vec2me

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Experiments in identifying someone's interests/knowledge using word embedding & topic modeling

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# vec2me
Experiments in identifying someone's interests/knowledge using word embedding &amp; topic modeling

### Ideas

#### Training data?
- Hacker News headlines (and comments?) ([All HN headlines w/10+ comments from users who've made 10+ comments](https://zenodo.org/record/45901))
- scraped extracted URLs
- use [sense2vec](https://github.com/spacy-io/sense2vec)
- Wikipedia
 
### Inspiration

### Research

#### [words2map](https://github.com/overlap-ai/words2map) - word2vec + t-SNE + HDBSCAN
words2map blogpost: [Making Sense of Everything with words2map](http://blog.yhat.com/posts/words2map.html)

[Visualizing Clusters of Clickbait Headlines Using Spark, Word2vec, and Plotly](http://minimaxir.com/2016/08/clickbait-cluster/) - built on top of words2map

#### [Facebook's fastText](https://github.com/facebookresearch/fastText)
fastText paper on word representations: [*Enriching Word Vectors with Subword Information*](https://arxiv.org/pdf/1607.04606v1.pdf)
fastText paper on text classification: [*Bag of Tricks for Efficient Text Classification*](https://arxiv.org/pdf/1607.01759v2.pdf)

#### [lda2vec](https://github.com/cemoody/lda2vec) - mixes lda and word2vec
lda2vec paper: [Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec](https://arxiv.org/pdf/1605.02019v1.pdf)

#### [entity2vec](https://github.com/ot/entity2vec) - finding related info sources (to expand training data?)
entity2vec paper: [Fast and Space-efficient Entity Linking in Queries](www.di.unipi.it/~ottavian/files/wsdm15_fel.pdf))

#### [topicvec](https://github.com/askerlee/topicvec) - a different way of mixing lda w/word vectors
topicvec paper: [Generative Topic Embedding: a Continuous Representation of Documents](https://arxiv.org/pdf/1606.02979v2))

#### [LFTM](https://github.com/datquocnguyen/LFTM) - a third attempt at mixing LDA and word2vec
LFTM paper: [Improving Topic Models with Latent Feature Word Representations](https://transacl.org/ojs/index.php/tacl/article/download/582/158)

#### [Word Mover's Distance](https://github.com/mkusner/wmd) - proposed future work at end of topicvec paper (unsure if it can be used here)
wmd paper: [From Word Embeddings To Document Distances](http://jmlr.org/proceedings/papers/v37/kusnerb15.pdf)

#### [tweet2vec](https://github.com/bdhingra/tweet2vec) - predicts hashtags via character-level vectors? (probably not useful for this)
tweet2vec paper: [Tweet2Vec: Character-Based Distributed Representations for Social Media](https://arxiv.org/abs/1605.03481)

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