A Collaborative Filtering Video Recommendation Method Considering Dynamic Changes of User Preferences

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 in particular relates to a collaborative filtering video recommendation method considering the dynamic change 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 interests, it takes a lot of energy to search for their favorite videos from a large number of video libraries. At the same time, this also brings a huge challenge to video providers. Only when recommended videos meet user preferences can more users be attracted. [0003] The key to solving the above contradictions is how the recommendation system can accurately and timely model the user's preference. As a content-independent recommendation technology, collaborative filtering has been widely used in vario...

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

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