It combines the (arguably) best properties of both algorithms. Word2vec is local and creates word representations that are powerful and flexible. LDA is global, creating document representations that are less flexible but very interpretable to humans. lda2vec mixes both ideas.
Ultimately, the goal is to use all of the information that is usually available alongside text. Word2vec treats this text like one long string. LDA has the notion of documents. But lda2vec can use more features (for example) the zip code a client comment might come from (and so you get regional topics, like outer wear in Vermont or cowboy boots in Texas) the client ID a comment comes from (so you get that a client might be a sporty client, or a expecting mother) in addition to document-level topics (which might surface customer comments like "perfect service!" or package delivery problems). Those topics are readily consumed by analysts and can be used to understand the business from the client's perspective; word2vec on the other hand produces representations that are hard for anything but machines to consume.
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I know word2vec and LDA separately, but what does this work do? Somehow combine the word similarities from word2vec when forming LDA topics?
It combines the (arguably) best properties of both algorithms. Word2vec is local and creates word representations that are powerful and flexible. LDA is global, creating document representations that are less flexible but very interpretable to humans. lda2vec mixes both ideas.
Ultimately, the goal is to use all of the information that is usually available alongside text. Word2vec treats this text like one long string. LDA has the notion of documents. But lda2vec can use more features (for example) the zip code a client comment might come from (and so you get regional topics, like outer wear in Vermont or cowboy boots in Texas) the client ID a comment comes from (so you get that a client might be a sporty client, or a expecting mother) in addition to document-level topics (which might surface customer comments like "perfect service!" or package delivery problems). Those topics are readily consumed by analysts and can be used to understand the business from the client's perspective; word2vec on the other hand produces representations that are hard for anything but machines to consume.