Dressipi is a consumer web application that is ultimately building a Situational Recommendation Engine (SRE) that maps products to individuals, taking into consideration the situational and emotional context of the purchaser/use of the purchase as well as more personal parameters. The context could be an event, a feeling, a mood etc. Personal parameters could be size, silhouette, hair and skin colour etc.
Dressipi creates a unique profile (Fashion Fingerprint) for each member by asking a series of questions and observing behaviour and subsequently makes garment and outfit recommendations personalised to that member. In between a members profile and garment sits an expert system that has a data structure against which the garments are categorised, and a proprietary rules engine that does the matchmaking.
Members can complete a range of conventional activities, from Liking/Disliking, Creating Lists, and Tagging etc. Or Dressipi observes and collects behaviour. The system then uses both inputs to offer the member a suggested recommendation which they are highly probable to like.
The aim is to offer consumers a personalised and context relevant filter of products and store front - making shopping simpler and more efficient.