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Cross recommendation method and system based on local weighted linear regression model

A linear regression model and local weighting technology, applied in the field of information processing, can solve problems such as overfitting, affecting the accuracy and recall of cross-recommendation systems, and affecting the accuracy and recall of cross-recommendation, so as to improve accuracy and recall rate effect

Pending Publication Date: 2019-08-09
QINGDAO UNIV OF SCI & TECH +1
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is to overcome the cross-recommendation algorithm based on the quadratic regression model in the prior art. Since the relationship between user behavior data and user rating data is often extremely complicated, it is difficult for the quadratic regression model to fully understand it. Fitting (that is, the data is in a state of under-learning), thus affecting the accuracy and recall of the cross-recommendation system; and establishing a higher-order (such as three, four) regression model may be more adequate for data fitting, but it is easy Causes overfitting and also affects the accuracy and recall of the cross-recommendation system

Method used

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  • Cross recommendation method and system based on local weighted linear regression model
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  • Cross recommendation method and system based on local weighted linear regression model

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Embodiment 1

[0040] An embodiment of the present invention provides a cross-recommendation method based on a locally weighted linear regression model, such as figure 1 As shown, the cross-recommendation method based on the locally weighted linear regression model includes:

[0041] Step S1: Obtain the user's scoring record for at least one target item in the target object and the auxiliary scoring record for at least one auxiliary item in auxiliary objects related to the target object.

[0042] Step S2: Establish an item vector to be evaluated of the target item to be evaluated according to the scoring record and the auxiliary scoring record.

[0043] Step S3: expand the vector of items to be evaluated into an augmented vector, and establish a locally weighted linear regression model according to the augmented vector.

[0044] Step S4: Using the stochastic gradient descent algorithm to solve the locally weighted linear regression model to obtain an optimal solution.

[0045] Step S5: Cal...

Embodiment 2

[0089] Embodiments of the present invention provide a cross-recommendation system based on a locally weighted linear regression model, such as Figure 4 As shown, the cross-recommendation system based on the local weighted linear regression model includes: a score record acquisition module 1, which is used to obtain the score record of the user on at least one target item in the target object and at least one target item in the auxiliary object related to the target object. An auxiliary scoring record of an auxiliary item; the target vector construction module 2 to be evaluated is used to establish the item vector to be evaluated of the target item to be evaluated according to the scoring record and the auxiliary scoring record; the local weighted linear regression model construction module 3 is used to The item vector is expanded into an augmented vector, and a locally weighted linear regression model is established according to the augmented vector; the optimized solution sol...

Embodiment 3

[0109] An embodiment of the present invention provides a non-transitory computer storage medium, the computer storage medium stores computer-executable instructions, and the computer-executable instructions can execute the locally weighted linear regression model-based cross-recommendation method in any of the first embodiments above. Wherein, the above-mentioned storage medium can be a magnetic disk, an optical disk, a read-only memory (Read-OnlyMemory, ROM), a random access memory (Random Access Memory, RAM), a flash memory (FlashMemory), a hard disk (Hard Disk Drive, Abbreviation: HDD) or solid-state drive (Solid-State Drive, SSD), etc.; the storage medium may also include a combination of the above types of storage.

[0110] Those skilled in the art can understand that all or part of the processes in the methods of the above embodiments can be implemented through computer programs to instruct related hardware, and the programs can be stored in a computer-readable storage me...

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Abstract

The invention provides a cross recommendation method and system based on a local weighted linear regression model. The cross recommendation method comprises the following steps: obtaining a scoring record of a user for at least one target item in a target object and an auxiliary scoring record for at least one auxiliary item in an auxiliary object related to the target object; establishing a to-be-evaluated project vector of the to-be-evaluated target project according to the scoring record and the auxiliary scoring record; expanding the vector of the project to be evaluated into an augmentedvector, and establishing a local weighted linear regression model according to the augmented vector; solving the local weighted linear regression model by utilizing a random gradient descent algorithmto obtain an optimized solution; calculating a prediction score of a to-be-evaluated target project in the target object according to the optimization solution; and recommending the to-be-evaluated target project with the predicted score meeting a preset score standard to the user. By implementing the method and the device, the behavior data and the score data of the user are fully fitted, so that the accuracy and the recall rate of the cross recommendation system are improved.

Description

technical field [0001] The invention relates to the technical field of information processing, in particular to a cross-recommendation method and system based on a locally weighted linear regression model. Background technique [0002] With the development of information technology and the Internet, people have gradually entered the era of information overload from the era of information scarcity. In this era, both information consumers and information producers have encountered great challenges: for information consumers, it is very difficult to find the information they are interested in from a large amount of information; It is also very difficult for the information produced by oneself to stand out and attract the attention of the majority of users. Recommender systems are an important tool to resolve this contradiction. The task of the recommendation system is to connect users and information. On the one hand, it helps users find information that is valuable to them, ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9535G06F17/18
CPCG06F17/18G06F16/9535
Inventor 于旭杜军威于淼胡强张国栋
Owner QINGDAO UNIV OF SCI & TECH
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