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The personal shopper
Transcript of The personal shopper
16% of online retailers have ANY recommendation software Relational Databases reactive - don't anticipate behavior recommendations become stale they're highly biased generic and impersonal products viewed
past purchases related to viewed products STATS101 we recommend... γi = β0 + β1χ1 + εi γi = β0 + β1χ1 + β2χ2 +β3χ3 +β4χ4 +β5χ5 +β6χ6 β7χ7 + εi ratings searches order of page views depths of page views brand duration platform click streams IP address broadband speed product catalog descriptions product details session stats history behavior Relationships Statistical Relational Modeling uncertainty complex relational structure Bayesian Statistics geography past searches past behaviors Neural Networks The sample must be representative of the population for the inference prediction.
The error is assumed to be a random variable with a mean of zero conditional on the explanatory variables.
The variables are error-free. If this is not so, modeling may be done using errors-in-variables model techniques.
The predictors must be linearly independent, i.e. it must not be possible to express any predictor as a linear combination of the others. See Multicollinearity.
The errors are uncorrelated, that is, the variance-covariance matrix of the errors is diagonal and each non-zero element is the variance of the error.
The variance of the error is constant across observations (homoscedasticity). If not, weighted least squares or other methods might be used. Keeps recommending products even if they were a one time purchase * Classical assumptions for regression analysis include: P(E1|H1) P(H1)
P(H1|E) = __________________________
P(E1|H1) P(H1) + P(E2|H2) P(H2) = ___.75x.05___
.75x.05 + .5x.5 = .6 new products get ignored amazon.com Bruce D'Ambrosio, Cleverset's founder and a professor of electrical engineering and computer science at Oregon State University.