# Low Level Design: Graph-Based Recommendation Engine

Source: https://www.techinterview.org/post/3233470923/lld-recommendation-graph/
Updated: 2026-06-30 · techinterview.org

## Graph-Based Recommendation Engine

Model the recommendation problem as a graph: users and items are nodes, interactions are weighted edges. Graph algorithms then surface relevant items for each user.

### Interaction Graph Structure


```
Bipartite graph: Users (U) <--> Items (I)

Edge weights by interaction type:
  view           = 1
  click          = 3
  purchase       = 5
  explicit_rating = raw rating value (1-5)
```

Higher-weight edges have stronger influence on traversal scores.

## Personalized PageRank (PPR)

Start a random walk from the target user node. At each step:

- With probability 0.85: follow a random outgoing edge (weighted by interaction weight)

- With probability 0.15: teleport back to the source user node

Run N iterations (typically 50-100). Nodes with highest visit frequency = top-K recommendations. PPR naturally handles the graph structure — items reachable through many similar users rank higher.


```
ppr_scores = personalized_pagerank(
    graph=G,
    source=user_node,
    alpha=0.15,       # teleport probability
    iterations=100,
    top_k=200
)
recommendations = [node for node in ppr_scores if node.type == 'item'][:50]
```


## Node2Vec Embeddings

Generate random walk sequences from the interaction graph, then train Word2Vec on those sequences to produce 128-dimensional embeddings for every user and item node.


```
walks = node2vec_walks(G, num_walks=10, walk_length=80, p=1, q=0.5)
model = Word2Vec(walks, vector_size=128, window=5, min_count=1)

user_vec  = model.wv['user_42']
item_vecs = [model.wv[f'item_{i}'] for i in all_items]

# Cosine similarity for ANN lookup
candidates = ann_index.query(user_vec, top_k=200)
```


## Collaborative Filtering on Graph

- Find users similar to target: those sharing >K item interactions (overlap in bipartite neighborhood)

- Collect items those similar users interacted with but target has not

- Rank by weighted interaction score from similar users, decay by similarity distance

## Offline Pipeline


```
Daily Spark job:
  1. Load full interaction graph from data warehouse
  2. Run PPR for all active users (parallelized by user partition)
  3. Run Node2Vec training on full graph
  4. Compute top-200 recommendations per user
  5. Write results to Redis sorted sets
```


## Online Serving


```
-- Pre-computed recs stored in Redis:
ZADD recs:user:42  5.0 item:901  4.8 item:234  4.5 item:567 ...

-- Serving layer reads:
ZREVRANGE recs:user:42 0 49  -- top 50 items
```

Latency target: <10ms per recommendation request. Redis sorted set lookup is O(log N).

## Cold Start

New users have no interaction edges. Fallback strategy: serve popularity-based recommendations (global top items by recent interaction count, bucketed by category). Transition to personalized once the user accumulates >5 interactions.

## Diversity: Maximal Marginal Relevance (MMR)


```
selected = []
candidates = top_200_by_score

while len(selected) < 50:
    best = argmax over candidates of:
        lambda * relevance(item) - (1-lambda) * max_similarity(item, selected)
    selected.append(best)
    candidates.remove(best)
# lambda ~ 0.7 balances relevance vs diversity
```

MMR prevents the recommendation list from being dominated by near-duplicate items (e.g., ten nearly identical products).

See also: [Anthropic Interview Guide 2026: Process, Questions, and AI Safety](https://www.techinterview.org/?p=3233460282)

See also: [Scale AI Interview Guide 2026: Data Infrastructure, RLHF Pipelines, and ML Engineering](https://www.techinterview.org/post/3233460275/scale-ai-interview-guide-2026-data-infrastructure-rlhf-pipelines-and-ml-engineering/)

See also: [Meta Interview Guide 2026: Facebook, Instagram, WhatsApp Engineering](https://www.techinterview.org/post/3233460272/meta-interview-guide-2026-facebook-instagram-whatsapp-engineering/)