Construction of Mobile Screening Movie Recommendation Model Based on Artificial Immune Algorithm

Tianzhi Ren

doi:10.1155/2022/5700427

Abstract

The mobile screening of digital movies can fully take into account the viewing experience of scattered areas. As a public cultural service system, it is playing a pivotal role. The consistency of the film screened with the tastes of the audience in the service area of the screening team has largely affected the quality of rural public culture services. Traditional recommendation algorithms directly use raw data to make predictions, leading to deviations in predictions. This article draws on the principles of immune recognition, clone selection, immune mutation, and self-adaptation of the artificial immune system to improve the recommendation effect of single-type data, the recommendation effect of sparse data, and the recommendation effect of project cold start problems and discusses the recommendation based on artificial immunity. For the single type of data, there are only positive samples, which leads to the problem that the training results are all positive. This paper proposes a single-class recommendation algorithm based on artificial immunity. The algorithm uses the positive and negative sample addition method proposed in this paper to add positive and negative samples related to user selection, so as to effectively solve the problem of difficult definition of data negative samples. Then, the artificial immune network is used to cluster the users of various activities, reduce the size of the candidate neighbor set, calculate the user’s nearest neighbor set, and give recommendations.

Highlights

On the Internet today, there are already some tools to help users solve the problem of information overload, such as search engines [1]. ese search engines help users provide information filtering by entering keywords and keywords
One of the important aspects of the singleclass recommendation algorithm based on artificial immune proposed in this paper is the processing and selection of positive and negative samples. e most important parameter in this process is the ratio of positive and negative samples. e results are shown in Figure 5 below
Specific analysis shows that the reason is that the negative sample exceeds a certain level, which will affect the tendency of the training result to be negative and reduce the discrimination of the recommended result. e specific analysis shows that the reasons are as follows: first, the information of the positive samples is appropriately increased based on the similarity so that the sparsity of the data is significantly reduced, and new obtained training samples are obtained

Summary

Introduction

On the Internet today, there are already some tools to help users solve the problem of information overload, such as search engines [1]. ese search engines help users provide information filtering by entering keywords and keywords. It can be seen that recommendation is divided into classification or prediction problems This algorithm is mainly based on related data mining and machine learning methods in the processing process, and on this basis, a corresponding score prediction model is established, and predictive evaluation is performed through it. When the recommendation algorithm solves a single-class problem, the following problems need to be considered: first, the problem of sparseness, that is, there is little information related to user selection In this case, the data cannot be extracted effectively, which is a problem for further applications [23]. 0 2 4 6 8 10 12 14 16 Nearest user N μ=15 μ=20 Figure 4: e influence of different N on the recommendation effect of the algorithm

Results and Analysis

Analysis of Experimental Results

Conclusion