Chapter 6. Document embeddings for rankings and recommendations

This chapter covers

  • Generating document embeddings using paragraph vectors
  • Using paragraph vectors for ranking
  • Retrieving related content
  • Improving related-content retrieval with paragraph vectors

In the previous chapter, I introduced you to neural information retrieval models by building a ranking function based on averaged word embeddings. You averaged word embeddings generated by word2vec to obtain a document embedding, a dense representation of a sequence of words, that demonstrated high precision in ranking documents according to user intent.

The drawback of common retrieval models such as Vector Space Model with TF-IDF and BM25, however, is that they only look at single ...

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