http://bit.ly/idxnNH
Yes, searching has become irritably daunting task.
Have you ever tried searching some keyword and found that the lower links displayed may have higher relevancy than the upper ones? Or the second/third link was more relevant than the first one?
The time is for decision-search. not search-search.
Social Search:
I do not think that the concept of social search is new in anyway.
If you wanted to go to a restaurant, what would you do?
The first thing is search the restaurant. The second thing that immediately comes into your mind is what I am going into/for?
For the best experience, we ask friends who have been there. In today's internet dominated by social media, searching for such relevant ideas would be definitely a nice input for making decision on whether we would like to check it or not.
One more layer of work here might be the layer of social recommendations. Say, your friend says that the customer services of the garage is not good enough. Would you try it? You would not want to go to the places where your friend have not been treated well. As your experience might turn out to be similar. Thus those places can be filtered out from the results.
Recommend:
Recommend could be another nice feature. Based upon the social search parameters, the machine learning algorithm can recommend the nearest results. For example, if I am searching to buy a printer or computer I would first love to talk to a geek next to me. Based upon his recommendation, I can be assured that I am getting the best out of my expenses. Thats how the social recommendations work. This can be easily adopted into present-day internet dominated by social networks.
Applications:
TLDR: Google goes+1 http://www.google.com/experimental/index.html
Yes, searching has become irritably daunting task.
Have you ever tried searching some keyword and found that the lower links displayed may have higher relevancy than the upper ones? Or the second/third link was more relevant than the first one?
The time is for decision-search. not search-search.
Social Search:
I do not think that the concept of social search is new in anyway.
If you wanted to go to a restaurant, what would you do?
The first thing is search the restaurant. The second thing that immediately comes into your mind is what I am going into/for?
For the best experience, we ask friends who have been there. In today's internet dominated by social media, searching for such relevant ideas would be definitely a nice input for making decision on whether we would like to check it or not.
One more layer of work here might be the layer of social recommendations. Say, your friend says that the customer services of the garage is not good enough. Would you try it? You would not want to go to the places where your friend have not been treated well. As your experience might turn out to be similar. Thus those places can be filtered out from the results.
Recommend:
Recommend could be another nice feature. Based upon the social search parameters, the machine learning algorithm can recommend the nearest results. For example, if I am searching to buy a printer or computer I would first love to talk to a geek next to me. Based upon his recommendation, I can be assured that I am getting the best out of my expenses. Thats how the social recommendations work. This can be easily adopted into present-day internet dominated by social networks.
Applications:
- Amazon search for books.
- Google/yahoo/bing Search or say twitter/facebook search
- google/yahoo/bing map with social search layer.
- reading circle
TLDR: Google goes+1 http://www.google.com/experimental/index.html
One of the presentation that I liked about social circle: @p186: people should be able to say it anonymously.
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