Algorithm + crowd is not enough
In the last decade, two kindred powers ruled the online world: the Crowd and Algorithm. Collective “users” of the Internet (Crowd) create content, click and vote, while mathematical equations introduce scalability and the ability to search through this huge data array (Algorithm).
Like the moon over the ocean, the interaction of these two forces creates waves of popularity (and oblivion) on the Internet. Information is more accessible, useful and egalitarian than ever.
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But over time (at least to me) the weaknesses of the “algorithm + crowdsourcing” system became visible. The next revolution seems inevitable.
Let's start by looking at some examples of crowdsourcing, algorithmic progress:
• Netflix - we look, put ratings and reviews. Netflix recommends (they even get rid of their algorithm ).
• Amazon - we choose, buy, set ratings and reviews. Amazon is analyzing the data until it knows exactly what to recommend (obviously, this is a critical part of their business ).
• Expedia - we fly, rent rooms, sleep and rest. Expedia machines analyze, set prices and calculate how to get the maximum profit.
• Google - we create content, quote others and exchange links. Google crawls, sorts, issues and ranks (based mainly on the human-generated link graph ).
• Facebook - we are friends, we update and like. Facebook is building the perfect system for targeting content and advertising.
• Reddit / Digg / StumbleUpon - we offer themes and vote for content. They accept them and determine how long the topic should hang on the main page and for whom it will be visible.
• Yelp - we have dinner, go to places and shop, leave reviews. Yelp classifies, ranks and recommends.
We are not completely dependent on these algorithms, because we have the opportunity to dig deeper, look further and collect more data for decision making (and often we do). But, since machines make assumptions about what we need, it is easier to use the default solution than to look for an alternative way.
Compare this with alternative methodologies before the Internet era:
• Video rental - a cassette for viewing helped select the sections “personnel recommendations”, “critics choice” and “award nominee”.
• Journal of technology (Consumer Reports) - experts analyzed and tested the product from all sides, and then gave ratings and recommended the best, from their point of view.
• Travel Agents - travel booked by professionals who have unique access to databases with information about tickets and hotels.
• Restaurant Guide - such directories included restaurant ratings and reviews from anonymous appraisers from dozens of cities.
• Personal recommendations - when there was not a single reference resource, friends, relatives, colleagues and various service personnel (in-house concierge, helpline) could offer their options so personal, relevant and valuable that this methodology is often used even today.
Understand me correctly, I believe that in most cases these predecessors of the modern approach “algorithm + crowdsourcing” cannot be compared in quality, reliability and utility. Their range was often too limited, sometimes distorted by commercial gain, and at times simply wrong.
But these predecessors had real advantages - the main thing was that everyone could understand what the recommendation was based on. Compare this with the mystery of our “algorithm + crowdsourcing” services.
Why is this page displayed first by a Google search? Why does one link stay on the Reddit home page for hours, and the other with the same number of votes disappears in a few minutes? Why does Facebook show me ads to work in Comcast Support? Why does Amazon recommend buying whole milk with this homemade tank ?
If we do not understand the reason for such recommendations, can this reduce our confidence in the future recommendations of these services?
Fred Wilson recently wrote an interesting story about why we should not invest in what we don’t understand:
... the venture capital industry is filled with investors seeking profit. And some of them do not understand what they are investing in. A few weeks ago, I received a call from a private investor who wanted to invest in one company from our portfolio. He asked a few questions about the company, and it became quite clear that the person did not fully understand the essence of its activities. But then he offered his investment. That scares me.
I have recently been visited by some foreign representatives, many of whom invest billions of dollars on behalf of their bosses. They all want to get into our funds and our deals. When I ask about the reasons, they cannot even formulate a convincing argument about the economic potential of a social network. But they see a profit and they want it too. That scares me.
I believe that for some people it is equally difficult and stupid to trust recommendation services, whose algorithms they cannot understand. These same people can turn to for recommendations to sources that they understand, and the results of which they can infer.
My point is not that Google, Netflix, Amazon, Yelp, or anyone else is doomed to failure. But I believe that there is a business opportunity for entrepreneurs, websites and companies to add an editorial component to the “algo-crowd” paradigm.
Many startups are already acting in this vein.
• Quora / FormSpring / StackExchange - unlike Google answers or even Wikipedia, where the order of results or information is unknown and free from responsibility, modern Q + A systems have put power in the hands of the crowd, algorithms and experts .
• Techmeme / Memeorandum / Mediagazer - a crowd of bloggers create content and quote each other. The algorithm compiles and sorts information, while curators ensure quality and relevance.
• Alltop — A collection of feeds selected by industry under the supervision of Guy Kawasaki. At the moment there is no algorithmic element, but I think it cannot last for so long.
• Oyster / Raveable - Travel sites are notorious for too many dubious comments and unreliable comments of suspicious quality from commercial partners, financially motivated affiliates. Oyster acts by sending editorial experts to hotels to write their own reviews, and then using user data and algorithmic ranking / optimization to help readers make the best choice. Raveable combines lightweight editorial oversight with powerful data mining and a sorting algorithm to help you find a hotel.
• Groupon / LivingSocial / Gilt Group - Sites with coupons and offers on the Web for about ten years, but these three (and a lot of similar ones) revolutionized the industry and become one of the most famous brands online thanks to social features (crowdsourcing marketing and advertising proposals), algorithmic filtering for the personalization / localization of proposals and editorial oversight (so that the deals they offer are truly worthy).
• TheSixtyOne / Pandora / Last.fm / Spotify - Music recommendations are getting better, and the latest generation of such services is a mixture of algorithms, crowdsourcing and editorial choices that deliver symphonic delight to many users.
I think we will see even more examples of this approach in the future, perhaps even on the websites / services that I mentioned at the very beginning (Yelp, Amazon, Netflix, etc.). I am sure that algorithms and crowdsourcing can be made stronger with the addition of a third pillar - confident, benevolent editors.
Like the moon over the ocean, the interaction of these two forces creates waves of popularity (and oblivion) on the Internet. Information is more accessible, useful and egalitarian than ever.
')
But over time (at least to me) the weaknesses of the “algorithm + crowdsourcing” system became visible. The next revolution seems inevitable.
Let's start by looking at some examples of crowdsourcing, algorithmic progress:
• Netflix - we look, put ratings and reviews. Netflix recommends (they even get rid of their algorithm ).
• Amazon - we choose, buy, set ratings and reviews. Amazon is analyzing the data until it knows exactly what to recommend (obviously, this is a critical part of their business ).
• Expedia - we fly, rent rooms, sleep and rest. Expedia machines analyze, set prices and calculate how to get the maximum profit.
• Google - we create content, quote others and exchange links. Google crawls, sorts, issues and ranks (based mainly on the human-generated link graph ).
• Facebook - we are friends, we update and like. Facebook is building the perfect system for targeting content and advertising.
• Reddit / Digg / StumbleUpon - we offer themes and vote for content. They accept them and determine how long the topic should hang on the main page and for whom it will be visible.
• Yelp - we have dinner, go to places and shop, leave reviews. Yelp classifies, ranks and recommends.
We are not completely dependent on these algorithms, because we have the opportunity to dig deeper, look further and collect more data for decision making (and often we do). But, since machines make assumptions about what we need, it is easier to use the default solution than to look for an alternative way.
Compare this with alternative methodologies before the Internet era:
• Video rental - a cassette for viewing helped select the sections “personnel recommendations”, “critics choice” and “award nominee”.
• Journal of technology (Consumer Reports) - experts analyzed and tested the product from all sides, and then gave ratings and recommended the best, from their point of view.
• Travel Agents - travel booked by professionals who have unique access to databases with information about tickets and hotels.
• Restaurant Guide - such directories included restaurant ratings and reviews from anonymous appraisers from dozens of cities.
• Personal recommendations - when there was not a single reference resource, friends, relatives, colleagues and various service personnel (in-house concierge, helpline) could offer their options so personal, relevant and valuable that this methodology is often used even today.
Understand me correctly, I believe that in most cases these predecessors of the modern approach “algorithm + crowdsourcing” cannot be compared in quality, reliability and utility. Their range was often too limited, sometimes distorted by commercial gain, and at times simply wrong.
But these predecessors had real advantages - the main thing was that everyone could understand what the recommendation was based on. Compare this with the mystery of our “algorithm + crowdsourcing” services.
Why is this page displayed first by a Google search? Why does one link stay on the Reddit home page for hours, and the other with the same number of votes disappears in a few minutes? Why does Facebook show me ads to work in Comcast Support? Why does Amazon recommend buying whole milk with this homemade tank ?
If we do not understand the reason for such recommendations, can this reduce our confidence in the future recommendations of these services?
Fred Wilson recently wrote an interesting story about why we should not invest in what we don’t understand:
... the venture capital industry is filled with investors seeking profit. And some of them do not understand what they are investing in. A few weeks ago, I received a call from a private investor who wanted to invest in one company from our portfolio. He asked a few questions about the company, and it became quite clear that the person did not fully understand the essence of its activities. But then he offered his investment. That scares me.
I have recently been visited by some foreign representatives, many of whom invest billions of dollars on behalf of their bosses. They all want to get into our funds and our deals. When I ask about the reasons, they cannot even formulate a convincing argument about the economic potential of a social network. But they see a profit and they want it too. That scares me.
I believe that for some people it is equally difficult and stupid to trust recommendation services, whose algorithms they cannot understand. These same people can turn to for recommendations to sources that they understand, and the results of which they can infer.
My point is not that Google, Netflix, Amazon, Yelp, or anyone else is doomed to failure. But I believe that there is a business opportunity for entrepreneurs, websites and companies to add an editorial component to the “algo-crowd” paradigm.
Many startups are already acting in this vein.
• Quora / FormSpring / StackExchange - unlike Google answers or even Wikipedia, where the order of results or information is unknown and free from responsibility, modern Q + A systems have put power in the hands of the crowd, algorithms and experts .
• Techmeme / Memeorandum / Mediagazer - a crowd of bloggers create content and quote each other. The algorithm compiles and sorts information, while curators ensure quality and relevance.
• Alltop — A collection of feeds selected by industry under the supervision of Guy Kawasaki. At the moment there is no algorithmic element, but I think it cannot last for so long.
• Oyster / Raveable - Travel sites are notorious for too many dubious comments and unreliable comments of suspicious quality from commercial partners, financially motivated affiliates. Oyster acts by sending editorial experts to hotels to write their own reviews, and then using user data and algorithmic ranking / optimization to help readers make the best choice. Raveable combines lightweight editorial oversight with powerful data mining and a sorting algorithm to help you find a hotel.
• Groupon / LivingSocial / Gilt Group - Sites with coupons and offers on the Web for about ten years, but these three (and a lot of similar ones) revolutionized the industry and become one of the most famous brands online thanks to social features (crowdsourcing marketing and advertising proposals), algorithmic filtering for the personalization / localization of proposals and editorial oversight (so that the deals they offer are truly worthy).
• TheSixtyOne / Pandora / Last.fm / Spotify - Music recommendations are getting better, and the latest generation of such services is a mixture of algorithms, crowdsourcing and editorial choices that deliver symphonic delight to many users.
I think we will see even more examples of this approach in the future, perhaps even on the websites / services that I mentioned at the very beginning (Yelp, Amazon, Netflix, etc.). I am sure that algorithms and crowdsourcing can be made stronger with the addition of a third pillar - confident, benevolent editors.
Source: https://habr.com/ru/post/109108/
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