Multi-task recommendation method and system for joint Bayesian inference and weighted rejection sampling

Abstract

The disclosure provides a multi-task recommendation method and system for joint Bayesian reasoning and weighted rejection sampling. Among them, the multi-task recommendation method includes: constructing a Bayesian model embedding potential prior factors and a sampling model embedding balance factors running in parallel; determining the total number of recommended products and embedding Bayesian models embedding potential prior factors and embedding balance The sampling model of the factor is the ratio of the number of recommended products to the total number of recommended products; the corresponding negative sequence pattern mined from the specific user shopping behavior data set is input into the Bayesian model embedded with potential prior factors, and the probability Sequentially recommend a corresponding number of products to a specific user; at the same time, use the sampling model embedded in the balance factor to sample all user shopping behavior data sets, and recommend a corresponding number of products to a specific user.

Description

Technical field

[0001] This disclosure belongs to the product recommendation area, in particular, involving a multi-task recommendation method and system for combined Bayesian reasoning and weighted denial sampling.Background technique

[0002] The statement of this section is merely the background technology information related to the present disclosure, which is not necessarily constituted in prior art.

[0003] For the field of user shopping behavior data, the sequence mode can indeed achieve good results for user behavior analysis, and can be interpreted. However, most sequence modes are only focused on the forward movement of the user. If a user purchases bananas, apples, but pears, for the user's negative action, such as the user's purchase of bananas, basketball, Apple's concern, showing more Low attention. However, in real life, the application and reference value of negative sequences is not less than positive sequence. When the user generated explicit feedback, negative sequence mod...

Examples

Embodiment 1

[0042]figure 1 A multi-task recommendation method for combined Bayesian reasoning and weighted rejection sampling is given in this embodiment.

[0043]Such asfigure 1 As shown, a multi-task recommendation method of a combined Bayesian reasoning and weighted denial, including:

[0044]S101: Building a parallel operation of the Bayesian model and the sampling model of embedding equilibrium factors; where potential prior factors are tapped from a specific user shopping behavior data set for each item. The probability is constrained for the condition of the Bayesian model; the sampling model is a weighted acceptance-reject sampling model with the countdown of equilibrium factors.

[0045]In the specific implementation, the process of building a Bayesian model embedded in a potential prior factor is:

[0046]Set a node in the Bayesian network in the Bayesian network, based on the user's shopping order, establish the contact between the Bayesian network;

[0047]The conditional probability of each item ...

Embodiment 2

[0132]This embodiment provides a multi-task recommendation system with a combined Bayesian reasoning and weighted rejection sampling corresponding to Example 1, which improves the coverage of goods, alleviating long-tail effect, and improves the recommended accuracy. .

[0133]A multi-task recommendation system of a combined Bayesian reasoning and weighting denial, including:

[0134](1) Model build module, which is used to build a parallel operation of a Bayesian model and a sampling model embedded in equilibrium factor; where potential prior factors are excavated from a specific user's shopping behavior data set The conditional probability of each item in the sequence mode is constrained for the condition of the Bayesian model; the sampling model is a weighted acceptance-reject sampling model with the countdown of equilibrium factors;

[0135]In the model build module, the negative sequence mode is excavated from a specific user shopping behavior data set using the F-NSP + algorithm.

[0136]...

Embodiment 3

[0146]This embodiment provides a computer readable storage medium that stores a computer program, which is implemented when executed by the processor.figure 1 The steps shown in the multi-task recommendation method shown in the combined Bayesian reasoning and weighted denial sampling.

Technical field

[0001] This disclosure belongs to the product recommendation area, in particular, involving a multi-task recommendation method and system for combined Bayesian reasoning and weighted denial sampling.Background technique

[0002] The statement of this section is merely the background technology information related to the present disclosure, which is not necessarily constituted in prior art.

[0003] For the field of user shopping behavior data, the sequence mode can indeed achieve good results for user behavior analysis, and can be interpreted. However, most sequence modes are only focused on the forward movement of the user. If a user purchases bananas, apples, but pears, for the user's negative action, such as the user's purchase of bananas, basketball, Apple's concern, showing more Low attention. However, in real life, the application and reference value of negative sequences is not less than positive sequence. When the user generated explicit feedback, negative sequence mod...

Examples

Embodiment 1

[0042]figure 1 A multi-task recommendation method for combined Bayesian reasoning and weighted rejection sampling is given in this embodiment.

[0043]Such asfigure 1 As shown, a multi-task recommendation method of a combined Bayesian reasoning and weighted denial, including:

[0044]S101: Building a parallel operation of the Bayesian model and the sampling model of embedding equilibrium factors; where potential prior factors are tapped from a specific user shopping behavior data set for each item. The probability is constrained for the condition of the Bayesian model; the sampling model is a weighted acceptance-reject sampling model with the countdown of equilibrium factors.

[0045]In the specific implementation, the process of building a Bayesian model embedded in a potential prior factor is:

[0046]Set a node in the Bayesian network in the Bayesian network, based on the user's shopping order, establish the contact between the Bayesian network;

[0047]The conditional probability of each item ...

Embodiment 2

[0132]This embodiment provides a multi-task recommendation system with a combined Bayesian reasoning and weighted rejection sampling corresponding to Example 1, which improves the coverage of goods, alleviating long-tail effect, and improves the recommended accuracy. .

[0133]A multi-task recommendation system of a combined Bayesian reasoning and weighting denial, including:

[0134](1) Model build module, which is used to build a parallel operation of a Bayesian model and a sampling model embedded in equilibrium factor; where potential prior factors are excavated from a specific user's shopping behavior data set The conditional probability of each item in the sequence mode is constrained for the condition of the Bayesian model; the sampling model is a weighted acceptance-reject sampling model with the countdown of equilibrium factors;

[0135]In the model build module, the negative sequence mode is excavated from a specific user shopping behavior data set using the F-NSP + algorithm.

[0136]...

Embodiment 3

[0146]This embodiment provides a computer readable storage medium that stores a computer program, which is implemented when executed by the processor.figure 1 The steps shown in the multi-task recommendation method shown in the combined Bayesian reasoning and weighted denial sampling.

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