Who Wants to Be a Millionaire Lifelines As Search Engine Types

OK, this is a bit of a silly post, but for what it’s worth . . .

My wife and I are hooked on watching Who Wants to be a Millionaire (yes, it is still on TV, just not with Regis Philbin). For those of you who have never watched this show, contestants must answer trivia questions with varying degrees of difficulty. If a contestant isn’t sure about an answer, he can use one of three “lifelines” or types of help. The lifelines are:

• Ask the Audience: the user gets the collective opinion of members of the studio audience;
• Phone a Friend: the user calls a friend up and has 30 seconds to get his advice on the question;
• 50/50: The computer removes two out of four incorrect answers, so that the user gets a 50% chance rather than 25% chance.

What does this have to do with search, well think of it this way:

• Ask the Audience: this is the same concept as collaborative filtering or “discovery” search like StumbleUpon, LaunchCast, or Rotten Tomatoes.
• Phone a Friend: Similar to Mahalo, ChaCha – the results are based on a person’s knowledge – humans instead of computers.
• 50/50: Algorithmic search. An algorithm like Google refines results based on rules to come up with the correct answer. Of course, in Millionaire this is a random result, but the point is that a computer is making the selection rather than one or many humans.

The new version of Millionaire, by the way, also has an additional lifeline once the user gets to the $25,000 milestone – the user can “swap out the question” for a new question. You could argue that this is similar to Google’s rarely used “I’m Feeling Lucky” feature.

For what it’s worth, on Millionaire the effectiveness of the lifelines almost always follows this order:

• Ask the Audience is almost always right.
• Phone a Friend is highly variable and is either 100% or next to no value (my sense though is that the most successful phone a friend experiences occur when the friend on the other line just types the question into Google . . .);
• 50/50 is slightly over 50% (since the user has a bit of an idea in advance);
• Swap the Question rarely has any value.

Interestingly – at least in the current state of search – most people would say that the current success rate of different search models would be:

• Algorithmic Search (Google)
• Collaborative Filtering or Discovery
• Human Guides or Directories
• I’m Feeling Lucky

So, in other words, the game show application is almost exactly opposite to the search world application. Of course a game show isn’t the real world, but this does make me think that algorithms aren’t always the best way to find results. Imagine a search engine that truly could ask 100 random people the answer to a search query instantly – it could be pretty powerful. Maybe Millionaire has it right after all?

 

Six Search Technologies You Should Know About

The days of the one-size-fits-all search algorithm are over. As impressed as we all were with Google’s PageRank algorithm back in 2001, the results you’d get from that algorithm today would be laughable in comparison to the results we have come to expect from search engines.

Why? Well, in part because SEOs have gamed the original algorithm to death at this point, but mostly because search technology has gotten so much better since then.

Here, then, are six search technologies that will (or are) reshaping the search landscape.

1. Collaborative Filtering. Loyal readers may wonder why I don’t just change the title of this blog to “Collaborative Filtering Thoughts” since I mention this technology about once a post. Collaborative filtering is technology that matches your interests to people similar to you, best expressed in Amazon’s “People who bought this book also bought . . .” Many Web 2.0 applications are based on this principle, such as StumbleUpon, del.icio.us, and Flixster. I’m very bullish on collaborative filtering, simply because I believe that the “wisdom of crowds” can be far more effective than even the best algorithm for many types of searches (product reviews, restaurant recommendations, someday perhaps even dating!).

Examples of Collaborative Filtering: Collarity, Launchcast, Flickster, Amazon.com.

2. Personalization. As the name implies, personalization uses data a search engine has about you to serve more relevant results in the future. For example, if I continually do searches for “lake trout” and “fly fishing”, a personalization engine will likely conclude that my search for “laker” is not for a basketball team, but rather for a fish. Personalization has the potential to be very powerful, but it also comes with a price – privacy concerns. Ultimately, this technology will only work if consumers really trust a search engine to protect and honor their personal data.

Examples of Personalization: Google Personalization.

3. Semantic Search. Semantic search identifies similarities in words and phrases. Thus, if I searched for “telephone”, the search results might show me results that contained sites about “cell phones.” In the paid search world, this might mean that you buy the word “mortgage” on broad match, but end up getting matched with terms like “refinance” and “home equity” because the search engine considers these words to be semantically related to one another.

Examples of Semantic Search: Hakia, Yahoo “also try” results.

4. Clustering. Clustering, or clustered search, tries to categorize words or phrases into a taxonomy (or groups) of related themes. I might type in “auction” and a clustering engine would show me categories like “Online Auctions” and “Fine Art Auctions” and beneath each category I would find sub-categories like “eBay”, “Ubid” and “Sotherby’s.” Clustering is a great way to provide additional navigation options to users who might feel overwhelmed by the raw search results.

Examples of Clustering: Clusty, eBay search results.

5. Local Search. Local search can be described in two ways – either it’s interactive maps like Google Maps, or it’s geotargeting based on the IP address or user registration information. For example, as a registered user of Yahoo, Yahoo knows that I live in the Bay Area (I gave this info to them when I signed up, and I was actually honest). As a result, I get a lot of ads for local events, car dealers, and Bay Area real estate brokers. But even if I didn’t register, Yahoo could still have a good idea of my location by looking at my IP address, or by cookie-ing my searches.

Examples of Local Search: Google Local, Krillion.

6. Human-Edited Search Engines. As funny as it may sound, humans are making a comeback (here’s hoping I don’t see the top of the Statue of Liberty on my next beach walk . . .). As the number of Web sites multiple, it becomes harder and harder for anyone to filter out the all the noise and get to the good stuff. So rather than do the hard work yourself, why not farm out the effort to someone else? That’s the theory behind search newcomers like ChaCha, Mahalo, and really Wikipedia when you think about it.

Examples of Human-Edited Search Engines: ChaCha, Mahalo.

Phew, that’s a lot of different search options. Ultimately, I’m still throwing my hat in the ring with collaborative filtering, but you can bet that each of the technologies above – as well as many that have yet to be developed – will play a role in the future of search!