Batch Resume Screener

Total Score = Academic Background × Weight1 + Career Stability × Weight2 + Tech Stack × Weight3 
            + Project Match × Weight4 + Problem Solving × Weight5 + Learning Ability × Weight6 + Bonus × Weight7

Scoring Rules:
1. Institution Level (40 points):
   - 985/top institutions: 40 points
   - 211/double first-class: 30 points
   - Provincial key undergraduate: 20 points
   - Ordinary full-time undergraduate: 10 points
   - Associate degree: 5 points

2. Major Match (40 points):
   - Computer Science/Software Engineering: 40 points
   - Mathematics/Automation: 20 points
   - Cross-major with relevant certificates: 10 points
   - Completely cross-major: 0 points

3. Academic Performance (20 points):
   - Scholarships/ranking proof: 20 points
   - No relevant description: 0 points

Score = Institution Level + Major Match + Academic Performance

Scoring Rules (based only on clearly calculable time data from resume):

1. Average Tenure (60 points):
   - Average tenure ≥3 years: 60 points
   - Average tenure 2-3 years: 40 points
   - Average tenure 1-2 years: 20 points
   - Average tenure <1 year: 0 points

2. Job-hopping Frequency (40 points):
   - <1 job change per year: 40 points
   - ~1 job change per year: 20 points
   - ≥2 job changes per year: 0 points

Score = Average Tenure + Job-hopping Frequency

Scoring Rules:

1. Tech Stack Match (35 points):
   - 100% coverage of core technologies: 35 points
   - ≥80% coverage: 25 points
   - ≥60% coverage: 15 points
   - <60% coverage: 0 points

2. Tech Stack Breadth (20 points):
   - Covers backend frameworks, databases, caches, message queues, DevOps, etc.: 20 points
   - Covers basic backend technologies: 10 points
   - Narrow technology range: 0 points

3. Tech Stack Depth (25 points):
   - Source code understanding, tuning experience: 25 points
   - Proficient usage: 15 points
   - Surface-level understanding only: 0 points

4. Practical Experience (20 points):
   - Clear multi-project practice descriptions: 20 points
   - Basic practice descriptions: 10 points
   - Lack of practice descriptions: 0 points

Score = Match + Breadth + Depth + Practical

Scoring Rules (5 aspects, 20 points each):

1. Business Domain & Industry Match:
   - Complete match with job business: 20 points
   - Partial match: 10 points
   - No match: 0 points

2. Project Scale & Complexity:
   - Equal to or higher than job requirements: 20 points
   - Slightly below job requirements: 10 points
   - Significantly below job requirements: 0 points

3. Personal Responsibility & Involvement:
   - Core developer/lead: 20 points
   - Core feature development: 15 points
   - Non-core module development: 8 points
   - Low involvement: 0 points

4. Technical Difficulty & Highlights:
   - Technical highlights/breakthroughs: 20 points
   - Some technical challenges: 10 points
   - Routine CRUD: 0 points

5. Project Results & Value:
   - Quantified results: 20 points
   - Qualitative result descriptions: 10 points
   - No result descriptions: 0 points

Score = Business + Scale + Responsibility + Difficulty + Results

Scoring Rules (quantifiable indicators can stack, max 100 points):

Quantifiable Indicators:
- Clear performance improvement data (e.g., "improved 50%"): +20 points
- Clear cost reduction data (e.g., "saved 30% server cost"): +20 points
- Clear user growth data: +15 points
- Complete technical solution description: +15 points
- Online issue troubleshooting cases: +15 points
- Architecture optimization/refactoring cases: +15 points

Score = min(sum of all indicators, 100)

Scoring Rules (indicators can stack, max 100 points):

Assessment Indicators:
- Self-learned new technology and completed full project: +25 points
- High-quality open source projects/technical blogs: +20 points
- Technical competition awards: +20 points
- Relevant technical certificates: +15 points
- Quickly took over unfamiliar business and produced results: +15 points
- Clear technical growth trajectory: +10 points

Score = min(sum of all indicators, 100)

Scoring Rules:

1. Job Priority Items Satisfaction (40 points):
   - Satisfies all or most priority items: 40 points
   - Satisfies some priority items: 20 points
   - Does not satisfy any priority items: 0 points

2. Technical Certifications (20 points):
   - Relevant advanced certifications: 20 points
   - Relevant basic certifications: 10 points
   - No relevant certifications: 0 points

3. Technical Influence (20 points):
   - Open source contributions/technical blogs/technical sharing: 20 points
   - Some technical sharing records: 10 points
   - No technical influence proof: 0 points

4. Other Highlights (20 points):
   - Award records/special achievements: 20 points
   - General highlights: 10 points
   - No other highlights: 0 points

Score = Priority Items + Certifications + Influence + Other

{
  "confidence": {
    "score": 0.85,
    "level": "high",
    "factors": {
      "resume_completeness": 0.9,
      "information_clarity": 0.8,
      "verifiable_data": 0.85
    },
    "suggestions": [
      "Resume project descriptions are detailed, scoring basis sufficient",
      "Recommend verifying work experience during interview"
    ]
  }
}

{
  "candidate_name": "候选人姓名",
  "resume_file": "简历文件名.txt",
  "evaluation_time": "YYYY-MM-DD HH:MM:SS",
  "job_level": "junior/mid/senior",
  "weight_coefficients": {
    "academic_background": 0.15,
    "career_stability": 0.05,
    "tech_stack": 0.15,
    "project_match": 0.20,
    "problem_solving": 0.10,
    "learning_ability": 0.20,
    "bonus": 0.15
  },
  "pre_filter": {
    "match_level": "high_match/medium_match/low_match",
    "key_findings": ["关键发现1", "关键发现2"]
  },
  "positions": [
    {
      "position_name": "岗位名称",
      "hard_requirements_check": {
        "passed": true/false,
        "rejection_reason": "如果未通过，说明原因"
      },
      "dimension_scores": {
        "academic_background": {
          "score": 85,
          "breakdown": {
            "institution_level": 30,
            "major_match": 40,
            "academic_performance": 15
          },
          "reason": "评分理由"
        },
        "career_stability": {
          "score": 70,
          "breakdown": {
            "average_tenure": 40,
            "job_hopping_frequency": 30
          },
          "reason": "评分理由"
        },
        "tech_stack": {
          "score": 75,
          "breakdown": {
            "match": 25,
            "breadth": 15,
            "depth": 20,
            "practical": 15
          },
          "reason": "评分理由"
        },
        "project_match": {
          "score": 80,
          "breakdown": {
            "business_match": 15,
            "project_scale": 18,
            "responsibility": 17,
            "technical_difficulty": 15,
            "achievement": 15
          },
          "reason": "评分理由"
        },
        "problem_solving": {
          "score": 65,
          "reason": "评分理由"
        },
        "learning_ability": {
          "score": 70,
          "reason": "评分理由"
        },
        "bonus": {
          "score": 60,
          "breakdown": {
            "job_priority": 20,
            "certifications": 10,
            "influence": 20,
            "other": 10
          },
          "reason": "评分理由"
        }
      },
      "weighted_score": 72.5,
      "rating": "推荐面试",
      "recommended": true
    }
  ],
  "best_position": {
    "position_name": "推荐岗位名称",
    "weighted_score": 72.5,
    "rating": "推荐面试"
  },
  "confidence": {
    "score": 0.85,
    "level": "high",
    "factors": {
      "resume_completeness": 0.9,
      "information_clarity": 0.8,
      "verifiable_data": 0.85
    },
    "suggestions": [
      "简历项目描述较为详细，评分依据充分"
    ]
  }
}

---

# 批量简历初筛结果汇总报告

## 统计概览

- 总简历数：XX份
- 总岗位数：XX个
- 筛选完成时间：YYYY-MM-DD HH:MM:SS，耗时：xxx
- 岗位级别分布：初级X个，中级X个，高级X个

---

## 候选人排名（按加权总分降序排列）

| 排名 | 候选人姓名 | 推荐岗位 | 岗位级别 | 加权总分 | 综合评级 | 置信度 | 学术背景 | 职业稳定性 | 技术栈能力 | 项目经验匹配 | 问题解决能力 | 学习能力 | 加分项 |
|------|------------|----------|----------|----------|----------|--------|----------|------------|------------|--------------|--------------|----------|--------|
| 1 | 张三 | Java高级工程师 | 高级 | 85 | 推荐面试 | 高 | 75 | 80 | 85 | 90 | 80 | 75 | 70 |
| 2 | 李四 | 后端开发工程师 | 中级 | 78 | 推荐面试 | 高 | 70 | 75 | 80 | 85 | 70 | 80 | 65 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |

---

## 通过候选人综合评价

| 候选人姓名 | 推荐岗位 | 综合评价 | 关键亮点 |
|------------|----------|----------|----------|
| 张三 | Java高级工程师 | 技术栈全面，有源码阅读经验，有大型项目经验，问题解决能力强 | 熟悉JVM调优，有分布式架构经验 |
| ... | ... | ... | ... |

---

## 未通过候选人汇总

| 候选人姓名 | 未通过原因 | 涉及岗位 | 置信度 |
|------------|------------|----------|--------|
| 王五 | 学历不满足（岗位要求本科及以上，简历显示专科） | Java高级工程师, 后端开发工程师 | 高 |
| ... | ... | ... | ... |

---

## 低置信度候选人（建议人工复核）

| 候选人姓名 | 置信度 | 复核建议 |
|------------|--------|----------|
| 赵六 | 低(0.55) | 简历信息不完整，建议核实工作年限和项目经验 |
| ... | ... | ... |

---

候选人姓名,推荐岗位,岗位级别,加权总分,综合评级,置信度,学术背景,职业稳定性,技术栈能力,项目经验匹配,问题解决能力,学习能力,加分项,关键亮点,评估时间
张三,Java高级工程师,高级,85,推荐面试,高,75,80,85,90,80,75,70,熟悉JVM调优,2024-01-15 10:30:00
李四,后端开发工程师,中级,78,推荐面试,高,70,75,80,85,70,80,65,有微服务经验,2024-01-15 10:35:00

## 候选人横向对比表

| 对比维度 | 张三 | 李四 | 王五 |
|----------|------|------|------|
| 推荐岗位 | Java高级工程师 | 后端开发工程师 | - |
| 加权总分 | 85 | 78 | 45 |
| 学术背景 | 75 | 70 | 60 |
| 职业稳定性 | 80 | 75 | 50 |
| 技术栈能力 | 85 | 80 | 55 |
| 项目经验匹配 | 90 | 85 | 40 |
| 问题解决能力 | 80 | 70 | 35 |
| 学习能力 | 75 | 80 | 50 |
| 加分项 | 70 | 65 | 30 |
| 核心优势 | JVM调优、分布式架构 | 微服务、高并发 | - |
| 主要不足 | - | - | 学历不满足、经验不足 |

## 候选人亮点摘要（供面试官参考）

### 张三 - Java高级工程师（加权总分：85）
**核心优势**：
- 熟悉JVM调优，有实际性能优化经验
- 有分布式架构设计和实施经验
- 项目经验丰富，有大型系统开发经验

**面试建议**：
- 深入了解JVM调优的具体案例
- 询问分布式架构中的难点和解决方案

---

### 李四 - 后端开发工程师（加权总分：78）
**核心优势**：
- 有微服务架构实践经验
- 高并发场景有实际处理经验
- 学习能力强，有技术博客

**面试建议**：
- 了解微服务拆分的思路和经验
- 询问高并发场景的具体处理方案