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 tangwang
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"""
示例：从Redis查询推荐结果
演示如何使用生成的索引进行在线推荐
"""
import redis
import argparse
from offline_tasks.config.offline_config import REDIS_CONFIG
def query_i2i_similar_items(redis_client, item_id, algorithm='swing', top_n=10):
"""
查询物品的相似物品
Args:
redis_client: Redis客户端
item_id: 物品ID
algorithm: 算法类型 (swing/session_w2v/deepwalk)
top_n: 返回前N个结果
Returns:
List of (item_id, score)
"""
key = f"i2i:{algorithm}:{item_id}"
value = redis_client.get(key)
if not value:
return []
# 解析结果
results = []
items = value.split(',')
for item in items[:top_n]:
parts = item.split(':')
if len(parts) == 2:
results.append((parts[0], float(parts[1])))
return results
def query_interest_items(redis_client, dimension_key, list_type='hot', top_n=20):
"""
查询兴趣点聚合的推荐物品
Args:
redis_client: Redis客户端
dimension_key: 维度key (如 platform:PC, country:US)
list_type: 列表类型 (hot/cart/new/global)
top_n: 返回前N个结果
Returns:
List of (item_id, score)
"""
key = f"interest:{list_type}:{dimension_key}"
value = redis_client.get(key)
if not value:
return []
# 解析结果
results = []
items = value.split(',')
for item in items[:top_n]:
parts = item.split(':')
if len(parts) == 2:
results.append((parts[0], float(parts[1])))
return results
def main():
parser = argparse.ArgumentParser(description='Query recommendation results from Redis')
parser.add_argument('--redis-host', type=str, default=REDIS_CONFIG.get('host', 'localhost'),
help='Redis host')
parser.add_argument('--redis-port', type=int, default=REDIS_CONFIG.get('port', 6379),
help='Redis port')
parser.add_argument('--redis-db', type=int, default=REDIS_CONFIG.get('db', 0),
help='Redis database')
args = parser.parse_args()
# 创建Redis连接
print("Connecting to Redis...")
redis_client = redis.Redis(
host=args.redis_host,
port=args.redis_port,
db=args.redis_db,
decode_responses=True
)
try:
redis_client.ping()
print("✓ Redis connected\n")
except Exception as e:
print(f"✗ Failed to connect to Redis: {e}")
return 1
# 示例1: 查询i2i相似物品
print("="*80)
print("示例1: 查询物品的相似物品（i2i）")
print("="*80)
test_item_id = "123456" # 替换为实际的物品ID
for algorithm in ['swing', 'session_w2v', 'deepwalk']:
print(f"\n算法: {algorithm}")
results = query_i2i_similar_items(redis_client, test_item_id, algorithm, top_n=5)
if results:
print(f"物品 {test_item_id} 的相似物品:")
for idx, (item_id, score) in enumerate(results, 1):
print(f" {idx}. 物品ID: {item_id}, 相似度: {score:.4f}")
else:
print(f" 未找到物品 {test_item_id} 的相似物品")
# 示例2: 查询兴趣点推荐
print("\n" + "="*80)
print("示例2: 查询兴趣点聚合推荐")
print("="*80)
# 测试不同维度
test_cases = [
('platform', 'PC', 'hot'),
('country', 'US', 'hot'),
('customer_type', 'retailer', 'cart'),
('category_level2', '100', 'new'),
]
for dimension, value, list_type in test_cases:
dimension_key = f"{dimension}:{value}"
print(f"\n维度: {dimension_key}, 类型: {list_type}")
results = query_interest_items(redis_client, dimension_key, list_type, top_n=5)
if results:
print(f"推荐物品:")
for idx, (item_id, score) in enumerate(results, 1):
print(f" {idx}. 物品ID: {item_id}, 分数: {score:.4f}")
else:
print(f" 未找到推荐结果")
# 示例3: 组合查询（实际推荐场景）
print("\n" + "="*80)
print("示例3: 组合推荐场景")
print("="*80)
print("\n场景: 用户在PC端，来自美国，是零售商，浏览了物品123456")
print("\n1. 基于物品的相似推荐（i2i）:")
results = query_i2i_similar_items(redis_client, test_item_id, 'swing', top_n=3)
for idx, (item_id, score) in enumerate(results, 1):
print(f" {idx}. 物品ID: {item_id}, 相似度: {score:.4f}")
print("\n2. 基于平台+国家的热门推荐:")
results = query_interest_items(redis_client, 'platform_country:PC_US', 'hot', top_n=3)
for idx, (item_id, score) in enumerate(results, 1):
print(f" {idx}. 物品ID: {item_id}, 分数: {score:.4f}")
print("\n3. 基于客户类型的加购推荐:")
results = query_interest_items(redis_client, 'customer_type:retailer', 'cart', top_n=3)
for idx, (item_id, score) in enumerate(results, 1):
print(f" {idx}. 物品ID: {item_id}, 分数: {score:.4f}")
print("\n" + "="*80)
print("✓ 查询示例完成")
print("="*80)
return 0
if __name__ == '__main__':
main()
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