You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Production Ranking Systems: A Review 
Chapter Title:  Shallow Embeddings 
1. Abstract of Cited Paper (BIB001):  E-commerce sponsored search contributes an important part of revenue for the e-commerce company. In consideration of effectiveness and efficiency, a large-scale sponsored search system commonly adopts a multi-stage architecture. We name these stages as ad retrieval, ad pre-ranking and ad ranking. Ad retrieval and ad pre-ranking are collectively referred to as ad matching in this paper. We propose an end-to-end neural matching framework (EENMF) to model two tasks---vector-based ad retrieval and neural networks based ad pre-ranking. Under the deep matching framework, vector-based ad retrieval harnesses user recent behavior sequence to retrieve relevant ad candidates without the constraint of keyword bidding. Simultaneously, the deep model is employed to perform the global pre-ranking of ad candidates from multiple retrieval paths effectively and efficiently. Besides, the proposed model tries to optimize the pointwise cross-entropy loss which is consistent with the objective of predict models in the ranking stage. We conduct extensive evaluation to validate the performance of the proposed framework. In the real traffic of a large-scale e-commerce sponsored search, the proposed approach significantly outperforms the baseline.

Literature Review:
e simplest architectures to form representations employ shallow architectures such as word2vec over a combination of vectorized interaction data and side information. Here a "word" within the vocabulary can be categorical user actions, a discretized continuous feature, or English words BIB001 . User action data can be taken strictly as words, or as sequences of actions, in which case embeddings can be built from skip-grams of user action sequences BIB001 BIB002 . Multi-hot user action vectors can also be weighted by dwell time BIB001 . is allows us to learn query and item representations unsupervised from user engagement data. To handle cold start, out of vocabulary items can be taken as linear combinations of embeddings of associated in vocabulary items, or corresponding content data BIB001  BIB002 .