Optimizing Similar Item Recommendations in a semi-structured marketplace to maximize conversion


  • Recall + Ranking
    • Recall: Candidate Generation
      • Coviewed items
        • A pair of items that have been frequently viewed together in the same browsing session by multiple users
        • Filter out covered from a different item category to ensure similarity.
      • Title similarity
        • Using ElasticSearch indexing scheme (TF/IDF) give higher quality than previous approaches Recommending Similar Items in Large-scale Online MarketplacesRecommending Similar Items in Large-scale Online Marketplaces

          Offline process, generates long-term cluster based on product’s title


          c1 = {nike, air-max, white, gray, running}

          Nike white gray 7 m...
          which used Locality Sensitive Hashing (LSH)
    • Ranking
      • Pointwise learning to rank problem
      • Binary classification,whether purchased (positive class) vs. non-clicked (negative class)
      • Sampling
        • KL divergence to get a quantitative measure of the overlap of the probability distributions for 2 classes
        • The non- clicked / purchased strategy is optimal for class separation. Reflect…
          • Non-clicked show absolutely no user interest
          • Purchased indicate complete user intention
  • Baseline = Linear model with manually adjusted weights based on domain exports


  • CTR +3.0%
  • PTR (Purchase-Through-Rate) +6.6%
  • Revenue +6.0%


Y. M. Brovman et al., “Optimizing Similar Item Recommendations in a Semi-structured Marketplace to Maximize Conversion,” in Proceedings of the 10th ACM Conference on Recommender Systems, Boston Massachusetts USA, Sep. 2016, pp. 199–202. doi: 10.1145/2959100.2959166.

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