第六章：前沿进展与未来方向

Algorithm: Greedy Diverse Ranking
Input: 候选集D, 参数λ, k
Output: 排序列表S

S ← ∅
covered_categories ← ∅
for i = 1 to k:
    best_score ← -∞
    best_doc ← null

    for d in D \ S:
        # 计算NDCG增益
        ndcg_gain = NDCG(S ∪ {d}) - NDCG(S)

        # 计算多样性增益
        new_cats = categories(d) \ covered_categories
        div_gain = |new_cats| / |all_categories|

        # 组合得分
        score = λ × ndcg_gain + (1-λ) × div_gain

        if score > best_score:
            best_score ← score
            best_doc ← d

    S ← S ∪ {best_doc}
    covered_categories ← covered_categories ∪ categories(best_doc)

return S

def ensemble_ranking(query, docs, n_runs=5):
    rankings = []
    for _ in range(n_runs):
        rank = llm_rank(query, docs)
        rankings.append(rank)

    # Borda count聚合
    scores = defaultdict(int)
    for rank in rankings:
        for pos, doc in enumerate(rank):
            scores[doc] += len(docs) - pos

    return sorted(docs, key=lambda d: scores[d], reverse=True)

提示词增强：
"请给出排序结果。注意：相同的查询应该产生相同的排序。
请基于以下固定规则进行排序：

1. 精确匹配优先
2. 语义相关性次之
3. 时效性作为补充"

Algorithm: Fair Online Learning to Rank
Parameters: 

  - α: 探索参数
  - β: 公平性权重
  - G: 群体集合

Initialize:

  - θ_g ← 0 for all g ∈ G (群体参数)
  - n_g ← 0 (群体展示次数)
  - r_g ← 0 (群体累积奖励)

For each query q:

  1. 获取候选文档D和群体标签

  2. 计算UCB分数：
     UCB_d = μ_d + α√(log t / n_d) + β·Fair_d

     其中Fair_d = max(0, target_exposure_g - atual_exposure_g)

  3. 选择top-k文档基于UCB分数

  4. 观察用户反馈，更新参数：
     - 使用梯度下降更新θ_g
     - 更新展示计数n_g
     - 更新奖励估计r_g

def cold_start_prior(group):
    # 基于群体元信息设置先验
    if group.is_minority:
        # 乐观先验，鼓励探索
        return Beta(α=2, β=1)
    else:
        # 中性先验
        return Beta(α=1, β=1)

def sample_relevance(doc, group):
    if doc.impressions < threshold:
        # 冷启动：从先验采样
        prior = cold_start_prior(group)
        return prior.sample()
    else:
        # 温启动：从后验采样
        return posterior[doc].sample()

class DriftAdapter:
    def __init__(self, window_size=1000):
        self.window = deque(maxlen=window_size)
        self.baseline_stats = None

    def detect_drift(self, new_data):
        self.window.append(new_data)

        if len(self.window) == window_size:
            current_stats = compute_stats(self.window)

            # KL散度检测分布变化
            kl_div = KL(baseline_stats, current_stats)

            if kl_div > threshold:
                # 检测到漂移，重置模型
                self.reset_model()
                self.baseline_stats = current_stats

    def reset_model(self):
        # 保留部分知识，加快重新学习
        global θ_g
        θ_g = θ_g * decay_factor

search_space = {
    'embedding': {
        'type': ['dense', 'sparse', 'hybrid'],
        'dim': [128, 256, 512],
        'activation': ['relu', 'gelu', 'swish']
    },
    'interaction': {
        'type': ['concat', 'hadamard', 'attention'],
        'layers': [1, 2, 3, 4],
        'hidden_dim': [256, 512, 1024]
    },
    'aggregation': {
        'type': ['mean', 'max', 'attention_weighted'],
        'dropout': [0.1, 0.2, 0.3]
    }
}

class SuperNet(nn.Module):
    def __init__(self):
        # 构建所有可能的操作
        self.embeddings = nn.ModuleDict({
            'dense': DenseEmbedding(),
            'sparse': SparseEmbedding(),
            'hybrid': HybridEmbedding()
        })

        self.interactions = nn.ModuleList([
            InteractionLayer(type) 
            for type in ['concat', 'hadamard', 'attention']
        ])

        # 架构参数（可学习）
        self.arch_params = nn.Parameter(
            torch.randn(len(search_space))
        )

    def forward(self, query, doc, arch_weights=None):
        if arch_weights is None:
            # 使用Gumbel-Softmax采样
            arch_weights = F.gumbel_softmax(
                self.arch_params, tau=temperature
            )

        # 根据架构权重组合操作
        embed = sum(
            w * emb(query, doc) 
            for w, emb in zip(arch_weights[:3], self.embeddings.values())
        )

        # ... 类似处理interaction和aggregation

        return score

def search_architecture(train_data, val_data, epochs=50):
    supernet = SuperNet()

    # 双层优化
    arch_optimizer = optim.Adam(
        [supernet.arch_params], lr=3e-4
    )
    weight_optimizer = optim.Adam(
        supernet.parameters(), lr=1e-3
    )

    for epoch in range(epochs):
        # 步骤1：更新网络权重（固定架构）
        for batch in train_data:
            loss = compute_loss(supernet, batch)
            weight_optimizer.zero_grad()
            loss.backward()
            weight_optimizer.step()

        # 步骤2：更新架构参数（固定权重）
        for batch in val_data:
            loss = compute_loss(supernet, batch)
            arch_optimizer.zero_grad()
            loss.backward()
            arch_optimizer.step()

        # 步骤3：温度退火
        temperature *= 0.95

    # 导出最优架构
    best_arch = derive_architecture(supernet.arch_params)
    return best_arch

class PerformancePredictor:
    def __init__(self):
        self.history = []

    def predict(self, arch_encoding, partial_epochs=5):
        # 训练少量epoch
        model = create_model(arch_encoding)
        for epoch in range(partial_epochs):
            train_epoch(model)

        early_performance = evaluate(model)

        # 使用高斯过程预测最终性能
        gp = GaussianProcess()
        gp.fit(self.history)
        predicted_final = gp.predict(
            [arch_encoding, early_performance]
        )

        return predicted_final

def pareto_search():
    population = []

    for _ in range(population_size):
        arch = sample_architecture()
        perf = evaluate_performance(arch)
        latency = measure_latency(arch)
        population.append((arch, perf, latency))

    # 计算Pareto前沿
    pareto_front = []
    for arch, perf, lat in population:
        dominated = False
        for other_arch, other_perf, other_lat in population:
            if other_perf > perf and other_lat < lat:
                dominated = True
                break

        if not dominated:
            pareto_front.append((arch, perf, lat))

    return pareto_front

class UnifiedRankingEvaluator:
    def __init__(self, weights=None):
        self.metrics = {
            'relevance': {
                'ndcg': NDCG(),
                'map': MAP(),
                'mrr': MRR()
            },
            'diversity': {
                'ilad': ILAD(),  # Intra-List Average Distance
                'coverage': CategoryCoverage(),
                'novelty': Novelty()
            },
            'fairness': {
                'exposure': ExposureFairness(),
                'demographic': DemographicParity(),
                'equal_opportunity': EqualOpportunity()
            },
            'user_satisfaction': {
                'ctr': CTR(),
                'dwell_time': DwellTime(),
                'return_rate': ReturnRate()
            },
            'business': {
                'revenue': Revenue(),
                'engagement': Engagement(),
                'retention': Retention()
            }
        }

        # 默认权重（可通过A/B测试调整）
        self.weights = weights or {
            'relevance': 0.4,
            'diversity': 0.2,
            'fairness': 0.15,
            'user_satisfaction': 0.15,
            'business': 0.1
        }

def normalize_scores(self, raw_scores):
    """将不同量纲的指标归一化到[0,1]"""
    normalized = {}

    for category, scores in raw_scores.items():
        normalized[category] = {}
        for metric, value in scores.items():
            # Min-Max归一化
            min_val = self.baselines[category][metric]['min']
            max_val = self.baselines[category][metric]['max']

            if max_val > min_val:
                norm_value = (value - min_val) / (max_val - min_val)
            else:
                norm_value = 0.5

            normalized[category][metric] = np.clip(norm_value, 0, 1)

    return normalized

def aggregate_scores(self, normalized_scores):
    """多层次聚合"""
    # 第一层：类别内聚合
    category_scores = {}
    for category, metrics in normalized_scores.items():
        # 使用几何平均避免某个指标过低
        category_scores[category] = gmean(list(metrics.values()))

    # 第二层：跨类别聚合
    final_score = sum(
        self.weights[cat] * score 
        for cat, score in category_scores.items()
    )

    return final_score, category_scores

class AdaptiveWeightOptimizer:
    def __init__(self, evaluator):
        self.evaluator = evaluator
        self.weight_history = []

    def optimize_weights(self, experiments):
        """基于历史A/B测试结果优化权重"""

        def objective(weights):
            # 确保权重和为1
            weights = weights / weights.sum()

            # 计算加权得分与业务KPI的相关性
            scores = []
            kpis = []

            for exp in experiments:
                score = self.evaluator.evaluate(
                    exp['ranking'], 
                    weights=weights
                )
                scores.append(score)
                kpis.append(exp['business_kpi'])

            # 最大化与KPI的相关性
            correlation = np.corrcoef(scores, kpis)[0, 1]
            return -correlation

        # 使用贝叶斯优化
        from skopt import gp_minimize

        result = gp_minimize(
            objective,
            dimensions=[(0.1, 0.6)] * 5,  # 5个类别的权重范围
            n_calls=50
        )

        optimal_weights = result.x / sum(result.x)
        return dict(zip(self.evaluator.weights.keys(), optimal_weights))

def generate_evaluation_report(self, ranking_result):
    """生成详细的评估报告"""

    report = {
        'overall_score': None,
        'category_scores': {},
        'detailed_metrics': {},
        'warnings': [],
        'recommendations': []
    }

    # 计算各维度得分
    raw_scores = self.compute_all_metrics(ranking_result)
    normalized = self.normalize_scores(raw_scores)
    overall, category = self.aggregate_scores(normalized)

    report['overall_score'] = overall
    report['category_scores'] = category
    report['detailed_metrics'] = normalized

    # 识别问题和机会
    for cat, score in category.items():
        if score < 0.3:
            report['warnings'].append(
                f"{cat}得分过低({score:.2f})，需要重点优化"
            )

        # 具体建议
        if cat == 'diversity' and score < 0.5:
            report['recommendations'].append(
                "建议：增加MMR或DPP后处理提升多样性"
            )
        elif cat == 'fairness' and score < 0.5:
            report['recommendations'].append(
                "建议：引入公平性约束或重新采样训练数据"
            )

    # 可视化
    report['visualization'] = self.create_radar_chart(category)

    return report

class RankingMonitor:
    def __init__(self, evaluator, thresholds):
        self.evaluator = evaluator
        self.thresholds = thresholds
        self.baseline = None

    def monitor(self, live_ranking):
        """实时监控排序质量"""
        current_scores = self.evaluator.evaluate(live_ranking)

        alerts = []
        for metric, score in current_scores.items():
            # 检查绝对阈值
            if score < self.thresholds[metric]['min']:
                alerts.append({
                    'level': 'critical',
                    'metric': metric,
                    'message': f'{metric}低于最低阈值'
                })

            # 检查相对变化
            if self.baseline:
                change = (score - self.baseline[metric]) / self.baseline[metric]
                if abs(change) > 0.1:  # 10%变化
                    alerts.append({
                        'level': 'warning',
                        'metric': metric,
                        'message': f'{metric}变化{change:.1%}'
                    })

        return alerts