to find your information. Granted there is a spectrum of modes that different users fall into depending on their personality types (and time constraints) ranging from solitary to the very social (as pointed out by Charles Knight in his blog). In the end everyone can and wants to benefit from accessing the information (and people) they need as quickly as possible. This is why we are including “Related Users” for every query you perform on Youlicit (this feature is soon to come and can currently be seen on your Personalized Recommendations and User pages). We are using this as a base to build out a social networking aspect to our website recommendation service.

As you read this, we are working hard to better determine what users are interested in as well as allow users to share with others what they are recommending on a certain topic. The end goal is to become an enabler of collaboration between users to better facilitate the discovery and sharing of information. Building a social network based on your real world relationships with people you already know can help improve and extract more value out of those relationships but isn’t the most effective means to introduce you to other people you ought to know. A higher value social network connects you to people who share your interests and can help you not only discover the information you need quicker but ultimately increase your productivity and introduce you to more “meaningful” resources in your area of interest (see Expert Systems entry).

This is obviously not an easy feat to accomplish (otherwise it would have already been done!) and there are many hurdles that need to be crossed. How do you learn a user’s interests while safeguarding and protecting his right to privacy? How do you maintain the credibility and quality of such a “transactional platform” (i.e. how do you prevent unwanted information such as spam from diluting the quality of the service)? How do you enable varying levels of collaboration (from direct synchronous communication to asynchronous communication) with minimal distraction and effort from users? How, if possible, do you most optimally monetize such a transactional platform that will incentivize further collaboration?

The creation of such a platform inherently requires the cooperation of users (and of course technology) to make it all happen but we are confident that this is possible and have no doubt that a need for such a platform exists and must be met. As we develop and roll out this platform, we would love to hear your thoughts on this matter and get your feedback on what you would like to see on such a platform.

Facebook flyers experiment – Pt. 2

October 12, 2007

(See Facebook Flyers Experiment Pt 1 for background info)

After running the facebook flyer campaigns for a week, the results are out. To recap, the five campaigns were:

  1. All high school students (ages 13-19) with $0.05 per click.
  2. All high school students (ages 13-19) with $0.03 per click
  3. All people between the ages of 17-40 who live in the top technology cities as identified by Wired and some others (Seattle, San Francisco, LA, Austin, Orlando, New York, Boston, Philadelphia, Washington DC, Pittsburgh & Chicago)
  4. All college students attending some of the top technical schools (MIT, Stanford, Harvard, Princeton, UPenn, Columbia, Cornell, Carnegie Mellon, Illinois, U. Texas, U. Maryland, Georgia Tech, Cal Tech, Berkeley, UCLA & Penn State).
  5. All college students majoring in Engineering and otherwise technical majors (engineering disciplines, computer science, web design, etc)

As expected, most impressions occurred in campaigns 1 and 2, high school students, being these were the least specific, hence most probably the largest, user group targeted. What was also interesting to note was that there was a negligible difference in impressions between $0.03 and $0.05 campaigns (6.35% more impressions on the 0.5 cents per click) indicating that the demand for targeting these groups is less than the supply (i.e. number of impressions being demanded are far less than the number of potential impressions). In an ideal world, the number of impressions for the $0.05 per click campaign would be 40% more.

The second most impressions came from college students in technology majors, followed by adults in “the top technology cities” and then college students in the top technology schools. Again these are directly related to the specificity of the groups targeted.

As for the click through rates? They were low, atrociously low. The most clicks for any campaign were a whopping 1 click! The table below breaks down the CTRs for each campaign:

Campaign Impressions CTR (%)
1 5,904 0
2 5,551 0.018
3 894 0
4 852 0.117
5 1001 0.0999

Clickthrough rates of 0% to 0.1% were about as low as predicted. No conclusive statements can be made about which campaigns were better targeted due to the incredibly low number of clicks.

Being avid facebook users ourselves, we rarely find ourselves clicking on such flyers. In analyzing our own actions, we hypothesized some possible reasons to explain these poor click through rates:

  1. Facebook users are rarely in search for “external” information (information not available within Facebook) as opposed to say when one is searching on Google.
  2. Users find Facebook content far too engaging to click on a link that will direct them away from the site.
  3. The placement of the Facebook flyers is not at an optimum place on the pages.
  4. The flyer we created failed to capture the interest of the audience.

To perform a more rigorous study, one would need to run these campaigns for a much longer time than just a week. However, it is hard to imagine the CTR’s being significantly higher if the campaigns were to be prolonged (see Mashable post). It would be interesting to examine how using more captivating flyer designs (specifically targeted to each user group) would affect the click through rates, if at all. Needless to say, we are exploring other possible ways in which we can tap into the Facebook userbase as a means to generate traffic. The Youlicit team would love to hear from you if you have fared better than us, in terms of CTRs, with Facebook Flyers or are interested in sharing ideas to better target Facebook users.

On a side note, for all campaigns, the highest impressions occurred on a Friday, Saturday and Sunday – on average 86.8% of the total impressions – highly indicative of Facebook usage habits.

Facebook flyers experiment – Pt. 1

October 4, 2007

Facebook recently launched an updated version of their Flyers product called Flyers Pro. The main difference between this and the now Flyers Basic product is that advertisers can now pay per click instead of paying per impression.

In theory, advertising on Facebook has immense appeal. Given its incredible user base (now over 30 million active users) and extremely high visitor-return frequency (ranking second in visits/visitor) it is an advertiser’s haven. With Facebook’s improved targeting options – you can now filter on keywords (“Beatles”, “Lord of the Rings”, “iPhone”), countries, cities, age, workplace (“Google”, “Goldman Sachs”) and colleges – advertisers can now (theoretically) target their ads much better and reach the exact demographics they are looking for. Social networking sites, however, are infamous for their abysmal click-through rates (ranging from 0.01% to less than 1%). Unheeding these statistics, however, we decided to try out this new platform.

To test out the new Flyers platform, we created several campaigns to see:

  1. what demographics get us the most impressions for a given cost per click (CPC), and
  2. what demographics have the highest click through rates (CTRs).

The first question is to gauge the current demand of advertising to certain demographics on Facebook (independent of product and creative) and the second question is for us to see which Facebook demographics Youlicit appeals to the most (highly dependent on the ad, the placement of the ad, the product being advertised and the audience). The different demographics we targeted were as follows:

  • All high school students (ages 13-19)
  • All college students majoring in Engineering and otherwise technical majors (engineering disciplines, computer science, web design, etc)
  • All college students attending some of the top technical schools (MIT, Stanford, Harvard, Princeton, UPenn, Columbia, Cornell, Carnegie Mellon, Illinois, U. Texas, U. Maryland, Georgia Tech, Cal Tech, Berkeley, UCLA & Penn State). Nothing personal if your school isn’t listed, this was just a random sampling.
  • All people between the ages of 17-40 who live in the top technology cities as identified by Wired and some others (Seattle, San Francisco, LA, Austin, Orlando, New York, Boston, Philadelphia, Washington DC, Pittsburgh & Chicago)

We created this flyer to be used on all the campaigns:

Facebook Flyer

Given our current download rates per visit on Youlicit, we set our maximum costs per click to $0.03 in order to keep our user acquisition costs to under $1/user (however we did set 2 campaigns for the high school demographics to compare number of impressions for CPC’s of $0.05 and $0.03). We launched these campaigns this week for a period of 7 days and will see how each one fares after that. Stay tuned for the results…

(see the results here)

What Comes After Google?

October 4, 2007

Question: What Comes After Google?

Yahoo just released a new Search Assistant feature this week (TechCrunch) (Read/WriteWeb). Ask has been trying a new interface lift for a while (TechCrunch) (Read/WriteWeb). While these are all very nice incremental improvements to search, are they enough to supplant Google? Do they tackle the fundamental problem of information retrieval in a paradigm shifting way? The answer is probably not.

Now imagine several years into the future. Will you find information in the same way in the future as you do today? Again, probably not.

This may sound like an obvious “duh”-ism, but its ramification certainly is not. As unfathomable as it may seem, Google, as we know it today, will probably not be how we find most of our information in a few years. Since Youlicit is an information retrieval company, we had to ask ourselves, “If not Google, then what?”

What is the logic that dictates the evolution of information retrieval paradigms?

Evolution of Information Retrieval Paradigms

To answer this question, we first plotted the different paradigms of information retrieval on a timeline. If we can figure out what the axis of this graph represents, then we should be able to predict which new solutions will succeed and which will not by simply identifying the solutions that maximize the metric along this axis.

If you’re a start-up, this understanding can guide you in building a successful innovative product. If you’re a venture capitalist or technology evaluator, this insight gives you a criterion for determining which technologies to invest in and which ones will fade away as fads of the day.

Evolution of Information Retrieval Paradigms

After plotting them on a timeline, we then explored the three major paradigms of information retrieval:

  1. Manual Organization
  2. Algorithmic Search
  3. User-Generated Recommendations

Manual Organization

Information retrieval, during its infancy, started off as a very rigid and structured process. Those who remember Gopher or Jerry and David’s Guide to the World Wide Web (later known as Yahoo) know how attempts were made to organize sites into a pre-determined hierarchy. However, as the number of web sites exploded exponentially, manually organizing sites into structured directories became practically impossible:

“A universal ontology is difficult and expensive to construct and maintain when there involve hundreds of millions of users with diverse background. When used to organize Web objects, ontology faces two hard problems: unlike physical objects, digital content is seldom semantically pure to fit in a specific category; and it is difficult to predict the paths, through which a user would explore to discover a digital object.”
Clay Shirkey

Algorithmic Search

Too many sites to categorize? No problem. Algorithmic search to the rescue. Web search engines, such as Altavista and Google, arrived and allowed the web to grow in its chaotic unstructured way while still providing a level of organization in the form of keywords. Now instead of having to know the correct directory hierarchy, users only needed to know the keyword combination (and page number) for sites they were looking for.

Counter to intuition, search engines actually decrease the relevance of individual results as compared to those in a manually organized directory. A hand-picked set of results are always better than an algorithmically generated set of results. However, since search engines have a much greater coverage of the Web, the average relevance of search results from a given set of topics is usually better than the average relevance of directory results on the same set of topics.

The other improvement made by search was the replacement of directory hierarchies by keywords as the primary recall mechanism. While still not perfect, guessing and checking keywords took a lot less effort than guessing and checking hierarchies. Seach engines effectively decreased the recall effort.

User-Generated Recommendations

Recently, we’ve witnessed the niche adoption of tagging, voting, stumbling and other “user-generated relevance” as a means of finding information. Why? It’s because they improved something along either the average relevance dimension or the recall effort dimension.

Take and Digg for instance. In the scope of technology related content, the average relevance of results from these folksonomy sites is better than from search engines because these folksonomy sites have been able to increase coverage by effectively crowdsourcing an easier manual organization process.

StumbleUpon went the other route. Instead of improving average relevance, it decided to reduce the recall effort from guessing and checking keywords to a one-click no-thought “stumble.” In doing so, it did something ingenious: StumbleUpon removed the world’s most scarce resource from the information retrieval process… human thought.

Answer: The Solutions that Maximizes the “Search Metric”

As well as they’ve done, both kinds of user-generated recommendation services are plateauing well before crossing the chasm into the mainstream market. Why? We think it’s because they’ve only focused on a singular improvement, either average relevance or recall effort, but not both.

In order for an information retrieval solution to penetrate into the mass market, the solution has to take a dual approach. It has to concurrently maximize average relevance and minimize recall effort. It’s simply a matter of optimizing the Average Relevance / Recall Effort ratio, or as we like to call it, the “Search Metric.” The solution that does this best will probably gain the most mindshare and supplant algorithmic search as the primary mode of information retrieval. And that is what comes after Google.


Does this imply that algorithmic search will become extinct anytime soon? Absolutely not. It just means that more and more people will find larger percentages of their daily information through means other than search. Our bets are on online “word of mouth” or user-generated recommendations.

Evolution of Information Retrieval Paradigms

We’ve builit Youlicit with this assumption at the core. Youlicit is a “word of mouth” or recommendation engine (as opposed to a search engine). We’re trying to maximize the Search Metric by combining user-generated relevance with one-click no-thought recall. We want to improve the information retrieval landscape by enabling the average user to harness the wisdom of the crowds with very little effort. If you’re as obsessed with this problem as we are, we’d love nothing more than to hear from you!

— The Youlicit Team

Search vs. Recommendations

September 12, 2007

In an interesting blog entry by Seth Godin, he states:

“The fact is that search engines are very good at fairly simple searches, and very good at finding information about single products, services, people and ideas. But they’re terrible at connections, at rankings, at horizontal results… They can’t help me find six products that are viable alternatives to something that was just discontinued.”

These are precisely the problems we are trying to address at Youlicit. Our primary focus is drawing connections between horizontal results, or to put it more plainly providing “recommendations” on topics, through user provided data.

While Google and Yahoo! are “search” engines, Youlicit is a “recommendation” engine.

What’s the difference between search and recommendations you ask? Search is when you precisely know what you are looking for, whereas recommendations are when you aren’t entirely sure and would like some guidance on where to go and what to see. A great example is visiting your local department store. Search is the equivalent of going to the store and saying, “I’m looking for a navy blue dress shirt with thin vertical stripes.” Recommendations are when you walk to the mens department, pick up a blue shirt and say, “Can you show me more like this?”

As applied to web content, when you can accurately guess the keywords that are likely to occur on the pages you’re looking for, use search. But when you’re not quite sure how to describe what you’re looking, but you know it’s related to what you’re currently looking at, that’s when you Youlicit More recommmendations!