I think that the problem with this conversation and its not quite direct matching is that we have several nearly independent characteristics. As I see it, these include:
a) whether items that are retrieved/recommended are opaque or have attributes (is the process content-based?) b) whether the basis for retrieving/recommending items is an explicit query (of whatever form) or is an implicit query formed by the user's historical actions (is this search or recommendation?) c) whether the retrieval/recommendation of items uses the behavior of all users to sharpen the results (is this a social algorithm or not?) d) what do we call the system (recommendation, collaborative filtering, search or whatever) These qualities are relatively independent and factoring them seems useful to me. Whether the user input is words typed, videos clicked or ratings made seems much less important to me. On Wed, Jan 27, 2010 at 3:21 PM, Jake Mannix <jake.man...@gmail.com> wrote: > I guess another way that I think of it is that CF is actually a very > special > case of recommendation: you have generic items and users, and withou > knowing > anything about the content of the items (or items), you can use ratings to > predict unknown preferences. > -- Ted Dunning, CTO DeepDyve
