Mapping Startups & Services Filtering For Relevance In A Matrix

On September 2, 2010, in Social Web, by Mahendra Palsule

After looking at the different approaches to filtering for Relevance, I have been seeking a way to map them visually. There are many different startups competing in this space along with the giants, and a way to map them in a matrix would help us see the big picture of how the battle for relevance is evolving on the social web.

What are the fundamental ways in which these approaches and startups differ? These could form the axis around which we can then proceed to map them.

The Popular – Personalized Axis

Filtering either works by showing us the most popular stuff being shared online, or by understanding our individual preferences and surfacing personalized content. Thus, we have the following axis:

PopularPersonalized

The Serendipity – Search Axis

You either search for content or you see it serendipitously without seeking anything specific. Search is actively initiated by the user and is goal-driven, while serendipitous discovery is gifted with the user being passive at the receiving end. This gives us our second axis:

SerendipitySearch

The Filtering for Relevance Matrix (FORMAT)

We combine these two axes to form the backbone of our visualization. We then place different services within our matrix as per their core filtering approach. The result is the Filtering FOR Relevance Matrix (FORMAT) as seen below:

 

Format

Let us now look at each quadrant closely.

Popular – Search Quadrant

This is the simplest and oldest of all. Search powered by algorithms to surface most popular content online. This also includes other Twitter search services like Topsy. These services are powered by algorithms such as PageRank, PersonRank, Resonance, etc. to surface the most popular result relevant to a query.

This approach dominated the Web 1.0 era before the advent of the social web.

Popular – Serendipity Quadrant

Services in this category help you find the most popular content being shared online across different social networks. These were the next to evolve in the Web 2.0 era, beginning with social bookmarking services like Reddit, StumbleUpon, etc.

There is an element of personalization provided by many of these, in that you “follow” some users, but the motive behind such following is less to seek personalized content, more to seek trending, viral content.

Note how Digg is attempting to move from this quadrant to the personalized quadrant, and facing hurdles along the way.

Search – Personalized Quadrant

A breed of services has evolved around delivering personalized recommendations and content tailored for your needs. Hunch learns about you and acts as a “taste engine”, while Blekko allows you to personalize your searches with slashtags. Google is making forays in this space with its Social Search service, which tries to personalize search results based on your social graph.

Personalized Serendipity Quadrant

This is the hottest space where most of the competition is today.

Twitter Lists are personalized (created by you) and deliver fresh, serendipitous content relevant to your interests. Facebook Likes give you serendipitous discovery from your personal friends. Flipboard provides a social magazine based on your personal social circle on Facebook and Twitter. My6sense delivers new content using ‘Digital Intuition’. Vertical networks like Last.fm deliver music recommendations based on your individual taste. Personalized Twitter newspapers give you fresh content filtered by your social graph on Twitter.

Note how Datasift lies at the center of the matrix. This is because Datasift is a platform providing different filtering services and approaches. Developers may use the platform to develop different services and apps that can lie in any of these quadrants.

How does FORMAT help?

So what is the point of this exercise? Using FORMAT:

  • We see the big picture of how services providing relevance and filtering are evolving.
  • We see how personalized serendipity is the holy grail of the social web right now.
  • We see how different services relate to each other and who is competing with whom and how.
  • We see how identifying the target quadrant is important for any new startup in this space.
  • We see how users provide friction when a service tries to change quadrants (Digg).

If you are involved in a startup aiming to provide filtered, relevant content to users, which quadrant would you target? See how FORMAT helps?

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DataSift Curation Engine Aims for Relevance in Real-time

On August 27, 2010, in Social Web, by Mahendra Palsule

As I have said many times previously, if 2009 was all about the hype of Real-time, the future is all about capturing Relevance in real-time. Datasift has partnered with Twitter to get the full Twitter firehose and is building a platform to enable curation and filtering in real-time.Datasift

An introductory video about Datasift was posted in their first blog post, which didn’t reveal much about how the platform works. Now, uber-geek Robert Scoble has posted a video of an extensive discussion with Datasift’s founder, Nick Halstead.

Robert Scoble with Datasift founder Nick Halstead

This post is a summary of Datasift as discussed above concluding with my own thoughts.

The Basics

Twitter’s firehose at present has around 800 tweets/sec, or 70 million tweets/day. Datasift can filter this firehose using over 20 variables. Examples of these variables include:

  • Profile information like name, location, bio, number of follows, followers, lists, etc.
  • Text and language of tweets
  • Geo-location of tweets
  • Verified users
  • Source of tweets – web, Seesmic, TweetDeck, etc.
  • Number of Retweets
  • Whether tweet contains a hyperlink

Datasift is a rules-based engine that can filter this firehose using thousands of complex rules and provide a filtered stream in real-time within milliseconds. It is built using a Service Oriented Architecture and has an API.

The Rules

Rules can comprise of any combination of filters using the above variables. Rules can be combined and merged, or added and subtracted, into a single new rule. Stream outputs from Datasift using such rules can become columns in Twitter clients like TweetDeck.

Here are a few examples of how rules can be used:

  • Show me tweets containing “google” from users who don’t have “social media” in their bio, and who have more than 500 followers.
  • Show me tweets from my curated Twitter list of tech brands that have more than 100 Retweets.
  • Show me tweets originating from within a radius of 5 miles from the location of XYZ Conference that don’t have swear words, irrespective of whether their tweets contain the hashtag for the conference.
  • Show me tweets originating from Starbucks shops around the world, of users who are “Verified Accounts”, irrespective of what they’re about.

Datasift’s website is intended as a community website for curators and developers to collaboratively work on developing these rules. You can leverage rules created by others to avoid duplication of effort. Rules are classified with tags, and Datasift provides search, ranking and trending for easier discoverability of rules.

Partnerships for Influence Tracking and Sentiment Analysis

Datasift has partnered with PeerIndex and Klout to enable filtering using their influence and authority scores. It has also partnered with a firm for real-time sentiment analysis.

Thus, any of the above rules can be filtered further using such scores, and a stream of tweets with negative sentiment about a brand or product, combined with any other rules, can be monitored in real-time.

Alerts and Analytics

For esoteric rules that may provide a result infrequently, alerts can be set up. The example discussed is of any politicians from a Twitter list tweeting the word “scandal”. Developers can send these alerts as email, SMS, or notifications on smartphones.

The resulting streams from all rules applied by the engine are stored by Datasift. This data can be extracted, segmented, and analyzed later. For example, this can be used to track the performance of social media campaigns.

Relevance Filtering of Links

Datasift can use TweetMeme and other databases to check the links in tweets, and determine whether they are relevant to a specific topic. Not much details on how this is achieved, but apparently, Nick says that all sites are already classified into different subjects by Tweetmeme and other such databases.

Blekko-style Twitter Search

Datasift has developed a prototype of Twitter search along the lines of Blekko’s slashtags. Thus, along with your query text, you can use filters such as “/nolinks” to get tweets without links, or “/California” to get tweets originating from CA.

RSS Feeds

Compared to the massive volume of the Twitter firehose, the volume of RSS is minimal. Datasift plans to have their own PubSubHubbub server. Developers and third-parties can plugin any RSS feeds and use Datasift’s filtering rules to get an output feed.

Revenue Model

One option is free access to the stream with in-stream ads. Ads will be tailored and designed for the target form factor – desktop/mobile/tablet/etc.

Second option is selling data B2B for developers and brand companies, charged by volume of data consumed.

Prospective Partners

Datasift is seeking to work with startups like Flipboard, who are creating new ways for curated content consumption. This can also include any of the startups focusing on Relevance, such as TwitterTimes or Paperli.

My Thoughts

When I compared approaches to filtering information for relevance, I had suggested that the service most likely to succeed would be the one that supports multiple approaches and platforms. We can easily see that Datasift supports all platforms and several approaches like crowdsourced filtering, influence filtering, location filtering, etc. It is easily the most powerful relevance filtering engine I have seen yet.

The market of end-users for curated real-time content is at present unknown. Startups involved in creating pleasant experiences for consuming content have yet to find a monetization strategy. The degree of Datasift’s success from an end-user perspective is largely dependent on:

  • The creativity of developers and curators to create compelling experiences, and
  • How the monetization strategies of presentation apps fare and how Datasift is able to work with them

Nevertheless, with the amount of content being created online growing exponentially, curation and filtering will eventually become necessities for any social media client. It is just a matter of time.

I also see a bright future on the B2B front. By partnering with influence and authority tracking companies, combined with sentiment analysis, Datasift may already be a compelling choice for brand monitoring and social media reputation tracking.

Lastly, thanks to Robert Scoble and Nick Halstead for the interesting interview.

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Comparing Approaches to Information Filtering for Relevance

On July 15, 2010, in Social Web, by Mahendra Palsule

In a previous post, we looked at the big shift From Numbers To Relevance. There are dozens of apps/sites that are focusing on filtering information today, but which of them will succeed?

To attempt to answer that question, let’s first look at the different approaches employed by such apps/sites today in the search for Relevance. This is a topic that is usually the subject of scholarly research papers in academia; this is only a layman’s overview.

The different approaches I observe are:

  • Algorithmic Filtering
  • Filtering Based on Social Graph
  • Human Filtering
  • Crowdsourced Filtering
  • Shared Sources Filtering (Meta)
  • Influence Filtering
  • Social Search
  • Location Filtering

Algorithmic Filtering

If you tell us what you want or like, our software can show you what you will like.

Google Suggest

The predominant use of algorithmic filtering is in web search, where Google has dominated and driven the web economy for the past two decades. You search for something and Google’s search algorithm filters billions of web pages to find the most relevant results.

Google also uses algorithmic filtering to suggest items in Google Reader’s “Sort by Magic” feature.

Pros: Highest relevance when searching for information.
Cons: No serendipity. Only useful for goal-oriented task of search. No personalization (search engines typically unaware of demographic information).

Filtering Based on Social Graph

If your friends like it, you’ll probably like it too.

This is the dominant approach being used today by various apps and websites. For example, Facebook uses the EdgeRank formula to determine what to display in your news feed:

edgerankform2

The key driving factor is the affinity score between you and the source.

Google also uses this approach when recommending posts in Google Buzz.

Most of the apps listed in my previous post, as well as the new Digg, use this or a similar approach that employs your Twitter or Facebook friends to recommend items.

Pros: High serendipity. Helps being “in the know”, a socially cool factor. Higher personalization.
Cons: Relevance depends on social graph, which often is not optimized for relevance, as Kevin Anderson noted.

Human Filtering

I trust a specific person to share all of the good stuff I like to know.

Some people make it a habit to go through news items every day and share what they deem to be the most significant ones. Others begin relying on them as trusted news sources.

Pros: High serendipity. Easy to use. Quickly become part of social circle of an influencer.
Cons: Unreliable. Susceptible to preferences and agendas of other people.

Crowdsourced Filtering

Quickly see what’s most important to know.

TweetMeme, OneRiot, Digg, and many other social bookmarking services aggregate the actions of millions of people to surface the most popular services. Techmeme and MediaGazer add human curation to the aggregation of thousands of websites to surface the most important tech and media stories.

Pros: Be up-to-date with the most important/popular need-to-know information.
Cons: No personalization. Popular doesn’t always equate to relevant.

Shared Sources Filtering (Meta)

If you read from sources similar to someone else, you’ll probably like their other sources too.

Facebook uses this approach to suggest new Fan Pages that you may like because your friends like them. Google Reader also uses this to recommend new RSS feeds. Toluu also compares your subscribed RSS feeds with other users to help you discover new feeds.

GR Recommendations

Pros: Useful for discovering new sources in social networks.
Cons: Filters sources, not actual news items, hence limited in scope.

Influence Filtering

Only read what influential people are saying/sharing.

This approach uses influence scores of sources to filter the news feed. An example of this is HootSuite, which uses Klout to let you filter tweets according to their Klout scores.

Klout Filtering

Pros: Flexibility. High serendipity. Helps being “in-the-know”.
Cons: Influence metric is unreliable. Currently only available for real-time feeds like Twitter.

Social Search: Algorithms + Social Graph

Let your social circle find the most relevant results for you.

Social Search uses a combination of algorithms and social graph to find relevant results.

Social Search

Pros: High relevance. Combines goal-orientation of search with serendipity of social. Very useful for news items from recent past.
Cons: Requires searching. Lesser utility for fresh, real-time news.

Location Filtering

If we know where you are, we can help you find relevant results.

Location is a treasure trove for relevance. As the mobile web explodes, services that provide information about nearby businesses or friends are gaining increased adoption.

Pros: High relevance. Can be serendipitous with real life impact.
Cons: Privacy concerns. Limited in scope.

Conclusion: Which Approach is the Best?

None. Relevance is dependent on the requirements of an individual at a specific moment in time. These requirements change from time to time and from person to person. There is no killer approach to relevance.

Which app or service is likely to succeed? I think the following factors will make a difference:

  • Support for multiple approaches
  • Flexibility of degree of filtering
  • Number of Mobile Platforms supported
  • Next Step: What can you do with the info? (e.g. Siri lets you take actions)

What do you think? Are there other approaches that I missed? Which other factors matter?

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Predicting Tech News in 2015

On April 7, 2010, in Social Web, by Mahendra Palsule

Last month, Stuart Miles, founder of the gadget and tech blog Pocket-Lint, asked me to contribute to its feature on “FutureWeek”:

What gadgets will we be using in 2015, where will Augmented Reality take us? What about robots, gadgets of the future, super-fast internet speeds, cars, materials of 2015 and much, much more.

The entire set of stories make excellent reading with insights from thought-leaders around the web. Apart from gadgets, there are other posts on what to expect from the semantic web and how we will consume content in 2015. Being in the technology news business, my thoughts were included in What will be the big tech stories in 2015.

I would like to elaborate on my thoughts here. I must say that these ‘predictions’ are nothing but a reflection of my hopes as well as fears. Further, I sent these on 21st March, after which there have been some interesting developments.

Facebook will not become AOL 2.0. To remain competitive, it will be forced to interoperate with other networks.

There has been discussion on this issue time and again on the web. I personally think the web is resilient to any attempts to dominate it in the long term. I also think the team working at Facebook is wise to learn from the past.

Social Networks will no longer be "places" on the web. Instead, your "social graph" will follow you on the web.

  1. You will control your social graph – choose and add from among different networks – Facebook, Twitter, Google, Windows Live – which will all be interoperable using an open standard. This evolution of social networking will be similar to that of Instant Messaging, where the open XMPP standard became popular, achieving interoperability to an extent.
  2. Rather than social networks wanting you to visit and spend time on their site, they will compete to become an inseparable part of the time you spend online, whether mobile or desktop.
  3. The social graph that follows you will help personalize and customize your browsing experience for everything:
  • Primary Content on websites – for example, which headlines/articles you see
  • Ads – tailored to your social identity and graph
  • Search Results
  • Which friends of yours are online, shown within your browser
  • Reactions/comments from your friends optionally shown for the web page you’re visiting

All the above is pretty self-explanatory. We are already seeing glimpses of this in Facebook chat, Google Sidewiki, and so on. Interestingly, one week after I sent these, there were reports of Facebook planning a “Like” button for any content anywhere on the web, and launching a Meebo-style persistent toolbar. Imagine my reaction when I saw these developments! :)

Websites will personalize according to your social graph using mechanisms like Facebook Connect, Google Friend Connect, Twitter Following/Followers graph, etc.

This is an ongoing trend I see towards a personalized relevant web. Again, a week afterwards, there were reports of Facebook sharing your profile data with external sites, so that these sites will tailor content for you.

I had also pointed out Facebook Connect being a mechanism for precisely this goal, when I wrote in January about Facebook’s non-portable data-portability. Marshall Kirkpatrick now points it out as well: there’s a big difference between opt-in and opt-out “data portability”.

Anti-trust legislation will be a major threat to Google’s dominance both in US and EU. "Will Google split up?" will be a question discussed in the media.

This is speculation. Google’s expansion into virtually every aspect of technology have already brought it under the scrutiny of anti-trust authorities.

Apple’s mindshare will start to decline. As Steve Jobs approaches retirement, questions will be asked of Apple’s survival.

Two weeks after I sent this, the question of what happens after the iPad and after Steve Jobs has been asked. I have my doubts about Apple’s innovation and competitive capabilities in a post-Jobs era, but would be happy if they’re proved wrong.

Privacy and Anti-Piracy will continue to make headlines.

  1. On Privacy: We would move to a public-by-default, private by opt-in model.
  2. On Anti-Piracy: Anti-Counterfeiting Trade Agreement (ACTA) will be in place, along with a global version of the DMCA.

These are my fears and they are very real. ACTA negotiations are making progress, and includes a global version of the DMCA. The politicians behind these negotiations may not understand technology and the people who understand the technology are busy writing about other topics that get their blogs more traffic. It’s also a case of those who matter, don’t understand; those who understand, don’t matter.

Do read the other pieces in FutureWeek. And thanks to Stuart for the opportunity to share my thoughts!

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The Evolution from Numbers to Relevance

On March 29, 2010, in Social Web, by Mahendra Palsule

Social media and Businesses on the web today are driven by the numbers game – of traffic, page views, and follower numbers. But the trend I foresee is:

The web is evolving from a numbers model to a relevance model.

Paradigm Shift: What is the Relevance Model?

Historically, monetization driven by CPC/CPM based advertising has led to websites and marketers focusing on page views and traffic. This is partly the cause of social media being spammed by internet marketers, ranking algorithms being gamed for traffic, and so on.

Numbers Model

Relevance Model
# of Followers Context-driven Lists
# of Clicks # of Interactions
# of Page Views # of Returning Visitors
# of Ads Displayed Time spent on site
# of Ads Clicked # of Subscriptions Gained
Obnoxious Ads Relevant Ads
Influence Management Dynamic Social Graph
Sharing Orgy & Noise Curation
Information Overload Filtered, Relevant Information
Traffic Economy Attention Economy
SEO and SMO Personalization

 

The above table lists different attributes of this paradigm shift. The “Influence Management” entry links to a post by Mia Dand who describes how leveraging social media is often about using a handful of influencers (read: with large follower numbers) to spread your message. Contrast that with Dynamic Social Graphs as described by Robert Scoble, where influence is dynamically determined based on relevance and not just numbers.

The Facebook Kingdom was built on Relevance

The king of the social web, Facebook, was not built on numbers, but relevance.

The success of Facebook and why it has garnered over 400 million users is because it grew on a base of real-life friends who were relevant in the users’ social circle. Other networks have failed to challenge Facebook partly because they have tried to go the other way around – from numbers to relevance.Bullseye

Prioritizing numbers over relevance is putting the cart in front of the horse.

Even as its explosive growth continues unabated, Facebook has not compromised on relevance. It knows that its success depends on users finding relevant content on Facebook and is willing to sacrifice advertising revenue to avoid becoming irrelevant.

I’ve touched upon various aspects of this ongoing theme while tracking the Google vs. Facebook race towards a relevant real-time. It’s becoming increasingly apparent that relevance wins over real-time.

While Facebook has never been in the numbers game, other networks like Digg are now moving from the numbers model to the relevance model.

Relevance vs. Real-Time in Location Check-ins

Consider the hottest trend of check-ins via location services, such as Foursquare or Gowalla.

When I check-in at a restaurant, the real-time checkins of my friends in other places is irrelevant. What is more important and relevant to me is the tips from my friends who have checked-in at the same place as I am right now.

In all cases, my friends are relevant in real-time only if they are at the same location as me. My other friends NOT at the same location become irrelevant.

Relevance wins over real-time.

The Mobile View

While mobile internet access grows, the screen of mobile devices remains constrained by its form factor. This is a major factor driving this evolution. If the content on your screen is constrained by its display, it had better be relevant.

Lifestreaming and Aggregation

As I discussed extensively in my post on why Google Buzz should not simply be yet-another-aggregator, lifestreaming and aggregation have failed to take off and gain mainstream adoption. The reason is simple – lack of relevance.

Which is why, it is personally heartening to see the champions of lifestreaming and aggregation turn their focus towards relevance and disaggregation.

Startups focusing on Relevance

Quite a few startups are hoping to capitalize on this trend:

  • my6sense – recently introduced an ‘Attention API’ allowing publishers to deliver relevant content to users
  • Cadmus – auto-filters Twitter/RSS streams by relevance
  • Knowmore – surfaces relevant stuff from Twitter/Facebook
  • TwitterTimes – personalized aggregation from Twitter
  • FeedTrace – personalized aggregation from Twitter
  • VictusMedia – ‘Intelligent Media Manager’
  • MixPanel – tracking what I’ll term “Relevance Analytics” for publishers
  • Cascaad – personalized news stream based on social graph from Twitter/Facebook

From Around the Web

Here are related posts that further elaborate on this evolution:

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