用java实现岗位推荐基于内容的推荐算法代码

岗位推荐常用的基于内容的推荐算法是基于tf-idf权重的文本相似度计算。以下是一个简单的Java实现示例：

import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

public class ContentBasedJobRecommendation {

    // 岗位描述文本数据，格式为Map<岗位ID, 岗位描述>
    private Map<Integer, String> jobDescriptions = new HashMap<>();

    // 岗位特征向量，格式为Map<岗位ID, Map<词项, 词项权重>>
    private Map<Integer, Map<String, Double>> jobFeatureVectors = new HashMap<>();

    /**
     * 添加一个岗位的描述文本数据
     *
     * @param id       岗位ID
     * @param jobDesc  岗位描述
     */
    public void addJobDescription(int id, String jobDesc) {
        jobDescriptions.put(id, jobDesc);
        updateJobFeatureVector(id, jobDesc);
    }

    /**
     * 更新一个岗位的特征向量
     *
     * @param id       岗位ID
     * @param jobDesc  岗位描述
     */
    private void updateJobFeatureVector(int id, String jobDesc) {
        Map<String, Double> featureVector = new HashMap<>();
        String[] terms = jobDesc.split("\\s+");
        for (String term : terms) {
            Double tf = featureVector.get(term);
            if (tf == null) {
                tf = 0.0;
            }
            featureVector.put(term, tf + 1.0);
        }

        double norm = 0.0;
        for (Double tf : featureVector.values()) {
            norm += tf * tf;
        }
        norm = Math.sqrt(norm);
        for (Map.Entry<String, Double> entry : featureVector.entrySet()) {
            entry.setValue(entry.getValue() / norm);
        }

        jobFeatureVectors.put(id, featureVector);
    }

    /**
     * 根据输入的简历文本，推荐与之最匹配的K个岗位
     *
     * @param resume   简历文本
     * @param topK     推荐的岗位数量
     * @return         推荐的岗位ID列表
     */
    public List<Integer> recommendJobs(String resume, int topK) {
        Map<String, Double> resumeFeatureVector = new HashMap<>();
        String[] terms = resume.split("\\s+");
        for (String term : terms) {
            Double tf = resumeFeatureVector.get(term);
            if (tf == null) {
                tf = 0.0;
            }
            resumeFeatureVector.put(term, tf + 1.0);
        }

        double norm = 0.0;
        for (Double tf : resumeFeatureVector.values()) {
            norm += tf * tf;
        }
        norm = Math.sqrt(norm);
        for (Map.Entry<String, Double> entry : resumeFeatureVector.entrySet()) {
            entry.setValue(entry.getValue() / norm);
        }

        List<Map.Entry<Integer, Double>> jobScores = new ArrayList<>();
        for (Map.Entry<Integer, Map<String, Double>> entry : jobFeatureVectors.entrySet()) {
            Integer jobId = entry.getKey();
            Map<String, Double> jobVector = entry.getValue();

            double score = 0.0;
            for (Map.Entry<String, Double> termEntry : resumeFeatureVector.entrySet()) {
                String term = termEntry.getKey();
                Double tfidf = jobVector.get(term);
                if (tfidf != null) {
                    score += termEntry.getValue() * tfidf;
                }
            }

            jobScores.add(Map.entry(jobId, score));
        }

        jobScores.sort((o1, o2) -> Double.compare(o2.getValue(), o1.getValue()));

        List<Integer> recommendedJobs = new ArrayList<>();
        for (int i = 0; i < topK && i < jobScores.size(); i++) {
            recommendedJobs.add(jobScores.get(i).getKey());
        }

        return recommendedJobs;
    }
}

示例代码中，`addJobDescription`方法用于添加岗位描述文本数据，`recommendJobs`方法用于根据输入的简历文本推荐与之最匹配的K个岗位。在`updateJobFeatureVector`方法中，对每个岗位的特征向量进行计算和归一化；在`recommendJobs`方法中，对输入的简历文本也进行特征向量计算和归一化，并计算与每个岗位的相似度得分，并根据得分排序返回前K个岗位。