Alternatives to BoW in KNN - TFIDF?

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Probably TF-IDF, but Word embedding would be ideal

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https://towardsdatascience.com/3-basic-approaches-in-bag-of-words-which-are-better-than-word-embeddings-c2cbc7398016

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Phrase embeddings:

https://towardsdatascience.com/fse-2b1ffa791cf9

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4. More meaningful alternatives to the 'Bag of Words' approach: I will probably implement an improvement of count occurrence like TF-IDF. I would like to experiment with using phrase embeddings in KNNs - although I have not found very much advice on this so far, and need to conduct more research. I would welcome you advice on this, if you have any suggestions?

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suggested research from Giseli:

https://www.researchgate.net/publication/326425709_Text_Mining_Use_of_TF-IDF_to_Examine_the_Relevance_of_Words_to_Documents

• https://www.researchgate.net/publication/328333048_Modified_TF-IDF_Term_Weighting_Strategies_for_Text_Categorization
• https://arxiv.org/ftp/arxiv/papers/1806/1806.06407.pdf