Some time back, I wanted to search one of my own social network post. It was a resource I had shared and somehow I was not able to “google” it (again). I eventually found it – but it took me 15 odd minutes to scroll down to my twitter feed. It was NOT fun! And I thought to myself – there’s got to be a better way! And I thought – It’ll be great if I solve it for not just Twitter but all my social network activities that includes LinkedIn, Facebook Pages, Google+. So here’s couple of things thats working for me, I hope it helps someone out there too:
Now, before we begin when I say “Searchable” – I mean searchable by YOU (or a human being) and not necessarily search engines. But it turns out, both my ideas increase your chances of getting your social media activities Indexed! With that, Here are the ideas:
1) Syndicate your Social Network Activities (Posts/Images/Updates) to Tumblr/Blogger
I use IFTTT to syndicate my Twitter, Facebook and LinkedIn activities to Blogger
2) Create a post about your social network activities on your blog:
Though Idea #2’s main goal is to keep my blog readers updated about my social network activities – But it also acts as a good way to make my social media posts “searchable”.
And remember I said earlier that the chances of your social network posts getting indexed by search engines increases? That’s because WordPress, Tumblr & Blogger’s posts are accessible by Google (unless you choose to block it). So that’s about it for this post. If you like the idea(s), please let me know! And if you have other ideas – also let me know, I am always looking for ways to make my social media activities easily searchable to me as well as for anyone else.
Let’s connect and converse on any of these people networks!
Recommendation systems is application of Data Mining Technologies. I have researched about how to implement a recommendation system and as a part of my research, I studied recommendation systems that are already out there on the Internet and here are five examples of Recommendation systems on the web:
Customers Who Bought This Item Also Bought:
Frequently Bought Together: (Example of Market Basket Analysis a.k.a Association Rules):
Jobs you may like + Groups you may like + Companies you may follow:
Did you knew about Netflix Prize for improving their recommendation engine? If not you should read that!
Here’s their Movies you’ll love recommendation system:
People you may want to follow:
I do not have a screenshot but just wanted to point out the Google “personalize” (a.k.a recommends based on past behavior) search results based on your search history. And you can switch that off, if you want: Turn off search history personalization
In this blog-post, we saw examples of recommendation systems. The key take away is that there is more than one approach to building a recommendation system. The approaches can be based on 1. Past Behavior 2. Past Behavior of “friends” 3. Recommendation based on the Item that is being searched And you can definitely, Mix and Match!
And I hope this post helped you understand an application of data mining that’s all around us! And question: Where else do you see recommendation systems in action? Leave a comment!