圖形資料函式庫多重關係產品推薦引擎實作

CREATE QUERY recommend_by_features_and_context(VERTEX<Customer> source_customer, 
STRING weather, STRING time_of_day, INT top_k) FOR GRAPH Recommendation_Engine2_0 {
  // 初始化起始頂點
  start = {source_customer};
  
  // 計算啟用的上下文過濾器數量
  INT min_filters = 0;
  IF time_of_day != "" THEN min_filters = min_filters + 1; END;
  IF weather != "" THEN min_filters = min_filters + 1; END;
  
  // 查詢符合上下文條件的產品
  all_products = SELECT p FROM Product:p;
  filtered_context = SELECT c FROM Context:c;
  IF min_filters != 0 THEN
    filtered_context = SELECT c FROM Context:c 
                        WHERE c.id == weather OR c.id == time_of_day;
    candidate_products = SELECT p 
                         FROM filtered_context:c -(product_context:pc)- Product:p 
                         ACCUM p.@filters += 1 
                         HAVING p.@filters >= min_filters;
  ELSE
    candidate_products = all_products;
  END;
  
  // 計算客戶與產品之間的推薦分數
  recommended_products = SELECT p 
                         FROM start:s -(customer_feature:cf)- Feature:f -(product_feature:pf)- Product:p 
                         ACCUM p.@max_score += cf.affinity * pf.weight;
  
  // 傳回前k個推薦產品
  top_recommended = ORDER BY recommended_products.@max_score DESC LIMIT top_k;
  RETURN top_recommended;
}

// 原始的 MaxAccum 定義
MaxAccum @max_score;

// 可以修改為 SumAccum 或 AvgAccum
SumAccum @total_affinity;
AvgAccum @average_affinity;

recomm_products = SELECT p
FROM start:s -(customer_feature:cf)- Feature:f -(product_feature:pf)- candidate_products:p
ACCUM p.@max_score += pf.weight * cf.affinity // 計算分數
ORDER BY p.@max_score DESC
LIMIT top_k;

TYPEDEF TUPLE<VERTEX<product> v, DOUBLE avg_score> Product_Score_Tuple;
AvgAccum @product_avg_score;
HeapAccum<Product_Score_Tuple>(1, avg_score DESC) @@top_product_heap;
SetAccum<VERTEX<product>> @@top_product_vertex_set;

SetAccum<VERTEX<customer>> @@final_customer_vertex_set;
SetAccum<VERTEX<feature>> @@final_feature_vertex_set;
SetAccum<VERTEX<product>> @@final_product_vertex_set;
SetAccum<VERTEX<context>> @@final_context_vertex_set;
SetAccum<EDGE> @@final_context_feature_edge_set;
SetAccum<EDGE> @@final_product_feature_edge_set;
SetAccum<EDGE> @@final_product_context_edge_set;

// 搜尋路徑語法選項1：三個單跳路徑
SELECT p
FROM customer_vertex_set:s -(customer_feature:cf)- Feature:f -(product_feature:pf)- p,
     context_vertex_set:c -(context_feature:cf2)- f
WHERE cf.feature_id == cf2.feature_id

// 搜尋路徑語法選項2：一個多跳路徑
SELECT p
FROM customer_vertex_set:s -(customer_feature:cf)- Feature:f -(product_feature:pf)- candidate_products:p,
     context_vertex_set:c -(context_feature:cf2)- Feature:f
ACCUM p.@product_avg_score += pf.weight * cf.affinity

// 定義查詢：根據客戶和上下文推薦產品
CREATE QUERY recomm_by_customer_and_context (SET<VERTEX<Customer>> customers, SET<VERTEX<Context>> contexts) FOR GRAPH referral_network {
  // 初始化累加器
  AvgAccum<DOUBLE> @product_avg_score;
  HeapAccum<Order_Product_Tuple>(1, score DESC) @@order_product_heap;

  // 遍歷客戶、特徵、產品和上下文的路徑
  customer_vertex_set = SELECT c FROM customers:c;

  product_vertex_set = SELECT p
  FROM customer_vertex_set:c -(customer_feature:cf)- feature:f
  -(product_feature:pf)- product:p
  -(product_context:pctx)- context_vertex_set:ctx
  // 計算每個產品的平均評分
  ACCUM p.@product_avg_score += (cf.affinity * pf.weight)
  // 將產品及其評分加入堆積積排序
  POST-ACCUM @@order_product_heap += Order_Product_Tuple(p, p.@product_avg_score);

  // 取得最高評分的產品
  product_vertex_set = SELECT p
  FROM customer_vertex_set:c -(customer_feature:cf)- Feature:f
  -(product_feature:pf)- product_vertex_set:p
  -(product_context:pctx)- context_vertex_set:ctx
  // 蒐集需要顯示的頂點和邊
  ACCUM @@final_context_feature_edge_set += cf,
  @@final_product_feature_edge_set += pf,
  @@final_product_context_edge_set += pctx
  POST-ACCUM @@final_customer_vertex_set += c
  POST-ACCUM @@final_feature_vertex_set += f
  POST-ACCUM @@final_product_vertex_set += p
  POST-ACCUM @@final_context_vertex_set += ctx;
}

// 定義查詢：取得最高平均親和力的客戶群體
CREATE QUERY display_top_demographic() FOR GRAPH referral_network {
  // 定義資料型別和累加器
  TYPEDEF TUPLE<VERTEX<demographic> v, DOUBLE score> Top_Demographic_Tuple;
  AvgAccum<DOUBLE> @demographic_avg_score;
  HeapAccum<Demographic_Score_Tuple>(1, score DESC) @@top_product_heap;
  SetAccum<VERTEX<Demographic>> @@top_demographic_vertex_set;

  // 遍歷客戶群體、特徵和客戶的路徑
  demographic_vertex_set = SELECT d
  FROM Demographic:d -(demo_feature:df)- Feature:f
  -(customer_feature:cf)- Customer:c
  // 計算每個客戶群體的平均親和力
  ACCUM d.@demographic_avg_score += (cf.affinity * df.affinity)
  // 將客戶群體及其平均親和力加入堆積積排序
  POST-ACCUM @@top_product_heap += Demographic_Score_Tuple(d, d.@demographic_avg_score);

  // 將堆積積中的客戶群體轉換為頂點集合
  WHILE (@@top_product_heap.size() > 0) DO
    @@top_demographic_vertex_set += @@top_product_heap.pop().v;
  END;
  demographic_vertex_set = { @@top_demographic_vertex_set };

  // 蒐集需要顯示的頂點和邊
  demographic_vertex_set = SELECT d
  FROM demographic_vertex_set:d -(demo_customer:dc)- customer:c,
  demographic_vertex_set:d -(demo_feature:df)- feature:f,
  customer:c -(customer_feature:cf)- feature:f
  ACCUM @@final_demo_customer_edge_set += dc,
  @@final_demo_feature_edge_set += df,
  @@final_context_feature_edge_set += cf
  POST-ACCUM @@final_demographic_vertex_set += d
  POST-ACCUM @@final_customer_vertex_set += c
  POST-ACCUM @@final_feature_vertex_set += f;
}