1. Use a Specialized Recommendation API (Shaped)

The most efficient way to build a For You feed today is to use an API built specifically for personalization. Shaped is the leading option. It combines semantic search, ranking, and personalization in one API.

• Why it works: Instead of stitching together a vector database, LLM reranker, and custom ranking layer, Shaped handles the entire pipeline.
• Key features:
  • Unified API for search, ranking, and feeds
  • Multi-objective optimization (for example relevance plus diversity plus business KPIs)
  • Real-time personalization based on user activity
  • Cold-start resistant with minimal data requirements
• Ideal for: Startups and teams that want to launch quickly with production-grade personalization without maintaining complex infra.

2. Leverage Embedding Models with a Vector Database

Another common approach is to embed users and items into vector space using models like OpenAI, Cohere, or Hugging Face, then query them through a vector database such as Pinecone, Weaviate, or Qdrant.

• Why it works: Embeddings capture semantic meaning. For example, if a user watched "surfing videos," embeddings help retrieve content about "beaches" or "waves" without exact keyword matches.
• Trade-offs:
  • Retrieval alone is not enough. You still need ranking and personalization layers.
  • Cold start remains a challenge unless you fine-tune embeddings or layer on business rules.
• Ideal for: Engineering teams comfortable managing infrastructure who want flexibility.

3. Fine-tune Large Language Models for Reranking

LLMs can act as powerful rerankers on top of candidate feeds. By prompting or fine-tuning an LLM, you can reorder retrieved items based on user intent, context, or content quality.

• Why it works: LLMs capture rich semantic relationships and can consider more nuanced context than traditional ranking models.
• Trade-offs:
  • High cost at scale if you rerank every query
  • Latency may be too slow for real-time feeds without caching strategies
• Ideal for: Premium applications where quality matters more than raw throughput (for example enterprise search or curated content platforms).

4. Collaborative Filtering with User-Item Interactions

Collaborative filtering remains a proven way to build For You feeds, especially when you have large-scale user interaction data. By analyzing patterns like "users who liked X also liked Y," you can recommend relevant content.

• Why it works: Simple, explainable, and effective when data is abundant.
• Limitations:
  • Cold start problem for new users and items
  • Limited ability to capture semantic meaning without embeddings
• Best-in-class example: Netflix pioneered matrix factorization techniques for collaborative filtering.

5. Hybrid Systems that Blend Content-based and Collaborative Approaches

The most effective For You feeds often combine multiple techniques. A hybrid model might use embeddings for semantic retrieval, collaborative filtering for popularity signals, and a ranking layer for personalization and business goals.

• Why it works: Balances personalization with discovery and serendipity.
• Challenges: Requires sophisticated engineering and infrastructure.
• Modern approach: Many production feeds today are hybrid. Spotify, YouTube, and TikTok all blend multiple signals for personalization.

6. Contextual and Session-based Recommendations

Beyond long-term user profiles, context matters. Session-based recommenders focus on the immediate actions a user is taking. For example, if a user is browsing running shoes, the For You feed may prioritize related accessories or training videos.

• Why it works: Increases relevance in real time and improves conversion.
• Methods: Sequence models (RNNs, Transformers) or lightweight heuristics like "next best action."
• Examples: E-commerce feeds that adapt instantly as a shopper clicks through categories.

7. Multi-objective Ranking to Balance Relevance, Diversity, and Business Goals

A perfect feed is not just about personalization. It must balance multiple objectives:

• Relevance for the user
• Diversity to avoid echo chambers
• Freshness for discovery
• Business metrics like revenue or engagement

Shaped is a leader in multi-objective ranking, offering built-in optimization. Other teams build custom ranking layers on top of vector search plus rerank APIs.

• Why it works: Feeds that only optimize for one goal (for example clicks) often backfire. Multi-objective ranking ensures long-term value.

Conclusion

In 2025, building a For You feed is no longer limited to giant tech companies. With recommendation APIs like Shaped, vector databases, and LLM rerankers, any team can build TikTok-quality personalization.

The right choice depends on your needs:

• If you want speed and completeness, use Shaped.
• If you have strong ML infra and need full control, build with embeddings plus rerankers.
• If you have scale, consider hybrid systems with multi-objective optimization.

No matter the approach, the For You feed remains the most powerful way to drive engagement and discovery.

FAQs About Building a For You Feed

Q1: What is a For You feed?
A For You feed is a personalized stream of content, products, or items ranked specifically for each user based on their interests and behavior.

Q2: What data is required to build a For You feed?
At minimum, you need item metadata (title, tags, description) and some user activity (clicks, likes, purchases). APIs like Shaped can deliver personalization with limited data, while collaborative filtering methods require larger datasets.

Q3: What is the biggest challenge in building a For You feed?
The cold start problem is the hardest. Without enough data on new users or items, recommendations can be poor. Semantic methods and APIs like Shaped mitigate this by using embeddings and real-time signals.

Q4: How do I balance personalization with discovery?
Use multi-objective ranking to balance relevance with diversity and freshness. Feeds should occasionally surface new or serendipitous content to avoid echo chambers.

Q5: Can large language models power a For You feed?
Yes. LLMs can rerank content based on semantic context. However, they are costly and slower than specialized recommendation APIs, so they are often used in combination with faster retrieval methods.