As the recommendation systems behind commercial services scale up and apply more and more sophisticated machine learning models, it becomes important to optimize computational cost (capacity) and runtime latency, besides the traditional objective of user engagement. Caching recommended results and reusing them later is a common technique used to reduce capacity and latency. However, the standard caching approach negatively impacts user engagement. To overcome the challenge, this paper presents an approach to optimizing capacity, latency and engagement simultaneously. We propose a smart caching system including a lightweight adjuster model to refresh the cached ranking scores, achieving significant capacity savings without impacting ranking quality. To further optimize latency, we introduce a prefetching strategy which leverages the smart cache. Our production deployment on Facebook Marketplace demonstrates that the approach reduces capacity demand by 50% and p75 end-to-end latency by 35%. While Facebook Marketplace is used as a case study, the approach is applicable to other industrial recommendation systems as well.

Many consumer products are two-sided marketplaces, ranging from commerce products that connect buyers and sellers, such as Amazon, Alibaba, and Facebook Marketplace, to sharing-economy products that connect passengers to drivers or guests to hosts, like Uber and Airbnb. The search and recommender systems behind these products are typically optimized for objectives like click-through, purchase, or booking rates, which are mostly tied to the consumer side of the marketplace (namely buyers, passengers, or guests). For the long-term growth of these products, it is also crucial to consider the value to the providers (sellers, drivers, or hosts). However, optimizing ranking for such objectives is uncommon because it is challenging to measure the causal effect of ranking changes on providers. For instance, if we run a standard seller-side A/B test on Facebook Marketplace that exposes a small percentage of sellers, what we observe in the test would be significantly different from when the treatment is launched to all sellers. To overcome this challenge, we propose a counterfactual framework for seller-side A/B testing. The key idea is that items in the treatment group are ranked the same regardless of experiment exposure rate. Similarly, the items in the control are ranked where they would be if the status quo is applied to all sellers. Theoretically, we show that the framework satisfies the stable unit treatment value assumption since the experience that sellers receive is only affected by their own treatment and independent of the treatment of other sellers. Empirically, both seller-side and buyer-side online A/B tests are conducted on Facebook Marketplace to verify the framework.

Normalized Cut is a well-established divisive image segmentation method, which we adapt in this paper for the segmentation of laser point clouds in urban areas. Our focus is on polyhedral objects with planar surfaces. Due to its target function, Normalized Cut favours cuts with "short cut lines" or "small cut surfaces", which is a drawback for our application. We therefore modify the target function, weighting the similarity measures with distance-dependent weights. We call the induced minimization problem "Distance-weighted Cut" (DWCut). The new target function leads to a generalized eigenvalue problem, which is slightly more complicated than the corresponding problem for the Normalized Cut; on the other hand, the new target function is easier to interpret and avoids some drawbacks of the Normalized Cut. We point out an efficient method for the numerical solution of the eigenvalue problem which is based on a Krylov subspace method. DWCut can be beneficially combined with an aggregation in order to reduce the computational effort and to avoid shortcomings due to insufficient plane parameters. We present examples for the successful application of the Distance-weighted Cut principle and evaluate its results by comparison with the results of corresponding manual segmentations.