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[REFACTOR] Redisearch로 매칭 알고리즘 리팩토링 #373

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Merged
Yoonchulchung merged 2 commits into from
Feb 11, 2026
Merged

[REFACTOR] Redisearch로 매칭 알고리즘 리팩토링 #373

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d5da136
refactor(#372): Redisearch로 매칭 알고리즘 변화
Yoonchulchung Feb 11, 2026
7fa72b1
fix: 체크스타일 반영
Yoonchulchung Feb 11, 2026
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22 changes: 22 additions & 0 deletions data/generators/redis_matching_generator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -371,9 +371,20 @@ def create_search_indexes(self):
NumericField('$.brandId', as_name='brandId'),
TagField('$.brandName', as_name='brandName'),
TagField('$.categories[*]', as_name='categories'),
# 이산형 태그
TagField('$.preferredFashionTags[*]', as_name='preferredFashionTags'),
TagField('$.preferredBeautyTags[*]', as_name='preferredBeautyTags'),
TagField('$.preferredContentTags[*]', as_name='preferredContentTags'),
# 연속형 태그
NumericField('$.minCreatorHeight', as_name='minCreatorHeight'),
NumericField('$.maxCreatorHeight', as_name='maxCreatorHeight'),
TagField('$.preferredBodyTypeTags[*]', as_name='preferredBodyTypeTags'),
TagField('$.preferredTopSizeTags[*]', as_name='preferredTopSizeTags'),
TagField('$.preferredBottomSizeTags[*]', as_name='preferredBottomSizeTags'),
TagField('$.preferredContentsAverageViewsTags[*]', as_name='preferredContentsAverageViewsTags'),
TagField('$.preferredContentsAgeTags[*]', as_name='preferredContentsAgeTags'),
TagField('$.preferredContentsGenderTags[*]', as_name='preferredContentsGenderTags'),
TagField('$.preferredContentsLengthTags[*]', as_name='preferredContentsLengthTags'),
)
},
{
Expand All @@ -382,9 +393,20 @@ def create_search_indexes(self):
'schema': (
NumericField('$.campaignId', as_name='campaignId'),
TagField('$.categories[*]', as_name='categories'),
# 이산형 태그
TagField('$.preferredFashionTags[*]', as_name='preferredFashionTags'),
TagField('$.preferredBeautyTags[*]', as_name='preferredBeautyTags'),
TagField('$.preferredContentTags[*]', as_name='preferredContentTags'),
# 연속형 태그
NumericField('$.minCreatorHeight', as_name='minCreatorHeight'),
NumericField('$.maxCreatorHeight', as_name='maxCreatorHeight'),
TagField('$.preferredBodyTypeTags[*]', as_name='preferredBodyTypeTags'),
TagField('$.preferredTopSizeTags[*]', as_name='preferredTopSizeTags'),
TagField('$.preferredBottomSizeTags[*]', as_name='preferredBottomSizeTags'),
TagField('$.preferredContentsAverageViewsTags[*]', as_name='preferredContentsAverageViewsTags'),
TagField('$.preferredContentsAgeTags[*]', as_name='preferredContentsAgeTags'),
TagField('$.preferredContentsGenderTags[*]', as_name='preferredContentsGenderTags'),
TagField('$.preferredContentsLengthTags[*]', as_name='preferredContentsLengthTags'),
)
},
{
Expand Down
34 changes: 17 additions & 17 deletions data/main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -104,9 +104,9 @@ def _create_master_account(self):
def generate_all(self, user_count=50, brand_count=20, campaign_count=30,
room_count=20, messages_per_room=10, applies_per_campaign=3,
reset=True):
if reset:
self._clear_existing_data()
self._create_master_account()
# if reset:
# self._clear_existing_data()
# self._create_master_account()

print("[시작] 더미 데이터 생성 시작...\n")
print(f"생성할 데이터:")
Expand All @@ -117,26 +117,26 @@ def generate_all(self, user_count=50, brand_count=20, campaign_count=30,

try:
# 시드 데이터 먼저 생성 (태그, 약관 등)
seed_gen = SeedGenerator(self.connection)
seed_gen.generate_all()
# seed_gen = SeedGenerator(self.connection)
# seed_gen.generate_all()

user_gen = UserGenerator(self.connection)
user_gen.generate_all(user_count)
# user_gen = UserGenerator(self.connection)
# user_gen.generate_all(user_count)

brand_gen = BrandGenerator(self.connection)
brand_gen.generate_all(brand_count)
# brand_gen = BrandGenerator(self.connection)
# brand_gen.generate_all(brand_count)

campaign_gen = CampaignGenerator(self.connection)
campaign_gen.generate_all(campaign_count)
# campaign_gen = CampaignGenerator(self.connection)
# campaign_gen.generate_all(campaign_count)

# 캠페인 생성 후 협찬 데이터 생성
brand_gen.generate_sponsors()
# # 캠페인 생성 후 협찬 데이터 생성
# brand_gen.generate_sponsors()

tag_gen = TagGenerator(self.connection)
tag_gen.generate_all()
# tag_gen = TagGenerator(self.connection)
# tag_gen.generate_all()

business_gen = BusinessGenerator(self.connection)
business_gen.generate_all(applies_per_campaign)
# business_gen = BusinessGenerator(self.connection)
# business_gen.generate_all(applies_per_campaign)

#chat_gen = ChatGenerator(self.connection)
#chat_gen.generate_all(room_count, messages_per_room)
Expand Down
103 changes: 79 additions & 24 deletions src/main/java/com/example/RealMatch/match/application/service/MatchServiceImpl.java
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Original file line number Diff line number Diff line change
Expand Up @@ -67,6 +67,13 @@ public class MatchServiceImpl implements MatchService {
private static final Logger LOG = LoggerFactory.getLogger(MatchServiceImpl.class);
private static final int TOP_MATCH_COUNT = 10;

private static final Map<String, List<String>> USER_TYPE_TAG_MAP = Map.of(
"유연한 연출가", List.of("기획중심", "구조탄탄", "디테일중심"),
"섬세한 설계자", List.of("연출유연", "트렌드 적용", "브랜드 이해도"),
"재치있는 스토리텔러", List.of("스토리감각", "소통형콘텐츠", "일상공감"),
"도전적인 실험가", List.of("콘셉트실험", "포맷도전", "새로움 추구")
);

private final RedisDocumentHelper redisDocumentHelper;

private final BrandRepository brandRepository;
Expand All @@ -87,13 +94,9 @@ public class MatchServiceImpl implements MatchService {
private final UserRepository userRepository;
private final UserMatchingDetailRepository userMatchingDetailRepository;

/**
* 매칭 검사는 다음을 하나의 트랜잭션으로 처리한다.
* - 기존 UserMatchingDetail 폐기
* - 새 UserMatchingDetail 생성 (creatorType + snsUrl만)
* - TagUser 전량 교체 저장 (나머지 정보 전부 user_tag로)
* - 브랜드/캠페인 매칭 히스토리 갱신
*/
// ******* //
// 매칭 검사 //
// ******* //
Comment on lines +97 to +99
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@gemini-code-assist gemini-code-assist bot Feb 11, 2026

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medium

match 메서드의 트랜잭션 범위와 동작을 설명하던 기존 Javadoc 주석이 제거되었습니다. 해당 주석은 메서드의 역할을 이해하는 데 매우 유용했습니다. 코드의 명확성과 유지보수성을 위해 이전 Javadoc을 복원하거나, 최소한 메서드의 핵심 동작을 설명하는 주석을 유지하는 것을 제안합니다.

@Override
@Transactional
public MatchResponseDto match(Long userId, MatchRequestDto requestDto) {
Expand Down Expand Up @@ -125,15 +128,18 @@ public MatchResponseDto match(Long userId, MatchRequestDto requestDto) {
List<CampaignMatchResult> campaignResults = findMatchingCampaignResults(userDoc, userId);
saveMatchHistory(userId, brandResults, campaignResults);

String username = userRepository.findById(userId)
.map(User::getName)
String userNickname = userRepository.findById(userId)
.map(User::getNickname)
.orElse("사용자");

List<String> userTypeTag = USER_TYPE_TAG_MAP.getOrDefault(userType, List.of());

return MatchResponseDto.builder()
.username(username)
.username(userNickname)
.userType(userType)
.userTypeImage("https://ui-avatars.com/api/?name=" + userType + "&background=6366f1&color=fff&size=200")
.typeTag(typeTag)
.userTypeTag(userTypeTag)
.highMatchingBrandList(brandListDto)
.build();
}
Expand Down Expand Up @@ -353,14 +359,63 @@ private String determineUserType(UserTagDocument userDoc) {
int fashionCount = safeSize(userDoc.getFashionTags());
int beautyCount = safeSize(userDoc.getBeautyTags());
int contentCount = safeSize(userDoc.getContentTags());
int total = fashionCount + beautyCount + contentCount;

if (fashionCount >= beautyCount && fashionCount >= contentCount) {
if (total == 0) {
return "유연한 연출가";
} else if (beautyCount >= contentCount) {
return "트렌드 리더";
} else {
return "콘텐츠 크리에이터";
}

// 체형/사이즈 디테일 입력 여부
boolean hasBodyDetail = userDoc.getHeightTag() != null
|| userDoc.getBodyTypeTag() != null
|| userDoc.getTopSizeTag() != null
|| userDoc.getBottomSizeTag() != null;

// SNS 시청자 정보 입력 여부
boolean hasSnsAudience = safeSize(userDoc.getContentsAgeTags()) > 0
|| safeSize(userDoc.getContentsGenderTags()) > 0
|| safeSize(userDoc.getContentsLengthTags()) > 0
|| safeSize(userDoc.getAverageContentsViewsTags()) > 0;

// 각 카테고리가 전체에서 차지하는 비율
double fashionRatio = (double) fashionCount / total;
double beautyRatio = (double) beautyCount / total;
double contentRatio = (double) contentCount / total;

// 3개 카테고리 중 비어있지 않은 카테고리 수
int filledCategories = (fashionCount > 0 ? 1 : 0)
+ (beautyCount > 0 ? 1 : 0)
+ (contentCount > 0 ? 1 : 0);

// 도전적인 실험가: 3개 카테고리 모두 보유 + 특정 카테고리 쏠림 없음 (최대 비율 50% 이하)
double maxRatio = Math.max(fashionRatio, Math.max(beautyRatio, contentRatio));
if (filledCategories == 3 && maxRatio <= 0.5) {
return "도전적인 실험가";
}

// 재치있는 스토리텔러: 콘텐츠 태그가 가장 많거나, SNS 시청자 정보가 있으면서 콘텐츠 비중이 높은 경우
if (contentRatio >= fashionRatio && contentRatio >= beautyRatio) {
if (hasSnsAudience || contentCount >= beautyCount + fashionCount) {
return "재치있는 스토리텔러";
}
}

// 유연한 연출가: 패션 태그가 가장 많고 체형 디테일까지 입력한 경우
if (fashionRatio >= beautyRatio && fashionRatio >= contentRatio && hasBodyDetail) {
return "유연한 연출가";
}

// 섬세한 설계자: 뷰티 비중이 높거나 패션+뷰티 균형
if (beautyRatio >= fashionRatio && beautyRatio >= contentRatio) {
return "섬세한 설계자";
}

// 패션이 높지만 체형 디테일 없음 → 섬세한 설계자 (트렌드/브랜드 중심)
if (fashionRatio >= contentRatio && !hasBodyDetail) {
return "섬세한 설계자";
}

return "유연한 연출가";
}

private List<String> determineTypeTags(UserTagDocument userDoc) {
Expand All @@ -385,10 +440,10 @@ private List<String> determineTypeTags(UserTagDocument userDoc) {
// Redis에서 매칭 요청 //
// **************** //
private List<BrandMatchResult> findMatchingBrandResults(UserTagDocument userDoc, Long userId) {
List<BrandTagDocument> allBrandDocs = redisDocumentHelper.findAllBrandTagDocuments();
List<BrandTagDocument> candidateBrandDocs = redisDocumentHelper.findCandidateBrands(userDoc);

if (allBrandDocs.isEmpty()) {
LOG.info("No brand tag documents found in Redis");
if (candidateBrandDocs.isEmpty()) {
LOG.info("No candidate brand documents found via FT.SEARCH");
return List.of();
}

Expand All @@ -398,7 +453,7 @@ private List<BrandMatchResult> findMatchingBrandResults(UserTagDocument userDoc,

Set<Long> recruitingBrandIds = getRecruitingBrandIds();

return allBrandDocs.stream()
return candidateBrandDocs.stream()
.map(brandDoc -> new BrandMatchResult(
brandDoc,
MatchScoreCalculator.calculateBrandMatchScore(userDoc, brandDoc),
Expand All @@ -411,23 +466,23 @@ private List<BrandMatchResult> findMatchingBrandResults(UserTagDocument userDoc,
}

private List<CampaignMatchResult> findMatchingCampaignResults(UserTagDocument userDoc, Long userId) {
List<CampaignTagDocument> allCampaignDocs = redisDocumentHelper.findAllCampaignTagDocuments();
List<CampaignTagDocument> candidateCampaignDocs = redisDocumentHelper.findCandidateCampaigns(userDoc);

if (allCampaignDocs.isEmpty()) {
LOG.info("No campaign tag documents found in Redis");
if (candidateCampaignDocs.isEmpty()) {
LOG.info("No candidate campaign documents found via FT.SEARCH");
return List.of();
}

Set<Long> likedCampaignIds = campaignLikeRepository.findByUserId(userId).stream()
.map(like -> like.getCampaign().getId())
.collect(Collectors.toSet());

List<Long> campaignIds = allCampaignDocs.stream()
List<Long> campaignIds = candidateCampaignDocs.stream()
.map(CampaignTagDocument::getCampaignId)
.toList();
Map<Long, Long> applyCountMap = getApplyCountMapForCampaignIds(campaignIds);

return allCampaignDocs.stream()
return candidateCampaignDocs.stream()
.filter(campaignDoc -> campaignDoc.getRecruitEndDate() == null
|| campaignDoc.getRecruitEndDate().isAfter(LocalDateTime.now()))
.map(campaignDoc -> new CampaignMatchResult(
Expand Down
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