Find it! Nail it! Boosting e-commerce search conversions with machine learning at scale
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The document discusses learning-to-rank models for improving search relevance in e-commerce. It describes how traditional information retrieval models do not scale well to modern needs, while learning-to-rank methods can handle thousands of features and implicit user feedback data. The document reports that using listwise learning-to-rank with NDCG as the loss function improved NDCG by 15.6% and increased conversion rates by 7.5% on e-commerce data. It concludes that deep neural network methods may now outperform traditional machine learning for information retrieval tasks.
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Find it! Nail it! Boosting e-commerce search conversions with machine learning at scale
- 1. October 28, 2017 Giuseppe “Pino” Di Fabbrizio Rakuten Institute of Technology – Boston
- 3. 3 • Motivations • Traditional information retrieval models • Learning-to-rank models • Relevance • Ranking Metrics • Algorithms • Ranking optimization • Use cases • Summary • What is next? Disclaimer: If not otherwise specified, images in this presentation comply with the (CC) creative commons publishing license
- 4. 4 • E-commerce growing faster than traditional brick-and-mortar market ($4.06T by 2020) • Mobile shopping adoption increasing worldwide (46% shoppers in Asia and 28% in North America) • Online catalogs offering broader selections and competitive products • Electronic money transactions gaining more consumers’ trust • Massive data collected during web and mobile interactions providing foundation for machine learning- driven optimizations 1.61B Shoppers $1.86T Sales $150B* Revenues ML *2016 Combined revenues for Amazon, Otto Group, and Rakuten https://www.statista.com/topics/871/online-shopping/
- 5. 5
- 7. 7 How do we find the most relevant products for a search query? www.rakuten.com Oct 10, 2017
- 8. 8Query Ranking function Documents www.rakuten.com Nov 2016 1 2 3 4 5 6 7 8 9
- 9. 9 • Relevance is estimated by lexical matches of query terms with document terms • Examples: • Boolean models • Vector space models • Latent semantic indexing • Okapi BM25 Index Indexer Query Documents Scoring model Top-n retrieved documents On-line Off-line
- 10. 10 www.rakuten.com Oct 10, 2017 Query (Q) Document 1 (D1) Document 2 (D2) iphone 7 case iphone 7 Case Q 1 1 1 D1 2 2 2 D2 3 1 0 Q D1 D2
- 11. 11 • Basic ideas • Lexical similarity metrics • Penalizing repeated occurrences of the same term • Penalizing term frequency for longer documents • Only few features • Manually hand-tuned feature weights based on heuristic • Cannot include important search signals such as user’s feedback, product popularity, purchase history, etc. • Fast and scalable
- 12. 12 • Data-driven approach • Directly optimize products rank based on relevance (different from classification and regression ML tasks) • Handle thousands of features • Robust to noisy data • Handle personalization • Industry & research state-of-the-art (Amazon, eBay, Microsoft, Yahoo!, Yandex, etc.)
- 13. 13 A document is relevant if contains the information the user was looking for when submitted the query Relevance is subjective and depends on many factors: • context (what is displayed and how) • task (purchase, search info, answer, etc.) • novelty (unexpected data, ads, ext.) • time and user’s effort involved
- 16. 16 • Clickthrough data (user’s implicit feedback) as source of relevance for search query / document pairs • Pros • Abundant and easy to harvest • Always fresh • Unbiased • Cons • Noisy • Long tail queries • Simple relevance mapping: • score = 0 (not relevant), score = 3 (highly relevant) • Purchase > cart > click > impression Score User’s implicit feedback 3 Product purchased 2 Product added to the shopping cart 1 Product clicked 0 No clicks
- 18. 18 Documents Normalized and Discounted Cumulative Gain 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1 2 3 4 5 6 7 8 9 10 NDCG
- 19. 19 • Tree ensemble method • Handle sparse data • Handle missing values and various value types • Robust to outliers • Learn higher-order feature interactions • Invariant to feature scaling • Highly scalable and optimized open source implementation (XGBoost)
- 20. 20 Point-wise • Input: single documents / Output: class labels or scores • Classify each document as relevant or non-relevant. • Adjust w to reduce classification errors Pairwise ranking • Input: document pairs / Output: partial order preferences • Classify pairs of documents – D1 > D2? • Adjust w to reduce discordant pairs List-wise ranking • Input: document collections / ranked document list • Score permutations -- Is {D1,D2,…} > {D1’,D2’,…} ? • Adjust w to directly maximize ranking measure of interest (NDCG) Di Q Q DjDi > Q DjDi > Dk>
- 21. 21 Green = relevant Gray = not-relevant Blue arrows = boost for pair-wise loss function Red arrows = boost for list-wise loss function (a) is the perfect ranking; (b) is ranking with 10 pairwise errors; (c) is ranking with 8 pairwise errors
- 22. 22 • Relevance: User’s behavior signals • Ranking Metrics: NDCG • Machine Learning Algorithm: Gradient Tree Boosting • Ranking optimization: List-wise with NDCG metrics
- 23. 23 Index Indexer Query Documents Scoring model Scores Query Features Training data Learning to rank Re-ranking model Top-n ranked documents (n > 1M) Top-m re-ranked documents (m < 1k) On-line Off-line Relevance
- 25. 25 Search Query: “40inch tv” Regular text search Search with user’s signals and learning-to-rank models Not relevant Not relevant Not relevant
- 26. 26 Conversion Rate (Simulation) NDCG CTR Simulated Queries Relative gain 15.58% 7.50% 10,000 Depth / Estimators 5 / 500 3 / 500 10 / 500 3 / 500 NDCG 0.687 0.688 0.685 0.689 Relative gain 15.14% 15.41% 14.92% 15.58% Training time (56 cores) 2:45:48 1:20:57 35:25:44 1:58:07
- 28. 28 Bhaskar Mitra, Fernando Diaz, and Nick Craswell. 2017. Learning to Match using Local and Distributed Representations of Text for Web Search. In Proceedings of the 26th International Conference on World Wide Web (WWW '17).
- 29. 29 Bhaskar Mitra, Fernando Diaz, and Nick Craswell. 2017. Learning to Match using Local and Distributed Representations of Text for Web Search. In Proceedings of the 26th International Conference on World Wide Web (WWW '17).
- 30. 30 Bhaskar Mitra, Fernando Diaz, and Nick Craswell. 2017. Learning to Match using Local and Distributed Representations of Text for Web Search. In Proceedings of the 26th International Conference on World Wide Web (WWW '17).
- 31. 31 • Traditional IR methods do not scale to modern e-commerce needs • User’s implicit feedback is a proxy for search query / document pairs relevance • Learning-to-rank (LTR) methods scale to thousand of features and are robust to data noise • LTR with listwise-based loss function substantially improve search relevance (15.6% NDCG increase on e-commerce data) • NDCG improvements directly correlate to conversion rates (7.5% CTR increase on e-commerce data) • DNN methods for IR are starting to outperform traditional ML methods