Method and device for establishing picture search correlation prediction model, and picture search method and device
A technology of image search and prediction model, applied in the field of information processing, can solve the problems of lack of statistical significance, sparse clicks, and no clicks, etc., to achieve the effect of optimizing image search technology and improving relevance
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no. 1 example
[0042] figure 1It is a flow chart of a method for establishing a picture search correlation prediction model provided in the first embodiment of the present invention. The method of this embodiment can be executed by the device for establishing a picture search correlation prediction model. The device can use hardware and / or or software, and generally can be integrated into a server used to establish a picture search correlation prediction model. The method of this embodiment specifically includes:
[0043] 110. Using the training samples to train the pre-built raw deep neural network.
[0044] In this embodiment, the training samples include: query formula and picture data.
[0045] As mentioned above, in order to realize that the final output of the deep neural network is the correlation measure between the picture and the query expression, it is necessary to use the image data and the query expression as training samples to train the original deep neural network.
[0046...
no. 2 example
[0092] Figure 4 It is a flowchart of a method for establishing a picture search correlation prediction model according to the second embodiment of the present invention. This embodiment is optimized on the basis of the above-mentioned embodiments. In this embodiment, the specific optimization of using training samples to train the pre-built original deep neural network is as follows: select a set number of training samples; sequentially obtain a training sample input to the original deep neural network, and adjust the weighting parameters in the original deep neural network according to the output of the original deep neural network based on the training sample; return to execute to obtain a training sample input to the The operation of the original deep neural network until the pre-set training end condition is reached.
[0093] Correspondingly, the method in this embodiment specifically includes:
[0094] 410. Select a set number of training samples.
[0095] Considering...
no. 3 example
[0113] Figure 6 It is a flowchart of a method for establishing a picture search correlation prediction model according to the third embodiment of the present invention. This embodiment is optimized on the basis of the above embodiments. In this embodiment, the specific optimization of selecting a set number of training samples is as follows: according to the image click log of the search user, summarize the image click information corresponding to the same query sample, Wherein, the query sample includes: a single query or at least two query that meet the set similarity threshold condition; according to the aggregated click information on the picture, a positive image sample set corresponding to the query sample is generated; and Negative picture sample set; select the query formula sample of setting quantity as described training query formula, and according to the positive picture sample set and the negative picture sample set respectively corresponding to each described tr...
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