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Data-driven recommendations mean customers have to spend less time digging for the perfect product themselves.

The algorithm does the heavy lifting – once they set off on the trail of whatever they’re seeking, they are guided to things they may have otherwise only found after hours of searching (or not at all).

To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Copyright 2012 ACM 978-1-4503-1638-5/12/09 ....00. DISSIMILAR LIKE DISLIKE SIMILAR Figure 1: The four kinds of relationships modeled in our approach: Like, dislike, similarity and dissim- ilarity.

It’s not surprising, in truth, that machine learning quickly discovered that humans are drawn to drama and tempted by content more extreme than what they originally set out to find.It doesn’t take long to think up recommendation scenarios that could raise an eyebrow.