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Collaborative filtering video recommendation method for considering user preference dynamic changes

A video recommendation and collaborative filtering technology, applied in special data processing applications, instruments, electrical and digital data processing, etc., can solve the problems of recommendation errors, rarely consider user time dynamic changes, etc., to improve recommendation accuracy, improve user experience, The effect of recommendation ability enhancement

Active Publication Date: 2018-10-16
NANJING UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Although the above method can recommend videos of interest to users, the user's preferences will change over time. If the model statically models user preferences, it may cause recommendation errors
Existing models rarely consider the feature that user preferences change dynamically over time

Method used

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  • Collaborative filtering video recommendation method for considering user preference dynamic changes
  • Collaborative filtering video recommendation method for considering user preference dynamic changes
  • Collaborative filtering video recommendation method for considering user preference dynamic changes

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Embodiment Construction

[0042] The technical scheme of the present invention will be further described below in conjunction with the drawings.

[0043] The present invention discloses a collaborative filtering recommendation method considering the dynamic changes of user preferences over time. In one embodiment, the method includes three stages of data preprocessing, model training, and sorting. When the system is ready to recommend videos to users, the recommendation engine The corresponding metadata is read into the preprocessing module. The data preprocessing part mainly processes the raw data to generate the formatted learning sample set required for model training. Model training mainly learns user features and video features based on the generated samples. The training module first initializes the feature parameters to be learned, and performs BPR learning and SimRank learning according to the corresponding learning samples input by the data preprocessing module. In the final ranking stage, the v...

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Abstract

The invention discloses a collaborative filtering video recommendation method for considering user preference dynamic changes. The method comprises the steps of data pre-processing, model training andsorting, wherein the data pre-processing is mainly that original data is processed to generate a formative leaning sample set required for model training; and a training model mainly learns user characteristics and video characteristics according to generated samples, and is mainly composed of a parameter matrix, a BPR model and a SimRank model. When a system is ready to recommend videos to users, a recommendation engine firstly reads the users and videos recorded by a background and corresponding metadata into a pre-processing module; then a training module firstly initializes to-be-learnedcharacteristic parameters, BPR leaning and SimRank learning are carried out respectively on input corresponding leaning samples according to the data pre-processing module; and lastly, the videos aresorted and recommended according to the trained user characteristics and video characteristics. The collaborative filtering video recommendation method for considering the user preference dynamic changes has the advantages that under the condition of not increasing the time complexity, the user preference is modeled dynamically, thereby improving the accuracy of recommendation.

Description

Technical field [0001] The invention belongs to the field of personalized content recommendation, and specifically relates to a collaborative filtering video recommendation method that considers the dynamic changes of user preferences over time. Background technique [0002] Thanks to the emergence of various video portals (such as Youku) and self-media platforms (such as YouTube), video streaming has shown explosive growth in recent years. For users, due to limited time and interest, it takes a lot of effort to search for their favorite videos from a large video library. At the same time, this also brings huge challenges to video providers. Only when recommended videos meet user preferences can they attract more users. [0003] The key to solving the above-mentioned contradictions is how the recommendation system can accurately and timely model user preferences. As a content-independent recommendation technology, collaborative filtering has been widely used in various recommenda...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
Inventor 单存宇叶保留陆桑璐
Owner NANJING UNIV
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