| CPC G06Q 30/0272 (2013.01) | 18 Claims |
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1. A system for predicting a probability of a computational advertisement (ad) displayed on a website or an online electronic user interface will be accessed when shown to an audience group, the system comprising:
a multidimensional knowledge transfer model, implemented by one or more processors, the multidimensional knowledge transfer model comprising:
a logical pre-processor configured to build:
an ad group node graph of a plurality of ad group nodes based on one or more feature similarities among the ad group nodes;
an ad campaign node graph of one or more ad campaign nodes from merging nodes in the ad group node graph of ad group nodes belonging to each of the ad campaign nodes; and
an ad account node graph of one or more ad account nodes from merging nodes in the ad campaign node graph of ad campaign nodes belonging to each of the ad account nodes;
an ad account multi-knowledge click-through-rate (CTR) prediction model comprising a first horizontal knowledge transfer model for solving cold start problem and a first hierarchical knowledge transfer model for solving imbalanced data problem, the ad account multi-knowledge CTR prediction model having trained to predict an ad account CTR for an ad account having its CTR predicted from the ad account node graph, features of the audience group, features of an ad account node of the ad account having its CTR predicted by the first horizontal knowledge transfer model, and features of other ad account nodes;
an ad campaign multi-knowledge CTR prediction model comprising a second horizontal knowledge transfer model for solving cold start problem and a second hierarchical knowledge transfer model for solving imbalanced data problem, the ad campaign multi-knowledge CTR prediction model having trained to predict an ad campaign CTR for an ad campaign having its CTR predicted from the ad campaign node graph, features of the audience group, features of an ad campaign node of the ad campaign having its CTR predicted appended with an ad account node hidden vector, and features of other ad campaign nodes;
wherein the ad campaign having its CTR predicted belongs to the ad account having its CTR predicted; and
wherein the ad account node hidden vector is extracted from the ad account multi-knowledge CTR prediction model in predicting the CTR of the ad account having its CTR predicted; and
an ad group multi-knowledge prediction model comprising a third horizontal knowledge transfer model for solving cold start problem and a third hierarchical knowledge transfer model for solving imbalanced data problem, the ad group multi-knowledge prediction model having trained to predict an ad group CTR for an ad group having its CTR predicted from the ad group node graph, features of the audience group, features of an ad group node of the ad group having its CTR predicted appended with an ad campaign node hidden vector, and features of other ad group nodes;
wherein the ad group having its CTR predicted belongs to the ad campaign having its CTR predicted; and
wherein the ad campaign node hidden vector is extracted from the ad campaign multi-knowledge CTR prediction model in predicting the CTR of the ad campaign having its CTR predicted;
wherein the ad group having its CTR predicted comprises the computational ad, and the predicted ad group CTR indicates the probability of the computational ad will be accessed; and
wherein each of the ad account multi-knowledge CTR prediction model, the ad campaign multi-knowledge CTR prediction model, and the ad group multi-knowledge prediction model is implemented by a Support Vector Machine neural network, a Factorization Machine neural network, a Parallel-Structure deep learning neural network, a Serial-Structure deep learning neural network, or a General Interest-Structure deep learning neural network.
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