Collaborative filtering recommendation algorithm based on variational inference

Abstract

Purpose The purpose of this paper is to alleviate the problem of poor robustness and over-fitting caused by large-scale data in collaborative filtering recommendation algorithms. Design/methodology/approach Interpreting user behavior from the probabilistic perspective of hidden variables is helpful to improve robustness and over-fitting problems. Constructing a recommendation network by variational inference can effectively solve the complex distribution calculation in the probabilistic recommendation model. Based on the aforementioned analysis, this paper uses variational auto-encoder to construct a generating network, which can restore user-rating data to solve the problem of poor robustness and over-fitting caused by large-scale data. Meanwhile, for the existing KL-vanishing problem in the variational inference deep learning model, this paper optimizes the model by the KL annealing and Free Bits methods. Findings The effect of the basic model is considerably improved after using the KL annealing or Free Bits method to solve KL vanishing. The proposed models evidently perform worse than competitors on small data sets, such as MovieLens 1 M. By contrast, they have better effects on large data sets such as MovieLens 10 M and MovieLens 20 M. Originality/value This paper presents the usage of the variational inference model for collaborative filtering recommendation and introduces the KL annealing and Free Bits methods to improve the basic model effect. Because the variational inference training denotes the probability distribution of the hidden vector, the problem of poor robustness and overfitting is alleviated. When the amount of data is relatively large in the actual application scenario, the probability distribution of the fitted actual data can better represent the user and the item. Therefore, using variational inference for collaborative filtering recommendation is of practical value.

Research on the differentiation of residents’ cultural consumption tendency and consumption recommendation system based on network inference algorithm

“…Trinh H C et al proposed Boolean network inference algorithm for the problem of insu cient network structure and dynamics methods for inferring gene regulatory networks from steady-state gene expression data, which improves the accuracy of prediction models (Trinh H C et al 2021) [6]. Zheng K team proposed to solve the problem of poor robustness and over tting caused by largescale data in collaborative ltering recommendation algorithms by using variational inference to construct recommendation networks, which can improve the performance of probabilistic recommendation models (Zheng K et al 2021) [7]. Ye J et al proposed a subset structure fusion based on the problem of accurate topological inference in nonsmooth networks network topology inference method, and the analysis and simulation results show that the method improves the accuracy of topology inference in nonsmooth networks (Ye J et al 2020) [8].Turabieh H's team proposed a dynamic adaptive network-based fuzzy inference system method to interpolate missing values in a simple and accurate way for the problem of IoMT systems that are prone to missing data.…”

“…For the user, the initial resource allocationf_{i}^{j} expression is shown in Eq. (7). f_{i}^{j}={a_{ij}}f(\phi ) 7…”

On Variational Inference for User Modeling in Attribute-Driven Collaborative Filtering

“…In terms of the application of variational inference and Bayesian statistics to solve collaborative filtering problems, most works focus on the use of Variational Auto Encoders. For example, [14] introduces a generative model with a multinomial likelihood and uses Bayesian inference for parameter estimation, [15] uses VAE to alleviate the problem of poor robustness and over-fitting caused by large-scale data, etc. Other works using Bayesian inference, such as [16], which presents a scalable inference for Variational Bayesian matrix factorization with side information, or [17], which proposes a distributed memo-free variational inference method for largescale matrix factorization problems, address some of the wellknown shortcomings of the same in recommender systems.…”

Research on personalised recommendation algorithm for college students’ employment

“…The employment recommendation of college students has always been a problem with high demand and high popularity, and many methods have been adopted to solve this problem [18]. For example, Zheng et al [19] proposed a new content-based employment recommendation algorithm, FoDRA, which quantitatively analyses and matches the suitability of job seekers by analysing the job requirements and content of job seekers' resumes. Siami-Namini et al [20] used text analysis technology to identify the professional skills of the user and the skills required by the position from the various data of the user's resume, so as to construct the user portrait and position portrait.…”