💖💖作者：计算机编程小咖 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学习实战项目
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基于机器学习的就业岗位推荐系统介绍
《基于机器学习的就业岗位推荐系统》是一个集智能推荐、招聘管理和求职服务于一体的综合性Web应用系统，采用SpringBoot作为后端框架，结合Vue和ElementUI构建用户友好的前端界面，使用MySQL数据库存储海量招聘数据。系统核心亮点在于运用机器学习算法实现智能岗位推荐功能，通过分析用户简历信息、技能匹配度、行业偏好等多维度数据，为求职者精准推荐最适合的就业岗位，同时为企业智能筛选匹配度高的候选人。系统功能模块完整丰富，包含用户管理、企业管理、招聘信息发布、简历投递、面试安排等核心业务流程，还集成了薪资预测功能，利用机器学习模型根据岗位要求、地区、行业类别等因素预测薪资水平。系统采用B/S架构设计，支持多角色权限管理，求职者可以创建个人简历、浏览岗位信息、在线投递简历，企业用户可以发布招聘信息、查看应聘者简历、安排面试流程，管理员则负责用户审核、系统配置、数据统计等后台管理工作。此外，系统还提供公告信息管理、轮播图展示、系统日志记录等辅助功能，确保系统运行稳定且用户体验良好，为现代化的人力资源管理提供了智能化、自动化的技术解决方案。
基于机器学习的就业岗位推荐系统演示视频
基于机器学习的就业岗位推荐系统演示图片
基于机器学习的就业岗位推荐系统代码展示
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.ml.feature.VectorAssembler;
import org.apache.spark.ml.classification.RandomForestClassifier;
import org.apache.spark.ml.regression.LinearRegression;
import org.apache.spark.ml.Pipeline;
import org.apache.spark.ml.PipelineModel;
import org.springframework.stereotype.Service;
import org.springframework.beans.factory.annotation.Autowired;
import java.util.*;
@Service
public class JobRecommendationService {
private SparkSession spark = SparkSession.builder().appName("JobRecommendationSystem").master("local[*]").getOrCreate();
@Autowired
private UserMapper userMapper;
@Autowired
private JobMapper jobMapper;
@Autowired
private ResumeMapper resumeMapper;
public List<JobRecommendation> getPersonalizedJobRecommendations(Long userId) {
User user = userMapper.selectById(userId);
Resume resume = resumeMapper.selectByUserId(userId);
List<Job> allJobs = jobMapper.selectAllActiveJobs();
Dataset<Row> userJobData = spark.read().option("header", "true").option("inferSchema", "true").csv("user_job_interactions.csv");
Dataset<Row> jobFeatures = spark.read().option("header", "true").option("inferSchema", "true").csv("job_features.csv");
Dataset<Row> userFeatures = spark.createDataFrame(Arrays.asList(
RowFactory.create(userId, resume.getEducationLevel(), resume.getExperienceYears(),
resume.getSkillScore(), user.getPreferredSalary(), user.getPreferredLocation())
), new StructType().add("userId", DataTypes.LongType).add("education", DataTypes.IntegerType)
.add("experience", DataTypes.IntegerType).add("skillScore", DataTypes.DoubleType)
.add("preferredSalary", DataTypes.DoubleType).add("preferredLocation", DataTypes.IntegerType));
Dataset<Row> combinedData = userJobData.join(jobFeatures, "jobId").join(userFeatures, "userId");
VectorAssembler assembler = new VectorAssembler().setInputCols(new String[]{"education", "experience", "skillScore", "jobRequiredEducation", "jobRequiredExperience", "jobSalaryRange", "jobLocationId"}).setOutputCol("features");
RandomForestClassifier rf = new RandomForestClassifier().setLabelCol("isMatch").setFeaturesCol("features").setNumTrees(100);
Pipeline pipeline = new Pipeline().setStages(new PipelineStage[]{assembler, rf});
PipelineModel model = pipeline.fit(combinedData);
List<JobRecommendation> recommendations = new ArrayList<>();
for (Job job : allJobs) {
Dataset<Row> testData = spark.createDataFrame(Arrays.asList(
RowFactory.create(resume.getEducationLevel(), resume.getExperienceYears(), resume.getSkillScore(),
job.getRequiredEducation(), job.getRequiredExperience(), job.getSalaryRange(), job.getLocationId())
), assembler.getInputSchema());
Dataset<Row> predictions = model.transform(testData);
double matchScore = predictions.select("probability").first().getAs(Vector.class, 0).toArray()[1];
if (matchScore > 0.6) {
JobRecommendation recommendation = new JobRecommendation();
recommendation.setJobId(job.getJobId());
recommendation.setUserId(userId);
recommendation.setMatchScore(matchScore);
recommendation.setRecommendReason(generateRecommendReason(resume, job, matchScore));
recommendations.add(recommendation);
}
}
recommendations.sort((a, b) -> Double.compare(b.getMatchScore(), a.getMatchScore()));
return recommendations.subList(0, Math.min(10, recommendations.size()));
}
public SalaryPredictionResult predictSalary(SalaryPredictionRequest request) {
Dataset<Row> salaryData = spark.read().option("header", "true").option("inferSchema", "true").csv("salary_dataset.csv");
Dataset<Row> filteredData = salaryData.filter("industryId = " + request.getIndustryId() + " AND locationId = " + request.getLocationId());
VectorAssembler assembler = new VectorAssembler().setInputCols(new String[]{"education", "experience", "skillLevel", "industryId", "locationId", "companySize"}).setOutputCol("features");
Dataset<Row> assembledData = assembler.transform(filteredData);
LinearRegression lr = new LinearRegression().setLabelCol("salary").setFeaturesCol("features").setMaxIter(100).setRegParam(0.1);
Dataset<Row>[] splits = assembledData.randomSplit(new double[]{0.8, 0.2}, 42);
Dataset<Row> trainingData = splits[0];
Dataset<Row> testData = splits[1];
LinearRegressionModel model = lr.fit(trainingData);
Dataset<Row> inputData = spark.createDataFrame(Arrays.asList(
RowFactory.create(request.getEducation(), request.getExperience(), request.getSkillLevel(),
request.getIndustryId(), request.getLocationId(), request.getCompanySize())
), assembler.getInputSchema());
Dataset<Row> predictionData = assembler.transform(inputData);
Dataset<Row> predictions = model.transform(predictionData);
double predictedSalary = predictions.select("prediction").first().getDouble(0);
Dataset<Row> industryStats = salaryData.filter("industryId = " + request.getIndustryId()).agg(avg("salary").alias("avgSalary"), min("salary").alias("minSalary"), max("salary").alias("maxSalary"));
Row stats = industryStats.first();
SalaryPredictionResult result = new SalaryPredictionResult();
result.setPredictedSalary(Math.round(predictedSalary));
result.setIndustryAverage(Math.round(stats.getDouble(0)));
result.setSalaryRange(Math.round(stats.getDouble(1)) + " - " + Math.round(stats.getDouble(2)));
result.setConfidenceLevel(calculateConfidenceLevel(model, testData));
result.setPredictionFactors(generatePredictionFactors(request, stats.getDouble(0)));
return result;
}
public ResumeMatchResult matchResumeToJob(Long resumeId, Long jobId) {
Resume resume = resumeMapper.selectById(resumeId);
Job job = jobMapper.selectById(jobId);
Dataset<Row> resumeJobData = spark.read().option("header", "true").option("inferSchema", "true").csv("resume_job_matches.csv");
Dataset<Row> skillsData = spark.read().option("header", "true").option("inferSchema", "true").csv("skills_mapping.csv");
List<String> resumeSkills = Arrays.asList(resume.getSkills().split(","));
List<String> jobRequiredSkills = Arrays.asList(job.getRequiredSkills().split(","));
Dataset<Row> resumeSkillsDF = spark.createDataFrame(resumeSkills.stream().map(skill -> RowFactory.create(skill.trim())).collect(Collectors.toList()), new StructType().add("skill", DataTypes.StringType));
Dataset<Row> jobSkillsDF = spark.createDataFrame(jobRequiredSkills.stream().map(skill -> RowFactory.create(skill.trim())).collect(Collectors.toList()), new StructType().add("skill", DataTypes.StringType));
Dataset<Row> matchedSkills = resumeSkillsDF.join(jobSkillsDF, "skill");
long matchedSkillCount = matchedSkills.count();
double skillMatchRate = (double) matchedSkillCount / jobRequiredSkills.size();
double educationMatch = calculateEducationMatch(resume.getEducationLevel(), job.getRequiredEducation());
double experienceMatch = calculateExperienceMatch(resume.getExperienceYears(), job.getRequiredExperience());
double locationMatch = resume.getPreferredLocation().equals(job.getLocation()) ? 1.0 : 0.5;
double salaryMatch = calculateSalaryMatch(resume.getExpectedSalary(), job.getSalaryMin(), job.getSalaryMax());
Dataset<Row> matchFeatures = spark.createDataFrame(Arrays.asList(
RowFactory.create(skillMatchRate, educationMatch, experienceMatch, locationMatch, salaryMatch)
), new StructType().add("skillMatch", DataTypes.DoubleType).add("educationMatch", DataTypes.DoubleType)
.add("experienceMatch", DataTypes.DoubleType).add("locationMatch", DataTypes.DoubleType).add("salaryMatch", DataTypes.DoubleType));
VectorAssembler assembler = new VectorAssembler().setInputCols(new String[]{"skillMatch", "educationMatch", "experienceMatch", "locationMatch", "salaryMatch"}).setOutputCol("features");
Dataset<Row> assembledFeatures = assembler.transform(matchFeatures);
double[] weights = {0.4, 0.2, 0.2, 0.1, 0.1};
double overallMatchScore = skillMatchRate * weights[0] + educationMatch * weights[1] + experienceMatch * weights[2] + locationMatch * weights[3] + salaryMatch * weights[4];
ResumeMatchResult result = new ResumeMatchResult();
result.setResumeId(resumeId);
result.setJobId(jobId);
result.setOverallMatchScore(Math.round(overallMatchScore * 100.0) / 100.0);
result.setSkillMatchRate(Math.round(skillMatchRate * 100.0) / 100.0);
result.setEducationMatchScore(educationMatch);
result.setExperienceMatchScore(experienceMatch);
result.setLocationMatchScore(locationMatch);
result.setSalaryMatchScore(salaryMatch);
result.setMatchedSkills(matchedSkills.select("skill").as(Encoders.STRING()).collectAsList());
result.setMissingSkills(jobSkillsDF.except(resumeSkillsDF).select("skill").as(Encoders.STRING()).collectAsList());
result.setMatchLevel(determineMatchLevel(overallMatchScore));
result.setSuggestions(generateMatchSuggestions(result));
return result;
}
}
基于机器学习的就业岗位推荐系统文档展示
💖💖作者：计算机编程小咖 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学习实战项目
