Browse Source
- add sort=match to company candidate list with in-memory scoring - matchScore normalized over known signals, integer clamped to 0-100 - matchReasons as short phrases, returned consistently for all sorts - config-driven signal weights under app.talent-match.weightbackup/master-local-20261012
pwklppg
2 months ago
11 changed files with 495 additions and 51 deletions
@ -0,0 +1,191 @@ |
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package com.mydreamjob.recommend; |
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import com.fasterxml.jackson.core.type.TypeReference; |
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import com.fasterxml.jackson.databind.ObjectMapper; |
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import com.mydreamjob.job.Job; |
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import com.mydreamjob.profile.StudentProfile; |
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import lombok.extern.slf4j.Slf4j; |
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import org.springframework.beans.factory.annotation.Value; |
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import org.springframework.stereotype.Component; |
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import java.math.BigDecimal; |
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import java.time.LocalDate; |
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import java.util.ArrayList; |
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import java.util.List; |
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import java.util.regex.Matcher; |
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import java.util.regex.Pattern; |
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/** |
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* 岗位匹配度确定性计算器（企业候选人看板 `sort=match`）。 |
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* |
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* <p>由 5 个加权信号组成（技能/学历/经验/城市/背景参考），每信号 0-100， |
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* 汇总后取整为 `matchScore`（0-100，四舍五入）。纯确定性规则，无 AI。 |
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* 信号权重来自 {@code app.talent-match.weight.*} 配置，总和约定为 1.00。</p> |
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*/ |
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@Slf4j |
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@Component |
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public class TalentMatchScorer { |
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private static final ObjectMapper MAPPER = new ObjectMapper(); |
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private static final Pattern EXPERIENCE_REQ = Pattern.compile("(\\d+)\\s*[-~至]\\s*(\\d+)\\s*年"); |
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private static final Pattern EXPERIENCE_MIN = Pattern.compile("([1-9]\\d*)\\s*年"); |
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// 学历档次（数值越大越高）
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private static final int LVL_PHD = 4; |
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private static final int LVL_MASTER = 3; |
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private static final int LVL_BACHELOR = 2; |
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private static final int LVL_ASSOCIATE = 1; |
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@Value("${app.talent-match.weight.skill:0.40}") |
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private double wSkill; |
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@Value("${app.talent-match.weight.edu:0.25}") |
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private double wEdu; |
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@Value("${app.talent-match.weight.exp:0.20}") |
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private double wExp; |
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@Value("${app.talent-match.weight.loc:0.10}") |
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private double wLoc; |
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@Value("${app.talent-match.weight.score:0.05}") |
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private double wScore; |
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/** |
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* 计算指定岗位与候选人档案的匹配度。overallScore 为候选综合评分（可为 null）。 |
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* |
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* <p>采用「已知信号加权归一化」：仅对两端数据都具备的信号计分， |
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* 缺失/无法判断的信号不参与（不因数据缺失而扣分）。 |
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* 分值 = Σ(已知信号得分 × 权重) / Σ(已知权重)，并夹取到 [0,100]。 |
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* |
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* @return matchScore（0-100 整数，不会低于 0 或超过 100）与简短理由数组 |
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*/ |
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public MatchResult match(Job job, StudentProfile profile, BigDecimal overallScore) { |
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List<String> reasons = new ArrayList<>(); |
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double weighted = 0.0; |
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double knownWeight = 0.0; |
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Double skill = skillScore(job, profile, reasons); |
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if (skill != null) { weighted += skill * wSkill; knownWeight += wSkill; } |
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Double edu = educationScore(job, profile, reasons); |
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if (edu != null) { weighted += edu * wEdu; knownWeight += wEdu; } |
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Double exp = experienceScore(job, profile, reasons); |
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if (exp != null) { weighted += exp * wExp; knownWeight += wExp; } |
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Double loc = cityScore(job, profile, reasons); |
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if (loc != null) { weighted += loc * wLoc; knownWeight += wLoc; } |
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Double bg = backgroundScore(overallScore, reasons); |
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if (bg != null) { weighted += bg * wScore; knownWeight += wScore; } |
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if (knownWeight == 0.0) { |
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return new MatchResult(0, List.copyOf(reasons)); |
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} |
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int matchScore = (int) Math.round(weighted / knownWeight); |
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matchScore = Math.max(0, Math.min(100, matchScore)); |
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return new MatchResult(matchScore, List.copyOf(reasons)); |
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} |
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/** 技能匹配：候选人技能命中岗位要求关键词的占比 → 0-100；数据不足返回 null */ |
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private Double skillScore(Job job, StudentProfile profile, List<String> reasons) { |
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if (profile == null || isBlank(profile.getSkillsJson())) return null; |
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if (job == null || isBlank(job.getRequirements())) return null; |
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List<String> skills = parseSkills(profile.getSkillsJson()); |
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if (skills.isEmpty()) return null; |
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// 忽略大小写与空格，兼容「Spring Boot」与「SpringBoot」同义的写法
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String reqNorm = job.getRequirements().toLowerCase().replace(" ", ""); |
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long hit = skills.stream() |
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.filter(s -> { |
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String sn = s.toLowerCase().replace(" ", ""); |
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return !sn.isBlank() && reqNorm.contains(sn); |
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}) |
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.count(); |
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double score = hit * 100.0 / skills.size(); |
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if (score > 0) reasons.add("技能匹配"); |
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return score; |
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} |
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/** 学历匹配：达到/高于岗位最低学历=100，低于按档位折算；要求未写明学历时返回 null */ |
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private Double educationScore(Job job, StudentProfile profile, List<String> reasons) { |
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if (profile == null || isBlank(profile.getEducation())) return null; |
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int reqLvl = job == null ? 0 : educationLevel(job.getRequirements()); |
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if (reqLvl == 0) return null; // 岗位未声明学历要求 → 该信号无法判断
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int candLvl = educationLevel(profile.getEducation()); |
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if (candLvl >= reqLvl) { |
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reasons.add("学历符合"); |
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return 100.0; |
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} |
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reasons.add("学历略低"); |
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return candLvl * 100.0 / reqLvl; |
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} |
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/** 经验匹配：毕业年数对照岗位要求经验区间 → 0-100；岗位未声明年限时返回 null */ |
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private Double experienceScore(Job job, StudentProfile profile, List<String> reasons) { |
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if (profile == null || profile.getGraduationYear() == null || profile.getGraduationYear() <= 0) return null; |
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String req = job == null ? "" : nullToEmpty(job.getRequirements()); |
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Integer required = parseExperience(req); |
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if (required == null) return null; // 岗位未声明经验要求 → 该信号无法判断
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int years = LocalDate.now().getYear() - profile.getGraduationYear(); |
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if (years >= required) { |
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reasons.add("经验匹配"); |
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return 100.0; |
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} |
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reasons.add("经验不足"); |
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return Math.max(0, years * 100.0 / required); |
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} |
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/** 城市匹配：岗位城市与候选人所在地一致=100，否则=50；任一缺失返回 null */ |
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private Double cityScore(Job job, StudentProfile profile, List<String> reasons) { |
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if (job == null || isBlank(job.getCity()) || profile == null || isBlank(profile.getLocation())) return null; |
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reasons.add(job.getCity().equals(profile.getLocation()) ? "城市匹配" : "城市待定"); |
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return job.getCity().equals(profile.getLocation()) ? 100.0 : 50.0; |
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} |
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/** 背景参考：候选综合评分映射档位；无评分时返回 null */ |
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private Double backgroundScore(BigDecimal overallScore, List<String> reasons) { |
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if (overallScore == null) return null; |
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double v = overallScore.doubleValue(); |
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if (v >= 85) { reasons.add("综合评分高"); return 100.0; } |
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if (v >= 60) { reasons.add("综合评分中"); return 70.0; } |
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reasons.add("综合评分低"); |
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return 40.0; |
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} |
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private int educationLevel(String text) { |
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if (isBlank(text)) return 0; |
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if (text.contains("博士")) return LVL_PHD; |
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if (text.contains("硕士") || text.contains("研究生")) return LVL_MASTER; |
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if (text.contains("本科")) return LVL_BACHELOR; |
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if (text.contains("大专") || text.contains("专科")) return LVL_ASSOCIATE; |
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return 0; |
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} |
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/** 解析经验要求：优先取区间下限，其次取"X年"最小值，解析不到返回 null */ |
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private Integer parseExperience(String text) { |
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Matcher range = EXPERIENCE_REQ.matcher(text); |
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if (range.find()) return Integer.valueOf(range.group(1)); |
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Matcher min = EXPERIENCE_MIN.matcher(text); |
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int lowest = Integer.MAX_VALUE; |
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while (min.find()) { |
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lowest = Math.min(lowest, Integer.parseInt(min.group(1))); |
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} |
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return lowest == Integer.MAX_VALUE ? null : lowest; |
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} |
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private List<String> parseSkills(String json) { |
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if (isBlank(json)) return List.of(); |
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try { |
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List<String> list = MAPPER.readValue(json, new TypeReference<List<String>>() {}); |
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return list == null ? List.of() : list.stream().filter(s -> s != null && !s.isBlank()).toList(); |
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} catch (Exception e) { |
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log.warn("failed to parse skillsJson: {}", e.getMessage()); |
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return List.of(); |
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} |
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} |
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private boolean isBlank(String s) { |
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return s == null || s.isBlank(); |
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} |
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private String nullToEmpty(String s) { |
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return s == null ? "" : s; |
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} |
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/** 匹配结果载体 */ |
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public record MatchResult(int matchScore, List<String> reasons) {} |
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} |
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@ -0,0 +1,130 @@ |
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package com.mydreamjob.recommend; |
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import com.mydreamjob.job.Job; |
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import com.mydreamjob.profile.StudentProfile; |
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import org.junit.jupiter.api.Test; |
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import org.springframework.test.util.ReflectionTestUtils; |
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import java.math.BigDecimal; |
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import java.util.List; |
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import static org.junit.jupiter.api.Assertions.*; |
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class TalentMatchScorerTest { |
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private TalentMatchScorer newScorer() { |
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TalentMatchScorer s = new TalentMatchScorer(); |
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// 注入与 application.yml 一致的默认权重（skill 0.40 / edu 0.25 / exp 0.20 / loc 0.10 / score 0.05）
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ReflectionTestUtils.setField(s, "wSkill", 0.40); |
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ReflectionTestUtils.setField(s, "wEdu", 0.25); |
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ReflectionTestUtils.setField(s, "wExp", 0.20); |
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ReflectionTestUtils.setField(s, "wLoc", 0.10); |
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ReflectionTestUtils.setField(s, "wScore", 0.05); |
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return s; |
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} |
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private Job job(String requirements, String city) { |
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return Job.builder().requirements(requirements).city(city).build(); |
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} |
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private StudentProfile profile(String education, String location, Short gradYear, String skillsJson) { |
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return StudentProfile.builder() |
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.education(education).location(location).graduationYear(gradYear) |
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.skillsJson(skillsJson).build(); |
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} |
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@Test void skillMatch_reasonIsSkillMatchWithoutDetail() { |
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Job j = job("熟悉Java、SpringBoot，三年以上经验，本科", "北京"); |
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StudentProfile p = profile("本科", "北京", (short) 2020, |
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"[\"Java\",\"SpringBoot\",\"Python\"]"); |
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TalentMatchScorer s = newScorer(); |
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TalentMatchScorer.MatchResult r = s.match(j, p, null); |
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assertTrue(r.reasons().contains("技能匹配")); |
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// 理由为简短短语，不含技能明细
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assertFalse(r.reasons().stream().anyMatch(x -> x.contains("Java") || x.contains("Spring"))); |
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} |
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@Test void educationMet_shouldBe100AndEduMatch() { |
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Job j = job("要求本科及以上", "北京"); |
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StudentProfile p = profile("硕士", "北京", (short) 2020, null); |
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TalentMatchScorer s = newScorer(); |
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TalentMatchScorer.MatchResult r = s.match(j, p, null); |
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assertTrue(r.reasons().contains("学历符合")); |
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// 已知信号=学历(100×0.25)+城市(100×0.10)，累计 0.35 → 100/100
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assertEquals(100, r.matchScore()); |
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} |
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@Test void educationBelow_shouldBeLowerAndEduLow() { |
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Job j = job("要求本科", "北京"); |
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StudentProfile p = profile("大专", "北京", (short) 2020, null); |
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TalentMatchScorer s = newScorer(); |
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TalentMatchScorer.MatchResult r = s.match(j, p, null); |
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assertTrue(r.reasons().contains("学历略低")); |
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// (50×0.25 + 100×0.10)/(0.25+0.10) = 22.5/0.35 ≈ 64
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assertEquals(64, r.matchScore()); |
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} |
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@Test void cityMatch_versusDifferentCity() { |
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Job j = job("本科", "北京"); |
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StudentProfile same = profile("本科", "北京", (short) 2020, null); |
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StudentProfile diff = profile("本科", "上海", (short) 2020, null); |
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TalentMatchScorer s = newScorer(); |
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TalentMatchScorer.MatchResult rSame = s.match(j, same, null); |
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TalentMatchScorer.MatchResult rDiff = s.match(j, diff, null); |
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assertTrue(rSame.reasons().contains("城市匹配")); |
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assertTrue(rDiff.reasons().contains("城市待定")); |
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assertTrue(rSame.matchScore() > rDiff.matchScore()); |
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// 同城 (100×0.25+100×0.10)/0.35=100；异城 (100×0.25+50×0.10)/0.35≈86
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assertEquals(100, rSame.matchScore()); |
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assertEquals(86, rDiff.matchScore()); |
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} |
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@Test void experienceMet_versusLack() { |
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Job j = job("3年以上经验", "北京"); |
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StudentProfile met = profile("本科", "北京", (short) 2018, null); // 8 年
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StudentProfile lack = profile("本科", "北京", (short) 2025, null); // 1 年
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TalentMatchScorer s = newScorer(); |
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TalentMatchScorer.MatchResult rMet = s.match(j, met, null); |
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TalentMatchScorer.MatchResult rLack = s.match(j, lack, null); |
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assertTrue(rMet.reasons().contains("经验匹配")); |
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assertTrue(rLack.reasons().contains("经验不足")); |
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assertTrue(rMet.matchScore() > rLack.matchScore()); |
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} |
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@Test void backgroundScoreHighAndLow() { |
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Job j = job("本科", "北京"); |
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StudentProfile p = profile("本科", "北京", (short) 2020, null); |
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TalentMatchScorer s = newScorer(); |
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TalentMatchScorer.MatchResult rHigh = s.match(j, p, new BigDecimal("90.00")); |
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TalentMatchScorer.MatchResult rLow = s.match(j, p, new BigDecimal("40.00")); |
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assertTrue(rHigh.reasons().contains("综合评分高")); |
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assertTrue(rLow.reasons().contains("综合评分低")); |
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assertTrue(rHigh.matchScore() > rLow.matchScore()); |
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} |
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@Test void missingData_shouldNotNpeAndScoreDegraded() { |
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Job j = job("本科", "北京"); |
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TalentMatchScorer s = newScorer(); |
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TalentMatchScorer.MatchResult r = s.match(j, null, null); |
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assertEquals(0, r.matchScore()); |
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assertTrue(r.reasons().isEmpty()); |
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} |
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@Test void allSignalsHit_intScoreAndShortReasons() { |
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Job j = job("要求本科，熟悉Java，3年经验", "北京"); |
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StudentProfile p = profile("硕士", "北京", (short) 2018, |
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"[\"Java\",\"SpringBoot\"]"); |
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TalentMatchScorer s = newScorer(); |
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TalentMatchScorer.MatchResult r = s.match(j, p, new BigDecimal("90.00")); |
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// 整数 0-100
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assertTrue(r.matchScore() >= 0 && r.matchScore() <= 100); |
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assertEquals(r.matchScore(), (int) Math.round(r.matchScore())); |
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// 每信号至多一句
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assertTrue(r.reasons().size() <= 5); |
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assertTrue(r.reasons().contains("技能匹配")); |
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assertTrue(r.reasons().contains("学历符合")); |
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assertTrue(r.reasons().contains("经验匹配")); |
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assertTrue(r.reasons().contains("城市匹配")); |
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assertTrue(r.reasons().contains("综合评分高")); |
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} |
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} |
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