In a Guide to Recommender Systems, a blog post written by ReadWriteWeb founder and editor, Richard MacManus, 4 types of recommender systems are identified:

Personalized recommendation – recommend things based on the individual’s past behavior

Social recommendation – recommend things based on the past behavior of similar users

Item recommendation – recommend things based on the item itself

A combination of the three approaches above

Each of these approaches has distinct advantages and disadvantages and the post outlines how both Google and Amazon use a combination of all three to drive their recommendation engines.

As I look at the current state of online fashion it’s pretty clear no one has figured out how to leverage these recommender systems in fashion affiliate ecommerce.  There are simply too many styles, they have too short of product lives and the diversity of consumer wants and needs are too segmented.  The complexity in making fashion recommendations makes creating an algorthimic solution extremely difficult.  Past behavior is not a good tool because the trends are always changing.  Item recommendations don’t work because there are simply too many product attributes in fashion and each attribute (think fit, price, color, style, fabric, brand, etc) has a different level of importance at different times for the same consumer.  Social recommendations, though challenging, hold the most promise.  So far, though, social shopping has fallen flat.

The problem, I think, is trust and scale.  In fashion, unlike say electronics or travel, you aren’t going to trust just anyone’s advice.  You want to know who is making the fashion recommendations and they need to have some credibility.  To overcome this trust issue, many fashion recommendation sites focus on “friend recommendations”.  The problem here is this model doesn’t scale.

By example, consider TripAdvisor, if you are looking for resorts in Bermuda, you will see the top-ranked 4-star resorts at the top of your search. The top-ranked resorts have credibility as “being the best” in part because of the many, many lower ranked resorts further down the list. The consumer knows that all resorts have been carefully considered and these are the best. Friend recommendations don’t pass this scale test. You just don’t have confidence that all the styles out there have been discovered and considered. Therefore, the recommendation has much less weight and credibility.

At StyleHop we believe the way to get around the trust-scale tradeoff in fashion social recommendations is to let users explicitly identify their “fashion peer group” when they are performing product searches.  So in addition to the typical product attribute refinements you can make when shopping for clothes online, we want to allow you to filter your results based on the 5-star rankings (think netflix) of folks you trust to help you shop.  For a nineteen year old going to college in the fall for her freshman year, she may want to see the styles highly ranked by women at the University of Michigan.  For another woman moving to New York City for her first job after graduation, she may want to see the top ranked wear-to-work clothes of 20-something Manhattan women.

By allowing consumers to identify their fashion peers and adjust who their peers are for each search, the user is in control and the results are broad and deep. Most importantly, you are giving the shopper validation -before she makes the purchase- that what she is buying will be acceptable among her peer group.  This goes well beyond surfacing top styles the consumer might like and acts as a powerful social reinforcement of the purchase decision.