updated: ai-recommendation-service

Author: Lakeserl

ai-recommendation-service/src/test/java/com/lakeserl/ai_recommendation_service/service/RecommendationServiceImplTest.java

@@ -0,0 +1,57 @@
+package com.lakeserl.ai_recommendation_service.service;
+
+import com.lakeserl.ai_recommendation_service.client.ProductServiceClient;
+import com.lakeserl.ai_recommendation_service.client.UserServiceClient;
+import org.junit.jupiter.api.BeforeEach;
+import org.junit.jupiter.api.Test;
+import org.springframework.data.redis.core.RedisTemplate;
+import org.springframework.test.util.ReflectionTestUtils;
+
+import java.util.Set;
+
+import static org.assertj.core.api.Assertions.assertThat;
+import static org.assertj.core.api.Assertions.within;
+import static org.mockito.Mockito.mock;
+
+/**
+ * Content-based "similar" ranking relies on Jaccard similarity over product attribute sets.
+ * The ranking is only meaningful if identical sets score 1.0, disjoint sets 0.0, and partial
+ * overlap exactly intersection/union — otherwise "similar" products are mis-ordered.
+ */
+class RecommendationServiceImplTest {
+
+    private RecommendationServiceImpl service;
+
+    @BeforeEach
+    @SuppressWarnings("unchecked")
+    void setUp() {
+        service = new RecommendationServiceImpl(
+                mock(ProductServiceClient.class), mock(UserServiceClient.class), mock(RedisTemplate.class));
+    }
+
+    private double jaccard(Set<String> a, Set<String> b) {
+        Double v = ReflectionTestUtils.invokeMethod(service, "jaccardSimilarity", a, b);
+        return v == null ? Double.NaN : v;
+    }
+
+    @Test
+    void identicalAttributeSetsScoreOne() {
+        assertThat(jaccard(Set.of("OILY", "ACNE"), Set.of("OILY", "ACNE"))).isEqualTo(1.0);
+    }
+
+    @Test
+    void disjointAttributeSetsScoreZero() {
+        assertThat(jaccard(Set.of("OILY"), Set.of("DRY"))).isEqualTo(0.0);
+    }
+
+    @Test
+    void partialOverlapScoresIntersectionOverUnion() {
+        // {A,B,C} vs {B,C,D}: intersection {B,C}=2, union {A,B,C,D}=4 -> 0.5
+        assertThat(jaccard(Set.of("A", "B", "C"), Set.of("B", "C", "D"))).isCloseTo(0.5, within(1e-9));
+    }
+
+    @Test
+    void twoEmptySetsScoreOne() {
+        assertThat(jaccard(Set.of(), Set.of())).isEqualTo(1.0);
+    }
+}

ai-recommendation-service/target/classes/application.yaml

@@ -0,0 +1,73 @@
+server:
+  port: 8096
+
+spring:
+  application:
+    name: ai-recommendation-service
+  config:
+    import:
+      - optional:file:.env[.properties]
+      - optional:file:../.env[.properties]
+
+  data:
+    redis:
+      host: ${SPRING_DATA_REDIS_HOST:localhost}
+      port: 6379
+
+  kafka:
+    bootstrap-servers: ${SPRING_KAFKA_BOOTSTRAP_SERVERS:localhost:9092}
+    consumer:
+      group-id: recommendation-service-group
+      auto-offset-reset: earliest
+      key-deserializer: org.apache.kafka.common.serialization.StringDeserializer
+      value-deserializer: org.apache.kafka.common.serialization.StringDeserializer
+
+eureka:
+  client:
+    serviceUrl:
+      defaultZone: ${EUREKA_URI:http://localhost:8761/eureka}
+    register-with-eureka: true
+    fetch-registry: true
+  instance:
+    prefer-ip-address: true
+
+management:
+  tracing:
+    sampling:
+      probability: ${TRACING_SAMPLE_RATE:0.1}
+  zipkin:
+    tracing:
+      endpoint: http://${ZIPKIN_HOST:localhost}:9411/api/v2/spans
+  endpoints:
+    web:
+      exposure:
+        include: health,info,metrics,prometheus
+  endpoint:
+    health:
+      show-details: always
+      probes:
+        enabled: true
+  health:
+    livenessstate:
+      enabled: true
+    readinessstate:
+      enabled: true
+  metrics:
+    tags:
+      application: ${spring.application.name}
+      environment: ${ENVIRONMENT:dev}
+
+springdoc:
+  swagger-ui:
+    path: /docs
+
+app:
+  internal:
+    secret: ${INTERNAL_SECRET}
+
+logging:
+  pattern:
+    level: "%5p [${spring.application.name},%X{traceId:-},%X{spanId:-}]"
+  level:
+    root: INFO
+    com.lakeserl.ai_recommendation_service: INFO