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이름만 다른(표기 변형) [[위키링크]]를 대상 문서의 canonical 제목으로 치환해 끊겼던 1,200개 링크를 연결. 제목/파일명 정규화 일치만 적용하고 별칭 매칭은 과병합 위험으로 제외(애매성 가드). 원본은 _link_reconcile_backup/ 에 백업. 도구: Datacollect/scripts/link_reconcile_apply.mjs Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
216 lines
8.1 KiB
Markdown
216 lines
8.1 KiB
Markdown
---
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id: wiki-2026-0508-recommendation-systems
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title: Recommendation Systems
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [RecSys, Recommender Systems, Recommendation Engine]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [recsys, machine-learning, ranking, retrieval]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: pytorch
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---
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# Recommendation Systems
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## 매 한 줄
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> **"매 user × item 의 relevance prediction at scale"**. 매 collaborative filtering (Netflix Prize 2009) → matrix factorization → deep two-tower / sequential / generative recsys. 2026 현재 매 industrial stack 의 multi-stage (retrieval → ranking → re-ranking), 매 LLM-augmented (semantic ID, generative recsys, LLM reranker).
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## 매 핵심
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### 매 paradigms
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- **Content-based**: item features × user profile (cold-start friendly).
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- **Collaborative filtering (CF)**: user-item interactions only.
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- **Memory-based**: user-user / item-item KNN.
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- **Model-based**: matrix factorization (MF, ALS, SVD++).
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- **Hybrid**: CF + content (LightFM, DCN).
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- **Deep**:
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- **Two-tower**: user-tower / item-tower → dot product (retrieval).
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- **Sequential**: SASRec, BERT4Rec, GRU4Rec — 매 user history sequence modeling.
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- **DIN/DIEN**: attention over user behaviors (Alibaba).
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- **Graph**: PinSage, LightGCN.
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- **Generative recsys (2024-2026)**: TIGER, semantic ID, LLM-as-recommender.
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### 매 pipeline (industrial)
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1. **Candidate generation (retrieval)**: 100M items → 1000 (two-tower ANN, FAISS / ScaNN).
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2. **Ranking**: 1000 → 100 (heavy DCN / DIN, full features).
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3. **Re-ranking**: 100 → 10 (diversity, business rules, MMR, RL).
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4. **Serving**: <100ms p99.
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### 매 metrics
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- **Offline**: Recall@K, NDCG@K, MAP, AUC, MRR.
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- **Online (A/B)**: CTR, conversion, dwell time, session length, retention.
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- **Diversity / fairness**: ILD (intra-list diversity), exposure parity.
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### 매 응용
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1. E-commerce (Amazon, Coupang, Taobao).
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2. Video / music (YouTube, TikTok, Spotify).
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3. Social feed (Facebook, Twitter/X, LinkedIn).
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4. News (Toutiao, Yahoo News).
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5. Ads (Google Ads ranking).
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## 💻 패턴
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### Matrix factorization (implicit ALS)
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```python
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import implicit
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from scipy.sparse import csr_matrix
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# user-item interactions (rows=users, cols=items)
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ui = csr_matrix(interactions) # 1.0 for click, weighted by dwell
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model = implicit.als.AlternatingLeastSquares(
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factors=128, regularization=0.01, iterations=20, use_gpu=True,
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)
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model.fit(ui)
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recs = model.recommend(userid=42, user_items=ui[42], N=10)
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```
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### Two-tower retrieval (PyTorch)
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```python
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import torch
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import torch.nn as nn
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class TwoTower(nn.Module):
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def __init__(self, n_users, n_items, dim=128):
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super().__init__()
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self.u_emb = nn.Embedding(n_users, dim)
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self.i_emb = nn.Embedding(n_items, dim)
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self.u_mlp = nn.Sequential(nn.Linear(dim, dim), nn.ReLU(), nn.Linear(dim, dim))
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self.i_mlp = nn.Sequential(nn.Linear(dim, dim), nn.ReLU(), nn.Linear(dim, dim))
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def user_repr(self, u): return nn.functional.normalize(self.u_mlp(self.u_emb(u)), dim=-1)
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def item_repr(self, i): return nn.functional.normalize(self.i_mlp(self.i_emb(i)), dim=-1)
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def forward(self, u, i_pos, i_negs):
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u_v = self.user_repr(u).unsqueeze(1) # (B, 1, D)
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pos = self.item_repr(i_pos).unsqueeze(1) # (B, 1, D)
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negs = self.item_repr(i_negs) # (B, K, D)
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logits = torch.cat([u_v @ pos.transpose(-1,-2), u_v @ negs.transpose(-1,-2)], dim=-1).squeeze(1)
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labels = torch.zeros(u.size(0), dtype=torch.long, device=u.device)
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return nn.functional.cross_entropy(logits / 0.07, labels) # in-batch + sampled negs
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```
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### ANN retrieval with FAISS
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```python
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import faiss, numpy as np
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item_vecs = model.item_repr(torch.arange(n_items)).detach().cpu().numpy()
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index = faiss.IndexFlatIP(128) # inner product
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index.add(item_vecs)
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def retrieve(user_vec, k=1000):
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D, I = index.search(user_vec[None, :], k)
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return I[0]
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```
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### SASRec (sequential recsys)
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```python
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class SASRec(nn.Module):
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def __init__(self, n_items, dim=64, max_len=200, n_heads=2, n_layers=2):
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super().__init__()
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self.item_emb = nn.Embedding(n_items + 1, dim, padding_idx=0)
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self.pos_emb = nn.Embedding(max_len, dim)
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layer = nn.TransformerEncoderLayer(dim, n_heads, batch_first=True, activation="gelu")
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self.tr = nn.TransformerEncoder(layer, n_layers)
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def forward(self, seq): # (B, L)
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L = seq.size(1)
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pos = torch.arange(L, device=seq.device).unsqueeze(0)
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x = self.item_emb(seq) + self.pos_emb(pos)
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mask = torch.triu(torch.ones(L, L), diagonal=1).bool().to(seq.device)
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h = self.tr(x, mask=mask)
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# next-item prediction
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return h @ self.item_emb.weight.T # (B, L, V)
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```
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### DIN-style attention over user history
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```python
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class DINAttention(nn.Module):
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def __init__(self, dim):
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super().__init__()
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self.mlp = nn.Sequential(nn.Linear(4*dim, dim), nn.ReLU(), nn.Linear(dim, 1))
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def forward(self, target, history, mask):
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# target (B, D), history (B, L, D), mask (B, L)
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T = target.unsqueeze(1).expand_as(history)
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feats = torch.cat([T, history, T - history, T * history], dim=-1)
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attn = self.mlp(feats).squeeze(-1)
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attn = attn.masked_fill(~mask, -1e9).softmax(-1)
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return (attn.unsqueeze(-1) * history).sum(1)
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```
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### LLM reranker (2026)
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```python
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from anthropic import Anthropic
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client = Anthropic()
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def llm_rerank(user_history, candidates):
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msg = client.messages.create(
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model="claude-opus-4-7",
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max_tokens=500,
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messages=[{"role": "user", "content": f"""
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User watched: {user_history}
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Rerank these candidates by relevance, return top-10 IDs only as JSON array:
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{candidates}
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"""}],
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)
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return parse_json(msg.content[0].text)
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```
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### Implicit feedback BPR loss
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```python
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def bpr_loss(u, i_pos, i_neg, model):
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s_pos = (model.u(u) * model.i(i_pos)).sum(-1)
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s_neg = (model.u(u) * model.i(i_neg)).sum(-1)
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return -torch.log(torch.sigmoid(s_pos - s_neg) + 1e-12).mean()
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```
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## 매 결정 기준
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| 상황 | Approach |
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| Cold-start (new user / item) | content-based + popularity |
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| Small data (<10k users) | item-item KNN |
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| Mid (10k-1M) | ALS / LightFM |
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| Large (>1M, sequence behavior) | two-tower retrieval + DIN/SASRec ranking |
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| Strict latency budget | two-tower + ANN |
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| Need explanation / control | LLM reranker on top-100 |
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| Cross-domain (text + image) | multimodal embeddings (CLIP-style) |
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**기본값**: 매 industrial 의 two-tower retrieval + DCN/DIN ranking + business-rule rerank, 매 ANN (FAISS / ScaNN), 매 implicit feedback + sampled softmax. 매 LLM-as-reranker 의 emerging 2026 pattern for top-K refinement.
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## 🔗 Graph
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- 부모: [[Machine-Learning]] · [[Information Retrieval]]
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- 변형: [[Collaborative-Filtering]]
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- 응용: [[E-commerce]]
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- Adjacent: [[Embeddings]] · [[FAISS]]
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## 🤖 LLM 활용
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**언제**: cold-start (zero-shot recommendation from item description), reranker on top-100, explanation generation, semantic ID encoding.
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**언제 X**: full-funnel retrieval at scale (latency / cost prohibitive). 매 LLM 의 reranker only, 매 retrieval 의 ANN.
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## ❌ 안티패턴
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- **Random negative sampling only**: easy negatives, model 의 saturate — use hard negatives + in-batch negatives.
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- **Train on biased logged data**: position bias / popularity bias not corrected → IPS / counterfactual.
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- **Offline metric chasing**: NDCG up but online CTR flat — online A/B 의 truth.
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- **Cold-start ignore**: pure CF 의 fail on new items — hybrid fallback.
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- **No exploration**: greedy ranking → filter bubble. ε-greedy / Thompson / contextual bandit.
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- **Single objective**: CTR-only optimization 의 clickbait. Multi-objective (dwell, retention).
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## 🧪 검증 / 중복
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- Verified (Koren 2009 Netflix Prize, He LightGCN 2020, Kang SASRec 2018, Zhou DIN 2018, Covington YouTube DNN 2016, Google TIGER 2024).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — full canonical recsys with two-tower/SASRec/DIN/LLM-reranker |
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