On component interactions in two-stage recommender systems

• URL: http://arxiv.org/abs/2106.14979v1
• Date: Mon, 28 Jun 2021 20:53:23 GMT
• Title: On component interactions in two-stage recommender systems
• Authors: Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus
• Abstract summary: Two-stage recommenders are used by many online platforms, including YouTube, LinkedIn, and Pinterest. We show that interactions between the ranker and the nominators substantially affect the overall performance. In particular, using a Mixture-of-Experts approach, we train the nominators to specialize on different subsets of the item pool.
• Score: 82.38014314502861
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Thanks to their scalability, two-stage recommenders are used by many of today's largest online platforms, including YouTube, LinkedIn, and Pinterest. These systems produce recommendations in two steps: (i) multiple nominators -- tuned for low prediction latency -- preselect a small subset of candidates from the whole item pool; (ii)~a slower but more accurate ranker further narrows down the nominated items, and serves to the user. Despite their popularity, the literature on two-stage recommenders is relatively scarce, and the algorithms are often treated as the sum of their parts. Such treatment presupposes that the two-stage performance is explained by the behavior of individual components if they were deployed independently. This is not the case: using synthetic and real-world data, we demonstrate that interactions between the ranker and the nominators substantially affect the overall performance. Motivated by these findings, we derive a generalization lower bound which shows that careful choice of each nominator's training set is sometimes the only difference between a poor and an optimal two-stage recommender. Since searching for a good choice manually is difficult, we learn one instead. In particular, using a Mixture-of-Experts approach, we train the nominators (experts) to specialize on different subsets of the item pool. This significantly improves performance.

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