Fix : 수정

recommend/src/main/java/com/ktds/hi/recommend/biz/domain/Location.java

@@ -1,40 +1,16 @@
 package com.ktds.hi.recommend.biz.domain;
 
-import lombok.AllArgsConstructor;
 import lombok.Builder;
 import lombok.Getter;
-import lombok.NoArgsConstructor;
 
-/**
- * 위치 도메인 클래스
- * 위치 정보를 담는 도메인 객체
- */
 @Getter
 @Builder
-@NoArgsConstructor
-@AllArgsConstructor
 public class Location {
-    
+    private Long id;           // 추가
-    private Long id;
-    private String address;
     private Double latitude;
     private Double longitude;
-    private String city;
+    private String address;
-    private String district;
+    private String city;       // 추가
-    private String country;
+    private String district;   // 추가
-    
+    private String country;    // 추가
-    /**
-     * 좌표 업데이트
-     */
-    public Location updateCoordinates(Double newLatitude, Double newLongitude) {
-        return Location.builder()
-                .id(this.id)
-                .address(this.address)
-                .latitude(newLatitude)
-                .longitude(newLongitude)
-                .city(this.city)
-                .district(this.district)
-                .country(this.country)
-                .build();
-    }
 }

recommend/src/main/java/com/ktds/hi/recommend/biz/domain/RecommendHistory.java

@@ -4,17 +4,24 @@ import lombok.Builder;
 import lombok.Getter;
 
 import java.time.LocalDateTime;
+import java.util.List;
 
 @Getter
 @Builder
 public class RecommendHistory {
     private Long id;
     private Long memberId;
-    private Long storeId;
+    private Long storeId;               // 기존 필드
-    private String recommendType;
+    private String recommendType;       // 기존 필드 (String)
     private Double score;
     private String reason;
     private LocalDateTime recommendedAt;
     private Boolean clicked;
     private Boolean visited;
+
+    // 추가 필드들 (Adapter에서 사용)
+    private List<Long> recommendedStoreIds;
+    private RecommendType recommendTypeEnum;
+    private String criteria;
+    private LocalDateTime createdAt;
 }

recommend/src/main/java/com/ktds/hi/recommend/biz/domain/RecommendStore.java

@@ -21,4 +21,7 @@ public class RecommendStore {
     private Double recommendScore;
     private Double distance;
     private String priceRange;
+
+    // 추가 필드
+    private RecommendType recommendType;
 }

recommend/src/main/java/com/ktds/hi/recommend/biz/domain/RecommendType.java

@@ -1,16 +1,11 @@
 package com.ktds.hi.recommend.biz.domain;
 
-/**
- * 추천 유형 열거형
- * 추천의 종류를 정의
- */
 public enum RecommendType {
-    TASTE_BASED("취향 기반"),
+    AI_RECOMMENDATION("AI 추천"),
     LOCATION_BASED("위치 기반"),
     POPULARITY_BASED("인기 기반"),
     COLLABORATIVE_FILTERING("협업 필터링"),
-    AI_RECOMMENDATION("AI 추천"),
+    PREFERENCE_BASED("취향 기반");
-    SIMILAR_USER("유사 사용자 기반");
 
     private final String description;
 

recommend/src/main/java/com/ktds/hi/recommend/biz/domain/TasteCategory.java

@@ -1,22 +1,16 @@
 package com.ktds.hi.recommend.biz.domain;
 
-/**
- * 취향 카테고리 열거형
- * 음식 카테고리를 정의
- */
 public enum TasteCategory {
     KOREAN("한식"),
     CHINESE("중식"),
     JAPANESE("일식"),
     WESTERN("양식"),
-    FAST_FOOD("패스트푸드"),
     CAFE("카페"),
+    FASTFOOD("패스트푸드"),
     DESSERT("디저트"),
     CHICKEN("치킨"),
     PIZZA("피자"),
-    ASIAN("아시안"),
+    OTHER("기타");
-    VEGETARIAN("채식"),
-    SEAFOOD("해산물");
 
     private final String description;
 

recommend/src/main/java/com/ktds/hi/recommend/biz/domain/TasteProfile.java

@@ -5,16 +5,26 @@ import lombok.Getter;
 
 import java.time.LocalDateTime;
 import java.util.List;
+import java.util.Map;
 
 @Getter
 @Builder
 public class TasteProfile {
     private Long id;
     private Long memberId;
-    private List<String> cuisinePreferences;
+    private List<String> cuisinePreferences;  // 기존 유지
-    private String priceRange;
+    private String priceRange;               // 기존 유지
-    private Integer distancePreference;
+    private Integer distancePreference;      // 기존 유지
-    private List<String> tasteTags;
+    private List<String> tasteTags;         // 기존 유지
+
+    // 추가 필드들 (Adapter에서 사용)
+    private List<TasteCategory> preferredCategories;
+    private Map<String, Double> categoryScores;
+    private List<String> preferredTags;
+    private Map<String, Object> behaviorPatterns;
+    private Double pricePreference;
+    private Double distancePreferenceDouble; // distancePreference와 구분
+
     private LocalDateTime createdAt;
     private LocalDateTime updatedAt;
 }

recommend/src/main/java/com/ktds/hi/recommend/infra/gateway/AiRecommendAdapter.java

@@ -3,17 +3,15 @@ package com.ktds.hi.recommend.infra.gateway;
 import com.ktds.hi.recommend.biz.usecase.out.AiRecommendRepository;
 import com.ktds.hi.recommend.biz.domain.RecommendStore;
 import com.ktds.hi.recommend.biz.domain.RecommendType;
+import com.ktds.hi.recommend.biz.domain.TasteProfile;
 import lombok.RequiredArgsConstructor;
 import lombok.extern.slf4j.Slf4j;
 import org.springframework.stereotype.Component;
 
+import java.util.Arrays;
 import java.util.List;
 import java.util.Map;
 
-/**
- * AI 추천 어댑터 클래스
- * AI 기반 추천 기능을 구현 (현재는 Mock 구현)
- */
 @Component
 @RequiredArgsConstructor
 @Slf4j
@@ -21,36 +19,21 @@ public class AiRecommendAdapter implements AiRecommendRepository {
 
     @Override
     public List<RecommendStore> recommendStoresByAI(Long memberId, Map<String, Object> preferences) {
-        log.info("AI 기반 매장 추천 요청: memberId={}, preferences={}", memberId, preferences);
+        log.info("AI 기반 매장 추천 요청: memberId={}", memberId);
 
-        // Mock 구현 - 실제로는 AI 모델 API 호출
+        return Arrays.asList(
-        return List.of(
             RecommendStore.builder()
                 .storeId(1L)
                 .storeName("AI 추천 매장 1")
                 .address("서울시 강남구 역삼동")
                 .category("한식")
-                        .tags(List.of("맛집", "깔끔", "한식"))
+                .tags(Arrays.asList("맛집", "깔끔", "한식"))
                 .rating(4.5)
                 .reviewCount(150)
                 .distance(500.0)
                 .recommendScore(92.0)
                 .recommendType(RecommendType.AI_RECOMMENDATION)
                 .recommendReason("사용자 취향과 92% 일치")
-                        .build(),
-                
-                RecommendStore.builder()
-                        .storeId(2L)
-                        .storeName("AI 추천 매장 2")
-                        .address("서울시 강남구 논현동")
-                        .category("일식")
-                        .tags(List.of("초밥", "신선", "일식"))
-                        .rating(4.3)
-                        .reviewCount(89)
-                        .distance(800.0)
-                        .recommendScore(87.0)
-                        .recommendType(RecommendType.AI_RECOMMENDATION)
-                        .recommendReason("사용자가 선호하는 일식 카테고리")
                 .build()
         );
     }
@@ -59,43 +42,47 @@ public class AiRecommendAdapter implements AiRecommendRepository {
     public List<RecommendStore> recommendStoresBySimilarUsers(Long memberId) {
         log.info("유사 사용자 기반 추천 요청: memberId={}", memberId);
 
-        // Mock 구현
+        return Arrays.asList(
-        return List.of(
             RecommendStore.builder()
                 .storeId(3L)
                 .storeName("유사 취향 추천 매장")
                 .address("서울시 서초구 서초동")
                 .category("양식")
-                        .tags(List.of("파스타", "분위기", "양식"))
+                .tags(Arrays.asList("파스타", "분위기", "데이트"))
-                        .rating(4.4)
+                .rating(4.2)
-                        .reviewCount(203)
+                .reviewCount(78)
                 .distance(1200.0)
                 .recommendScore(85.0)
-                        .recommendType(RecommendType.SIMILAR_USER)
+                .recommendType(RecommendType.COLLABORATIVE_FILTERING)
-                        .recommendReason("비슷한 취향의 사용자들이 좋아하는 매장")
+                .recommendReason("비슷한 취향의 사용자들이 선호")
                 .build()
         );
     }
 
     @Override
     public List<RecommendStore> recommendStoresByCollaborativeFiltering(Long memberId) {
-        log.info("협업 필터링 추천 요청: memberId={}", memberId);
+        return recommendStoresBySimilarUsers(memberId);
+    }
 
-        // Mock 구현
+    @Override
-        return List.of(
+    public List<RecommendStore> filterByPreferences(List<RecommendStore> stores, TasteProfile tasteProfile, String tags) {
-                RecommendStore.builder()
+        log.info("취향 기반 매장 필터링: 매장 수={}, 태그={}", stores.size(), tags);
-                        .storeId(4L)
+
-                        .storeName("협업 필터링 추천 매장")
+        // 간단한 필터링 로직 구현
-                        .address("서울시 마포구 홍대입구")
+        return stores.stream()
-                        .category("카페")
+            .filter(store -> {
-                        .tags(List.of("커피", "디저트", "분위기"))
+                if (tasteProfile.getPreferredTags() != null) {
-                        .rating(4.2)
+                    return store.getTags().stream()
-                        .reviewCount(127)
+                        .anyMatch(tag -> tasteProfile.getPreferredTags().contains(tag));
-                        .distance(2500.0)
+                }
-                        .recommendScore(82.0)
+                return true;
-                        .recommendType(RecommendType.COLLABORATIVE_FILTERING)
+            })
-                        .recommendReason("사용자 행동 패턴 기반 추천")
+            .toList();
-                        .build()
+    }
-        );
+
+    @Override
+    public String getStoreSummary(Long storeId) {
+        log.info("매장 AI 요약 조회: storeId={}", storeId);
+        return "AI가 분석한 매장 요약: 고객들이 맛과 서비스를 높이 평가하는 매장입니다.";
     }
 }

recommend/src/main/java/com/ktds/hi/recommend/infra/gateway/LocationServiceAdapter.java

@@ -8,12 +8,9 @@ import lombok.RequiredArgsConstructor;
 import lombok.extern.slf4j.Slf4j;
 import org.springframework.stereotype.Component;
 
+import java.util.Arrays;
 import java.util.List;
 
-/**
- * 위치 서비스 어댑터 클래스
- * 위치 기반 서비스 기능을 구현 (현재는 Mock 구현)
- */
 @Component
 @RequiredArgsConstructor
 @Slf4j
@@ -23,7 +20,6 @@ public class LocationServiceAdapter implements LocationRepository {
     public Location saveLocation(Location location) {
         log.info("위치 정보 저장: {}", location.getAddress());
 
-        // Mock 구현
         return Location.builder()
             .id(1L)
             .address(location.getAddress())
@@ -36,17 +32,16 @@ public class LocationServiceAdapter implements LocationRepository {
     }
 
     @Override
-    public List<RecommendStore> findStoresWithinRadius(Double latitude, Double longitude, Integer radius) {
+    public List<RecommendStore> findStoresWithinDistance(Double latitude, Double longitude, Integer radius) {
         log.info("반경 내 매장 조회: lat={}, lon={}, radius={}", latitude, longitude, radius);
 
-        // Mock 구현
+        return Arrays.asList(
-        return List.of(
             RecommendStore.builder()
                 .storeId(5L)
                 .storeName("근처 매장 1")
                 .address("서울시 강남구 역삼동 123-45")
                 .category("한식")
-                        .tags(List.of("근처", "맛집"))
+                .tags(Arrays.asList("근처", "맛집"))
                 .rating(4.1)
                 .reviewCount(95)
                 .distance(300.0)
@@ -60,7 +55,7 @@ public class LocationServiceAdapter implements LocationRepository {
                 .storeName("근처 매장 2")
                 .address("서울시 강남구 역삼동 678-90")
                 .category("카페")
-                        .tags(List.of("커피", "디저트"))
+                .tags(Arrays.asList("커피", "디저트"))
                 .rating(4.0)
                 .reviewCount(67)
                 .distance(450.0)
@@ -73,27 +68,24 @@ public class LocationServiceAdapter implements LocationRepository {
 
     @Override
     public Double calculateDistance(Double lat1, Double lon1, Double lat2, Double lon2) {
-        // Haversine 공식을 사용한 거리 계산
+        // 하버사인 공식을 이용한 거리 계산
-        final int R = 6371; // 지구의 반지름 (km)
+        final int R = 6371; // 지구 반지름 (km)
 
         double latDistance = Math.toRadians(lat2 - lat1);
         double lonDistance = Math.toRadians(lon2 - lon1);
-        
         double a = Math.sin(latDistance / 2) * Math.sin(latDistance / 2)
             + Math.cos(Math.toRadians(lat1)) * Math.cos(Math.toRadians(lat2))
             * Math.sin(lonDistance / 2) * Math.sin(lonDistance / 2);
-        
         double c = 2 * Math.atan2(Math.sqrt(a), Math.sqrt(1 - a));
-        double distance = R * c * 1000; // 미터로 변환
 
-        return distance;
+        return R * c * 1000; // 미터 단위로 반환
     }
 
     @Override
     public Location geocodeAddress(String address) {
-        log.info("주소 좌표 변환 요청: {}", address);
+        log.info("주소 좌표 변환: {}", address);
 
-        // Mock 구현 - 실제로는 Google Maps API 등 사용
+        // Mock 구현 - 실제로는 지도 API 호출
         return Location.builder()
             .address(address)
             .latitude(37.5665)

recommend/src/main/java/com/ktds/hi/recommend/infra/gateway/RecommendRepositoryAdapter.java

@@ -1,35 +1,44 @@
 package com.ktds.hi.recommend.infra.gateway;
 
+import com.fasterxml.jackson.core.type.TypeReference;
+import com.fasterxml.jackson.databind.ObjectMapper;
 import com.ktds.hi.recommend.biz.usecase.out.RecommendRepository;
 import com.ktds.hi.recommend.biz.domain.RecommendHistory;
+import com.ktds.hi.recommend.biz.domain.RecommendStore;
 import com.ktds.hi.recommend.biz.domain.TasteProfile;
+import com.ktds.hi.recommend.biz.domain.RecommendType;
 import com.ktds.hi.recommend.infra.gateway.repository.RecommendHistoryJpaRepository;
 import com.ktds.hi.recommend.infra.gateway.repository.TasteProfileJpaRepository;
 import com.ktds.hi.recommend.infra.gateway.entity.RecommendHistoryEntity;
 import com.ktds.hi.recommend.infra.gateway.entity.TasteProfileEntity;
 import lombok.RequiredArgsConstructor;
+import lombok.extern.slf4j.Slf4j;
 import org.springframework.stereotype.Component;
 
+import java.time.LocalDateTime;
+import java.util.Arrays;
 import java.util.List;
 import java.util.Optional;
-import java.util.stream.Collectors;
 
-/**
- * 추천 리포지토리 어댑터 클래스
- * 도메인 리포지토리 인터페이스를 JPA 리포지토리에 연결
- */
 @Component
 @RequiredArgsConstructor
+@Slf4j
 public class RecommendRepositoryAdapter implements RecommendRepository {
 
     private final RecommendHistoryJpaRepository recommendHistoryJpaRepository;
     private final TasteProfileJpaRepository tasteProfileJpaRepository;
+    private final ObjectMapper objectMapper = new ObjectMapper();
 
     @Override
     public RecommendHistory saveRecommendHistory(RecommendHistory history) {
+        try {
             RecommendHistoryEntity entity = toRecommendHistoryEntity(history);
             RecommendHistoryEntity savedEntity = recommendHistoryJpaRepository.save(entity);
             return toRecommendHistory(savedEntity);
+        } catch (Exception e) {
+            log.error("추천 히스토리 저장 실패", e);
+            throw new RuntimeException("추천 히스토리 저장 실패", e);
+        }
     }
 
     @Override
@@ -37,14 +46,19 @@ public class RecommendRepositoryAdapter implements RecommendRepository {
         List<RecommendHistoryEntity> entities = recommendHistoryJpaRepository.findByMemberIdOrderByCreatedAtDesc(memberId);
         return entities.stream()
             .map(this::toRecommendHistory)
-                .collect(Collectors.toList());
+            .toList();
     }
 
     @Override
     public TasteProfile saveTasteProfile(TasteProfile profile) {
+        try {
             TasteProfileEntity entity = toTasteProfileEntity(profile);
             TasteProfileEntity savedEntity = tasteProfileJpaRepository.save(entity);
             return toTasteProfile(savedEntity);
+        } catch (Exception e) {
+            log.error("취향 프로필 저장 실패", e);
+            throw new RuntimeException("취향 프로필 저장 실패", e);
+        }
     }
 
     @Override
@@ -53,15 +67,64 @@ public class RecommendRepositoryAdapter implements RecommendRepository {
             .map(this::toTasteProfile);
     }
 
-    /**
+    @Override
-     * 엔티티를 도메인으로 변환
+    public Optional<RecommendStore> findStoreById(Long storeId) {
-     */
+        // Mock 구현 - 실제로는 Store 서비스 호출 또는 캐시에서 조회
+        return Optional.of(RecommendStore.builder()
+            .storeId(storeId)
+            .storeName("매장 " + storeId)
+            .address("서울시 강남구")
+            .category("한식")
+            .rating(4.5)
+            .tags(Arrays.asList("맛집", "친절"))
+            .distance(500.0)
+            .priceRange("중간")
+            .build());
+    }
+
+    @Override
+    public List<RecommendStore> findPopularStores(String category, Integer limit) {
+        // Mock 구현
+        return Arrays.asList(
+            RecommendStore.builder()
+                .storeId(1L)
+                .storeName("인기 매장 1")
+                .category(category != null ? category : "한식")
+                .rating(4.8)
+                .tags(Arrays.asList("인기", "맛집"))
+                .build()
+        );
+    }
+
+    @Override
+    public void logStoreClick(Long memberId, Long storeId) {
+        log.info("매장 클릭 로그: memberId={}, storeId={}", memberId, storeId);
+        // 비동기 로깅 구현
+    }
+
+    @Override
+    public void logRecommendation(Long memberId, List<Long> storeIds, String context) {
+        try {
+            RecommendHistory history = RecommendHistory.builder()
+                .memberId(memberId)
+                .recommendedStoreIds(storeIds)
+                .criteria(context)
+                .recommendTypeEnum(RecommendType.PREFERENCE_BASED)
+                .createdAt(LocalDateTime.now())
+                .build();
+            saveRecommendHistory(history);
+        } catch (Exception e) {
+            log.error("추천 로그 저장 실패", e);
+        }
+    }
+
+    // 변환 메서드들
     private RecommendHistory toRecommendHistory(RecommendHistoryEntity entity) {
         return RecommendHistory.builder()
             .id(entity.getId())
             .memberId(entity.getMemberId())
             .recommendedStoreIds(entity.getRecommendedStoreIdsList())
-                .recommendType(entity.getRecommendType())
+            .recommendTypeEnum(entity.getRecommendType())
             .criteria(entity.getCriteria())
             .createdAt(entity.getCreatedAt())
             .build();
@@ -76,35 +139,40 @@ public class RecommendRepositoryAdapter implements RecommendRepository {
             .preferredTags(entity.getPreferredTagsList())
             .behaviorPatterns(entity.getBehaviorPatternsMap())
             .pricePreference(entity.getPricePreference())
-                .distancePreference(entity.getDistancePreference())
+            .distancePreferenceDouble(entity.getDistancePreference())
             .createdAt(entity.getCreatedAt())
             .updatedAt(entity.getUpdatedAt())
             .build();
     }
 
-    /**
-     * 도메인을 엔티티로 변환
-     */
     private RecommendHistoryEntity toRecommendHistoryEntity(RecommendHistory domain) {
+        try {
             return RecommendHistoryEntity.builder()
                 .id(domain.getId())
                 .memberId(domain.getMemberId())
-                .recommendedStoreIdsJson(domain.getRecommendedStoreIds().toString()) // JSON 변환 필요
+                .recommendedStoreIdsJson(objectMapper.writeValueAsString(domain.getRecommendedStoreIds()))
-                .recommendType(domain.getRecommendType())
+                .recommendType(domain.getRecommendTypeEnum())
                 .criteria(domain.getCriteria())
                 .build();
+        } catch (Exception e) {
+            throw new RuntimeException("Entity 변환 실패", e);
+        }
     }
 
     private TasteProfileEntity toTasteProfileEntity(TasteProfile domain) {
+        try {
             return TasteProfileEntity.builder()
                 .id(domain.getId())
                 .memberId(domain.getMemberId())
-                .preferredCategoriesJson(domain.getPreferredCategories().toString()) // JSON 변환 필요
+                .preferredCategoriesJson(objectMapper.writeValueAsString(domain.getPreferredCategories()))
-                .categoryScoresJson(domain.getCategoryScores().toString()) // JSON 변환 필요
+                .categoryScoresJson(objectMapper.writeValueAsString(domain.getCategoryScores()))
-                .preferredTagsJson(domain.getPreferredTags().toString()) // JSON 변환 필요
+                .preferredTagsJson(objectMapper.writeValueAsString(domain.getPreferredTags()))
-                .behaviorPatternsJson(domain.getBehaviorPatterns().toString()) // JSON 변환 필요
+                .behaviorPatternsJson(objectMapper.writeValueAsString(domain.getBehaviorPatterns()))
                 .pricePreference(domain.getPricePreference())
-                .distancePreference(domain.getDistancePreference())
+                .distancePreference(domain.getDistancePreferenceDouble())
                 .build();
+        } catch (Exception e) {
+            throw new RuntimeException("Entity 변환 실패", e);
+        }
     }
 }

recommend/src/main/java/com/ktds/hi/recommend/infra/gateway/UserPreferenceAdapter.java

@@ -3,26 +3,21 @@ package com.ktds.hi.recommend.infra.gateway;
 import com.ktds.hi.recommend.biz.usecase.out.UserPreferenceRepository;
 import com.ktds.hi.recommend.biz.domain.TasteProfile;
 import com.ktds.hi.recommend.biz.domain.TasteCategory;
-import com.ktds.hi.recommend.infra.gateway.repository.TasteProfileJpaRepository;
 import lombok.RequiredArgsConstructor;
 import lombok.extern.slf4j.Slf4j;
 import org.springframework.stereotype.Component;
 
 import java.time.LocalDateTime;
+import java.util.Arrays;
 import java.util.List;
 import java.util.Map;
 import java.util.Optional;
 
-/**
- * 사용자 선호도 어댑터 클래스
- * 사용자 취향 데이터 처리 기능을 구현
- */
 @Component
 @RequiredArgsConstructor
 @Slf4j
 public class UserPreferenceAdapter implements UserPreferenceRepository {
 
-    private final TasteProfileJpaRepository tasteProfileJpaRepository;
     private final RecommendRepositoryAdapter recommendRepositoryAdapter;
 
     @Override
@@ -34,16 +29,15 @@ public class UserPreferenceAdapter implements UserPreferenceRepository {
     public Map<String, Object> analyzePreferencesFromReviews(Long memberId) {
         log.info("리뷰 기반 취향 분석 시작: memberId={}", memberId);
 
-        // Mock 구현 - 실제로는 리뷰 서비스 API 호출하여 분석
         return Map.of(
-                "preferredCategories", List.of(TasteCategory.KOREAN, TasteCategory.JAPANESE),
+            "preferredCategories", Arrays.asList(TasteCategory.KOREAN, TasteCategory.JAPANESE),
             "categoryScores", Map.of(
                 "한식", 85.0,
                 "일식", 78.0,
                 "양식", 65.0
             ),
-                "preferredTags", List.of("맛집", "깔끔", "친절"),
+            "preferredTags", Arrays.asList("맛집", "깔끔", "친절"),
-                "pricePreference", 60.0, // 0-100 점수
+            "pricePreference", 60.0,
             "distancePreference", 70.0,
             "behaviorPatterns", Map.of(
                 "weekendDining", true,
@@ -56,16 +50,13 @@ public class UserPreferenceAdapter implements UserPreferenceRepository {
     @Override
     public List<Long> findSimilarTasteMembers(Long memberId) {
         log.info("유사 취향 사용자 조회: memberId={}", memberId);
-        
+        return Arrays.asList(123L, 456L, 789L);
-        // Mock 구현 - 실제로는 ML 모델 또는 유사도 계산 알고리즘 사용
-        return List.of(123L, 456L, 789L);
     }
 
     @Override
     public TasteProfile updateTasteProfile(Long memberId, Map<String, Object> analysisData) {
         log.info("취향 프로필 업데이트: memberId={}", memberId);
 
-        // 기존 프로필 조회 또는 새로 생성
         Optional<TasteProfile> existingProfile = getMemberPreferences(memberId);
 
         TasteProfile.TasteProfileBuilder builder = TasteProfile.builder()
@@ -75,7 +66,7 @@ public class UserPreferenceAdapter implements UserPreferenceRepository {
             .preferredTags((List<String>) analysisData.get("preferredTags"))
             .behaviorPatterns((Map<String, Object>) analysisData.get("behaviorPatterns"))
             .pricePreference((Double) analysisData.get("pricePreference"))
-                .distancePreference((Double) analysisData.get("distancePreference"))
+            .distancePreferenceDouble((Double) analysisData.get("distancePreference"))
             .updatedAt(LocalDateTime.now());
 
         if (existingProfile.isPresent()) {