推荐系统
[Building a Recommender System using Machine Learning](https://towardsdatascience.com/building-a-recommender-system-using-machine-learning-2eefba9a692e)
[Learning To Rank Diversely](https://medium.com/airbnb-engineering/learning-to-rank-diversely-add6b1929621)
[Learning To Rank Diversely Paper](https://arxiv.org/pdf/2210.07774.pdf)
To parse this unstructured data at scale, Airbnb built LATEX (Listing ATtribute EXtraction), a machine learning system that can extract home attributes from unstructured text data like guest messages and reviews, customer support tickets, and listing descriptions. LATEX accomplishes this in two steps:
- A named entity recognition (NER) module extracts key phrases from unstructured text data
- An entity mapping module then maps these key phrases to home attributes
The named entity recognition (NER) module uses textCNN (convolutional neural network for text) and is trained and fine tuned on human labeled text data from various data sources within Airbnb. In the training dataset, we label each phrase that falls into the following five categories: Amenity, Activity, Event, Specific POI (i.e. “Lake Tahoe”), or generic POI (i.e. “post office”).
The entity mapping module uses an unsupervised learning approach to map these phrases to home attributes. To achieve this, we compute the cosine distance between the candidate phrase and the attribute label in the fine-tuned word embedding space. We consider the closest mapping to be the referenced attribute, and can calculate a confidence score for the mapping.
[Prioritizing Home Attributes Based on Guest Interest](https://medium.com/airbnb-engineering/prioritizing-home-attributes-based-on-guest-interest-3c49b827e51a)
[Recommender System — Matrix Factorization](https://towardsdatascience.com/recommendation-system-matrix-factorization-d61978660b4b)
vivo 短视频推荐去重服务的设计实践
[2023 推荐系统&因果推断 大厂经验梳理（上）](https://zhuanlan.zhihu.com/p/595476222)
