SFT-ALS-based time sequence fan increase prediction method

Abstract

The invention discloses an SFT-ALS-based time sequence fan increase prediction method. The method comprises the steps of 1, acquiring user basic data; step 2, extracting basic data features, including daily video update quantity, fan quantity, video like quantity, video collection quantity, video playing quantity, average growth rate of fans and SFT features of fan time sequence signals; step 3, performing normalization processing on the features; 4, forming a hybrid matrix by the feature sets after feature processing, sending the hybrid matrix into an ALS model for matrix decomposition, and reconstructing the hybrid matrix; 5, performing optimization by using a genetic algorithm to obtain a dimension k of an optimal low-dimensional matrix; and step 6, embedding the fan increase model obtained by training into a platform system, and performing practical application. The invention provides a time sequence fan increase prediction method based on SFT-ALS. Various time sequence features related to the fan increase are extracted and are combined with an ALS model to predict and analyze the fan increase, so that rules and characteristics of each creative and creative worker in the creative and creative development process are revealed.

Description

technical field [0001] The present invention relates to the field of fan growth forecasting, in particular to a time series fan growth prediction method based on SFT-ALS. Background technique [0002] Big data technology originated in the 1990s. The theory matured in 2008 with Google's public publication of two papers "Google File System" and "Simple Data Processing Based on Clusters: MapReduce". After that, this technology was widely used in business, science and technology. , medical care, government, education, economy, transportation, logistics and various fields of society. With the continuous maturity of data theory, the continuous improvement of data acquisition methods, and the continuous improvement of data research tools, it is possible to transfer data analysis work from large companies to small teams. [0003] Therefore, we hope to put the power of science and technology into cultural creation through this project. At present, when cultural creators on various ...

AI Technical Summary

Problems solved by technology

Furthermore, most of the current traffic platforms have a set of content push methods, and this method will only push the content that is easy to be accepted by the audience first, so that cultural and creative workers are faced with the dilemma of low target audience viscosity and unstable content income.

Especially with the influx of individual cultural and creative workers, there will inevitably be problems such as high content similarity, low style recognition, extremely unstable audience groups, and difficulty in discovering individual IPs.

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