Hierarchical commodity information filtering and recommending method

Hierarchical commodity information filtering and recommending method

  • CN 105,809,474 A
  • Filed: 02/29/2016
  • Published: 07/27/2016
  • Est. Priority Date: 02/29/2016
  • Status: Active Application
First Claim
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1. a stratification merchandise news filtered recommendation method, it is characterised in that comprise the steps:

  • A1;

    for commending system, structure one layering Poisson model;

    A2, it is the vectorial z of K to each group of validated user commodity to structure lengthui, each of which component zuik

    Poisson (θ

    ukβ

    ik), scoring is sized to the inner product of corresponding user preference vector and item property vector, and wherein K is the length of item property vector sum user preference vector, zuiFor often organizing user, the commodity auxiliary vector that length is K to structure, θ

    uFor user preference vector, β

    iFor item property vector, k is the sequence number of component, and u is user'"'"'s sequence number, and i is commodity sequence number;

    The method that A3, employing variation are inferred carries out approaching Posterior distrbutionp, utilizes coordinate rise method successive ignition until convergence, derives all hidden variablesAPPROXIMATE DISTRIBUTION;

    Wherein the implication of each parameter is as follows;

    β

    is β

    iSet, θ

    represents θ

    uSet;

    ξ

    uMeeting the scale parameter in Gamma distribution for user preference vector, ξ

    represents ξ

    uSet, η

    iMeeting the scale parameter in Gamma distribution for item property vector, η

    is η

    iSet, z variable represents zuiSet;

    A4, prediction often organize user'"'"'s commodity to scoring,User can being carried out final recommendation according to the sequence of score size, wherein subscript T represents vector transposition, is row vector by column vector transposition.

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