内部推广优化

Rupesh Gupta, Guangde Chen, Shipeng Yu
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引用次数: 2

摘要

大多数大型互联网公司都会进行内部促销,以交叉推广不同的产品和/或教育成员如何从他们已经使用的产品中获得额外的价值。这反过来又推动了公司的粘性和/或收益。然而,由于这些内部促销活动可能会分散会员对产品或页面的注意力,因此显示这些内部促销活动会产生非零的蚕食损失。必须仔细权衡这种损失与展示内部晋升所带来的收益。如果不同的内部促销针对不同的目标进行优化,这可能是一个复杂的问题。在这种情况下,不仅很难比较通过内部推广获得的转化收益与显示该内部推广所造成的损失,而且很难比较通过不同的内部推广获得的转化收益。因此,我们需要一种原则性的方法来决定在每次为内部晋升服务的机会中为成员提供哪种内部晋升服务(如果有的话)。这种方法不仅要优化公司的净收益,还要优化会员的体验。在本文中,我们讨论了我们在LinkedIn内部促销的优化方法。特别地,我们给出了显示内部促销的成本效益分析,我们将内部促销优化作为约束优化问题的表述,解决优化问题和实时服务内部促销的系统架构,以及在线a /B测试的实验结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Internal Promotion Optimization
Most large Internet companies run internal promotions to cross-promote their different products and/or to educate members on how to obtain additional value from the products that they already use. This in turn drives engagement and/or revenue for the company. However, since these internal promotions can distract a member away from the product or page where these are shown, there is a non-zero cannibalization loss incurred for showing these internal promotions. This loss has to be carefully weighed against the gain from showing internal promotions. This can be a complex problem if different internal promotions optimize for different objectives. In that case, it is difficult to compare not just the gain from a conversion through an internal promotion against the loss incurred for showing that internal promotion, but also the gains from conversions through different internal promotions. Hence, we need a principled approach for deciding which internal promotion (if any) to serve to a member in each opportunity to serve an internal promotion. This approach should optimize not just for the net gain to the company, but also for the member's experience. In this paper, we discuss our approach for optimization of internal promotions at LinkedIn. In particular, we present a cost-benefit analysis of showing internal promotions, our formulation of internal promotion optimization as a constrained optimization problem, the architecture of the system for solving the optimization problem and serving internal promotions in real-time, and experimental results from online A/B tests.
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