Think about how much of your life is stored online. Between your daily Facebook status updates, your ‘Likes’, emails, Google +1 recommendations, your Netflix queue and history, and your purchases via iTunes and Amazon – that’s a lot of information about you that is used not only for advertising purposes but also to make recommendations for future purposes.
Let’s think about this not from a privacy perspective because I don’t even want to go there in this post. Let’s talk relevancy. On the one hand this is a good thing in a sense – more data on things you like typically means better recommendations, more relevant ads, etc. But maybe the next big thing in search and recommendations will be algorithms that forget. Why would that be important? We change. Our tastes in music can change drastically from year to year but iTunes may still recommend you pick up the Spice Girls’ Greatest Hits because of that one night you had too much to drink and bought ‘Wannabe’.
Or will Amazon stop recommending home improvement books to you after you’ve already bought the book you need to get kitchen remodeling ideas? The project is complete, so you don’t need more home improvement books on that topic.
Or Netflix – Remember this story or how a boyfriend took revenge on his cheating girlfriend through her Netflix queue? Kind of hilarious but will she always be recommended The Scarlet Letter? Will Netflix be able to account for extraneous or false positive ratings in the future?
With so much data being recorded about our purchasing decisions and preferences companies and businesses will have to do a better job at managing customer information, taking into consideration such as life stages in addition to past purchases, and learning in some senses – to forget.