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Collaborative filtering recommendation method based on search behavior perception

A collaborative filtering recommendation and behavior technology, applied in the field of collaborative filtering recommendation based on search behavior perception, can solve problems such as inability to perceive user search behavior, and achieve improved satisfaction and dependence, low time complexity, improved accuracy and timeliness sexual effect

Inactive Publication Date: 2013-06-05
FOCUS TECH +1
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Problems solved by technology

[0006] Purpose of the invention: Aiming at the problem that the collaborative filtering algorithm cannot perceive the user's search behavior, analyze the user's search behavior and inquiry behavior, use the keywords used in the user's search behavior as the context of the user's inquiry behavior, and provide a B2B e-commerce website Collaborative filtering recommendation method based on user search behavior perception

Method used

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  • Collaborative filtering recommendation method based on search behavior perception
  • Collaborative filtering recommendation method based on search behavior perception
  • Collaborative filtering recommendation method based on search behavior perception

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specific Embodiment approach

[0042] Step 104: Establish a recommendation method framework based on the factor machine model, the framework is divided into two processes of learning and prediction. During the learning process, the framework obtains the parameters of the factor machine model through learning according to the training data set constructed in step 103 . In the prediction process, the framework uses the learned model parameters to calculate evaluation values ​​based on the given user, product, and keywords used by the user. The specific implementation method is:

[0043] (104-1) Model Representation. The factor machine model measures the relationship between the components of the vector by factoring the interaction parameters, and its model is shown in formula (1):

[0044] r ^ ( x ) ≡ w 0 + Σ i = ...

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Abstract

The invention discloses a collaborative filtering recommendation method based on search behavior perception. The method includes the following steps: (1) analyzing inquiry behaviors and search behaviors of a user on an e-commerce website; (2) constructing user-product keyword tensor based on search behavior context; (3) constructing a training dataset of a factor machine model and mapping tensor data into vector data; (4) establishing a recommendation method frame based on the factor machine model and utilizing an improved alternate least squares algorithm to carry out parameter estimation; and (5) evaluating the recommendation method based on the factor machine model through an experiment. The collaborative filtering recommendation method based on the search behavior perception utilizes context information ignored by a traditional collaborative filtering recommendation method, solves the problem that a traditional individuation recommendation method cannot provide a recommended result which expresses the intent of the user on a business to business e-commerce website, and is better than a traditional method in accuracy and timeliness of the recommended result.

Description

technical field [0001] The invention relates to the field of Internet personalized recommendation, in particular to a collaborative filtering recommendation method based on search behavior perception. Background technique [0002] In recent years, the successful application of personalized recommendation systems on the Internet has created new opportunities for Internet companies, especially e-commerce websites. 30% of the purchase business on B2C e-commerce websites in specific fields comes from personalized recommendation systems. However, recommender systems are not widely used on B2B e-commerce websites. B2B e-commerce websites play an intermediary role in business activities, and buyers look for suppliers of target products through B2B e-commerce websites. In this process, the buyer first enters the search keywords related to the target product, and the e-commerce website returns a large number of products of the same type from different suppliers. Its suppliers make ...

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Application Information

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IPC IPC(8): G06Q30/02
Inventor 归耀城李仁勇陈建国高志强陈翠翠周洲
Owner FOCUS TECH
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