YesterYear It Was About Aggregating Ratings and Reviews
Curating Gives Reviews A Target Audience
What we need are recommendations that are targeted for a specific audience. For example, “Best Korean restaurants in Hong Kong for diners seeking best value for money”, “Best Korean restaurants for those with finer tastes”, “Healthy diners’ guide to Korean restaurants”.
While publications like Tatler, TimeOut, SassyHK, and others provide curated recommendations targeted to their demographics, they are providing the recommendations from a single person’s perspective and they have an inherent conflict of interest – they are often paid by the businesses they recommend.
Today It’s about Crowd-Curation
We want curation, but from sources without conflicts of interest, and from people with some depth of knowledge in the field we’re seeking recommendations. Many people have begun seeking recommendations from online forums or community groups feeling that those venues provide more authentic suggestions with sufficient depth of knowledge. This is today’s trend: to get crowd-curation from like-minded people.
While online forums and community groups provide us with a channel to ask fellow group members, they are not designed for finding relevant recommendations. Location-based or product-based searches are generally not features in these channels.
A platform designed specifically for crowd-curation of recommendations is what we need. On this platform, users join groups with people with similar interests, values and lifestyle. Together, they share, validate and curate recommendations. Like most social platforms, posts would be inherently trustworthy as authors hold reputational risk, and they would also be contemporary as content will be sorted on recency and popularity.
What differentiates the crowd-curation platform from other community-based social platforms is its focus on recommendations, and its flexibility to capture, organize, curate and validate content specific to the fields of interest:
Recommendations for restaurants, for example, would need to capture data on: cuisine type, location, quality of food, quality of service, decor, pictures of food, pictures of interior, etc.
Curation of restaurants would have a couple of dimensions: the targeted audience based on the group the recommendation is posted in, and what it is being recommended for (e.g. “Best dim sum for your money”).
Validation of recommendations would be based on members voting and commenting on the recommendations.