Latent variable model-based user preference extraction method

The invention discloses a latent variable model-based user preference extraction method. The method comprises the steps of firstly selecting N commodity relative attributes to form a commodity property set, building according to historical data to obtain a bayesian network, searching in the bayesian network to obtain a maximal semi-clique, and then inserting a latent variable L, showing user preference, into the maximal semi-clique, so as to obtain a latent variable model, wherein L being equal to 1 shows that a user prefers, and L being equal to 0 shows that the user does not prefer; performing parameter learning on the latent variable model to obtain a conditional probability table of various nodes in the latent variable mode; then according to the conditional probability table of the latent variable L, performing user preference extraction: searching to obtain an attribute combination item corresponding to a conditional probability maximum when L is equal to 1, wherein the attribute combination item corresponds to commodity types most preferred by the user; searching to obtain an attribute combination item corresponding to the conditional probability maximum when L is equal to 0, wherein the attribute combination item corresponds to commodity types least preferred by the user. By aiming at the user preference hidden in commodity evaluation data, the more objective and realistic user preference results are extracted by the structure of the bayesian network.