Overview
One of the primary methods of forecasting a customers rating of a product is to find other customers similar to the given customer, and taking an average of their ratings for the product in question. (see nearest neighbor method)
Determining Similarity
A central task in the similar user method is to find a function (a kernel) which can compute a measure of similarity between two customers. This function can be based on
- Feature Based - if a set of features for each customer is available (age, gender, income, education etc....), then a kernel can be constructed from those features. Of course, determining the form of the function (finding the optimal function) is the challenge
- Ratings Based - if the analyst has a set of ratings from the two customers, it is common to calculate a correlation among the two sets of ratings using the correlation as the similarity measure. Typically, a customers ratings are first normalized before the correlation is run. In addition, the correlations can only be run on products for which both customers have supplied a rating.
- Latent Factors
Similarity Measures
Similarity Measures