Mahendra Palsule is an Editor at TechMeme, and former Writer at MakeUseOf. com. He blogs at SkepticGeek.com and is @ScepticGeek on Twitter. The views expressed below are his personal views.

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.

TweetMemeOneRiotDigg, and many other social bookmarking services aggregate the actions of millions of people to surface the most popular services. Techmeme and MediaGazeradd 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?

(c) Mahendra Palsule

Contribute: If you have an opinion or business practice details to share with our readers, please do send across your contribution to nikhil AT medianama DOT com.