> For the complete documentation index, see [llms.txt](https://longxingtan.gitbook.io/mle-interview/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://longxingtan.gitbook.io/mle-interview/03_system/03_ml/poi_recommendation.md).

# POI推荐

> 场景: yelp, 美团, airbnb
>
> * Design a system to find nearby restaurants
> * Design a system to match drivers with riders for Uber
> * Design a system to compute ETA for food delivery

> 特点: 如果是event 推荐这种注重实效性、位置性的推荐，event发生后不存在了，所有item可以认为都是冷启动
>
> * 对于位置的挖掘可采用图特征或模型

## 1. requirements

**products/use cases**

* User Search: Users search for restaurants based on location, cuisine, and preferences.
* Real-Time Recommendations: Provide real-time recommendations based on user queries.
* Cold Start: Handle new restaurants with limited data.

**objective**

* Connect Users with Local Businesses: Help users discover great local businesses
* Increase Engagement: Encourage users to explore and interact with more POIs.

**constraint**

* Data Constraints: Limited data on new restaurants.
* Volume: Handle a high volume of users and queries.
* Latency: Provide real-time results with low latency (e.g., < 200ms).

## 2. ML task & pipeline

预测目标

* 是否点击
* 停留时间(dwell time), 可转化为t/(t+1)来逼近sigmoid函数，t很大时接近1；很小时接近0

## 3. data

**Data collection**

* User Profiles: Demographics, preferences, and past interactions
* POI Data: Location, cuisine, ratings, reviews, and other attributes
  * User location: For localized recommendations we need to consider only businesses near the city or neighborhood where the user is located
  * Business Data: Restaurant location, cuisine type, user ratings, and reviews
* Interaction Data: Past searches, clicks, and visits

**Data Processing**

* Data Cleaning: Handle missing data and outliers
* Data Integration: Combine data from different sources into a unified format
* Data Augmentation: Use techniques like synthetic data generation to handle cold start problems

## 4. feature

**sparse**

**dense**

**User Features**

* Demographics: Age, location
* Preferences: Favorite cuisines, price range
* Behavior: Past searches, clicks, and visits

**POI Features**

* Location: Latitude, longitude, and proximity to the user
* Attributes: Cuisine, price range, ratings, reviews
* Popularity: Number of visits, ratings, and reviews

**Context Features**

* Time of Day: Recommendations may vary based on the time of day
* Device: Recommendations may differ based on the device used (e.g., mobile vs. desktop)

## 5. model

**retrieval**

* 取决于filter
* Collaborative Filtering: Recommend POIs based on similar users' preferences
* Content-Based Filtering: Recommend POIs similar to those the user has interacted with in the past
* Graph-Based Models: Use graph algorithms (e.g., Node2Vec, GraphSAGE) to capture spatial relationships between POIs

**ranking**

**rerank**

## 6. evaluation

* offline
  * NDCG
  * MAP
  * precision, recall, and AUC-ROC
* online: A/B testing holdout canary

## 7. deploy & serving

* Batch Serving: Periodically update restaurant recommendations.
* Online Serving: Real-time requests for user queries.

## 8. monitor & maintenance

## 9. 优化与问答

* 冷启动的item
  * 双塔可以采用default embedding, 而不是random initial

## reference

* [yelp-Beyond Matrix Factorization: Using hybrid features for user-business recommendations](https://engineeringblog.yelp.com/2022/04/beyond-matrix-factorization-using-hybrid-features-for-user-business-recommendations.html)
* [Yelp Food Recommendation System](https://cs229.stanford.edu/proj2013/SawantPai-YelpFoodRecommendationSystem.pdf)
* [美团-旅游推荐系统的演进](https://tech.meituan.com/2017/03/24/travel-recsys.html)
* [美团-基于机器学习方法的POI品类推荐算法](https://tech.meituan.com/2014/12/18/poi-category-recommendation-algorithm-based-on-machine-learning.html)
* [Design Yelp](https://systemdesignschool.io/problems/yelp/solution)
* [System Design — Nearby Places Recommender System](https://mecha-mind.medium.com/system-design-nearby-places-recommender-system-7ac53e27c977)
* [Food Discovery with Uber Eats: Recommending for the Marketplace](https://www.uber.com/en-CA/blog/uber-eats-recommending-marketplace/)