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
