Search2Vec at OLX Group - Pydata Meetup Berlin
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Using Query Embeddings based on user sessions for query expansion. Discover how we expand queries at OLX Group by embedding our queries using Neural Networks.
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Search2Vec at OLX Group - Pydata Meetup Berlin
- 1. Query expansion using semantic query embeddings
- 2. Me, OLX, The team Ich heiße Mariano Semelman! Ich komme aus Argentinien. @msemelman mariano.semelman@olx.com I’m a Data Scientist with 6 years of experience working in: ● Behavioural targeting ● Natural language processing ● Recommendation systems ● Search engine
- 3. Me, OLX, The team ● OLX: Online classifieds platform ● Berlin Shared Service: Support and Center of expertise to the rest of the platform. ● PnR Services Team: Search, Recommender systems, Big Data.
- 4. Me, OLX, The team Vladan Radosavljevic Head of Data Science Mariano Semelman Senior Data Scientist Manish Saraswat Data Scientist Vaibhav Sharma Data Scientist
- 5. So frustrating... Reasons: Typos, Wrong brand/model combination, localism, specificity, etc.
- 6. What if we could search not just for what the user searched for, but also for highly similar queries which mean the same?
- 7. Sessions Search Sessions from OLX South Africa “13inch rims, “rims” “205 60 13”, “205”, ”205_13inch” “mountain bicycle”, “fiets”, “bike”, “bicycle” “honda nc 700”, “suzuki sv650”, “honda cbx 250 twister”, “honda xr 125”S1 S2 S3 “fencing”, “devils fork”S4 S5 “ferraro”, “ferrari”, “lamboghini”, “porsche”, “ewings” S6 “catering table”, “funeral tent”, “wedding tent”, “bar stool”, “tiffany chairs”
- 8. Word2Vec or How I Learned to Stop Worrying and Love Embeddings
- 9. Embedding Definition, very easy!: F: X↪Y X: Your domain (example: Words, Categories, etc) Y: Domain with interesting properties for your problem. F: Injective function that translates from X to Y. tricky part: creating F.
- 10. Word2Vec (skip-gram flavour) The fake task! http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/
- 13. Interesting property If word A and word B always have similar context, then cosine_similarity(F(A), F(B)) would tend to 1.
- 14. Gensim code # import modules & set up logging import gensim sentences = [['first', 'sentence'], ['second', 'sentence']] # train word2vec on the two sentences model = gensim.models.Word2Vec(sentences, min_count=1) indexes = model.wv.index2word embedding = model.wv.vectors
- 15. Search2Vec or What does all this have to do with searches... Based on “Scalable Semantic Matching of Queries to Ads in Sponsored Search Advertising” paper.
- 16. Remember the queries... Search Sessions from OLX Data 13inch_rims rims 205_60_13 205 205_13inch mountain_bicycle fiets bike bicycle honda_nc_700 suzuki_sv650 honda_cbx_250_twister honda_xr_125S1 S2 S3 fencing devils_forkS4 S5 ferraro ferrari lamboghini porsche ewings S6 catering_table funeral_tent wedding_tent bar_stool tiffany_chairs
- 17. Remember the queries... Search Sessions from OLX Data honda_nc_700 suzuki_sv650 honda_cbx_250_twister honda_xr_125S1 Train samples: (honda_nc_700, suzuki_sv650) (suzuki_sv650, honda_nc_700) (suzuki_sv650, honda_cbx_250_twister) (honda_cbx_250_twister, suzuki_sv650) (honda_cbx_250_twister, honda_xr_125) (honda_xr_125, honda_cbx_250_twister)
- 18. Training Data ~110M searches across a year ~12M sessions (aka sentences) ~4M unique searches Preprocessing (pyspark): ● lowercase remove trailing spaces, stopwords, punctuation marks, double spaces, etc ● outliers: long “sentences” long tail queries (<10 occurrences)
- 19. We have our model...
- 20. Offline evaluation If you are searching for "${search_string}", do you expect similar results for "${related_query}"? ● 1) very similar results ● 2) related results ● 3) very different results
- 21. Tail queries
- 22. Limitations Head queries: 162k embeddings =) Tail queries: 3.8M =( Frequency Query 10
- 23. Step 1: find top K queries for each head query from the vocabulary query expansions score scuba diving equipment 0.792 diving gear 0.766 scuba diving gear scuba equipment 0.765 scuba gear 0.764 scuba shop 0.763 query expansions score bread maker 0.728 bread machines 0.722 bread machines cusinart bread maker 0.644 bread machine reviews 0.621 bread machine recipes 0.605 query expansions score jeep 0.824 4x4 jeep 0.819 4x4 isuzu 4x4 0.805 toyota 4x4 0.793 hilux 4x4 0.790 Tail queries
- 24. Tail query Step 2: form query documents (ie: flatten) id document scuba_diving_gear scuba diving equipment diving gear scuba equipment scuba gear scuba shop bread_machines bread maker bread machines cusinart bread maker bread machine reviews bread machine recipes 4x4 jeep 4x4 jeep isuzu 4x4 toyota 4x4 hilux 4x4 bakkie nissan 4x4 4x4 off road
- 25. Tail query vectors Step 3: invert index for fast matching (BM25) input query top result top result’s document diving equipment scuba_diving_gea r scuba diving equipment diving gear scuba equipment scuba gear scuba shop cusinart machine bread_machines bread maker bread machines cusinart bread maker bread machine reviews bread machine recipes off road bakkie 4x4 jeep 4x4 jeep isuzu 4x4 toyota 4x4 hilux 4x4 bakkie nissan 4x4 4x4 off road
- 26. Offline Analysis with holdout data 90% 10% ordered by frequency use for testing the matching index query vectors Vq-context 0.3 1.3 6.2 0.5 3.1 Vq-index (top result) cosine similarity 0.2 1.4 7.2 0.6 6.1
- 28. Zahlen bitte!
- 29. We launched to production!!! playstation 4 peugot ktm 900
- 30. 8.7% -> 0.6% Searches with no results
- 31. +13.5%** Increase in new contacts by day
- 33. Possible extensions Include more entities in the sessions: ● Listings the user interacted with ● Categories the user browsed ● Locations the user search for/interacted with Meta-prod2vec: Add side information while generating pairs. Meta-Prod2Vec - Product Embeddings Using Side- Information for Recommendation