Nick, Thank you for taking time to respond to my question. Regarding your first suggestion about recommending based on geo-location, are you suggesting different landing pages for different geo-lications?
Regards, Rashi On Jul 11, 2014 4:54 PM, "Martin, Nick" <[email protected]> wrote: > Couple thoughts/comments: > > - How much anonymity are we talking about here? you have an IP which gives > you (ostensibly) geography. That's not entirely trivial...think about > looking at purchasing characteristics by geolocation. You can make some > common sense decisions about what you recommend (ie maybe dont pop a > recommendation for flip flops to someone hitting you from Montreal in > January). > > - I can't speak to whether somebody's solved the cold start problem but > I'd recommend taking a look at how your customers acquire product > categories/items/widgets in an early period of their lifetime with you. > Think looking at cohorts and comparing them to tease out if there's a > pattern of purchasing in the first n days of them being a customer. Absent > that, I'd pitch popular stuff with good margins :) > > Hope that gets the wheels turning a bit. I don't think cold start is a > "one size fits all" kind of thing. Tough nut to crack. > > Sent from my iPhone > > On Jul 11, 2014, at 6:58 PM, "Rashi Jain" <[email protected]> wrote: > > > Hi, > > > > I want to build a recommendation for anonymous/first time users on an > > e-commerce website. I was thinking of recommending products to a > > cluster/segment of users , something like TreeClusteringRecommender does > > but I believe this has been deprecated. > > > > I have used item based collaborative filtering based on boolean > preferences > > for registered users but am looking for ideas to achieve some sort of > > recommendation for anonymous/first-time users. > > > > Any feedback will be highly appreciated. > > > > Thank you. > > > > Regards, > > Rashi >
