Recommendation engines typically produce a list of recommendations in one of two ways – through collaborative or content-based filtering. Collaborative filtering approaches to build a model from a user’s past behavior (items previously purchased or selected and/or numerical ratings given to those items) as well as similar decisions made by other users, then use that model to predict items (or ratings for items) that the user may have an interest in. Content-based filtering approaches utilize a series of discrete characteristics of an item in order to recommend additional items with similar properties.

The following talk was recorded at the Recommendation Engines meetup at Pandora last night. , Data Science Lead at Trulia, discusses the ins and outs of Trulia Suggest.

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