品效合一的增长秘诀- 腾讯广告KDD2022录用论文分享之CONFLUX算法

1、品效合一的增长秘诀KDD2022录用论文分享之CONFLUX算法3品牌广告业务介绍预估询量锁量召回粗排精排媒体以预售的形式向广告主保证目标定向的投放量（售卖），并按照合约完成 投放（执行），下文品牌广告和合约广告交替使用离线-售卖阶段在线-执行阶段广告订单预估：提前N天预估不同维度不同粒度的库存(曝光)询量：当广告主有库存需求时，提供最大的可用库存锁量：当广告下单后，确保广告的预订库存不被抢占召回：根据广告请求的属性和广告状态召回广告粗排：进行广告的初步排序和筛选，特殊广告处理精排：品牌广告通过虚拟出价和竞价广告竞争曝光机会4CONFLUX: A Request-level Fusion Fr

2、amework for Impression Allocation via Cascade Distillation5SummaryR&D Target:A unified ranking framework for two different advertising markets: Guaranteed Delivery (GD) & Real-time Bidding (RTB) to boost revenue.Contributions:A framework serves at a request granularity based on a more precise modeli

3、ng of the non-stationary competitions.A multi-stage workflow named cascaded distillation to effectively produce an industrially applicable model.Extensive evaluation through industrial deployment on Tencent advertising system.Background and Challenges017Pain Point: Fully releasing the commercial val

4、ue of traffic inventoryMarket over $130 billionTraffic growth slowsChinas Internet users andInternet penetration rate reached1.032 billion and 73%, respectivelyAdvertising business volume grew less than 3% due to COVID Guaranteed Delivery Fixed price in bulkReal-time Bidding Floating price via aucti

5、onBackground - Advertising Market OverviewBackground - Guaranteed DeliveryArt of inventory:Desired volume and attributes of impressionsare promised at a fixed unit price via contracts in advanceThe publisher has an obligation to the contracts fulfillment and pays the penalty for any under-deliveryIm

6、pression allocation:A demand-supply problem described by a bipartite graphCompetition among contracts withoverlapping targeting8Targeting, industry, and inventory allocationsupplydemandindustryShanghai, male, 35Shanghai, female, 25Beijing, male, 20Shenzhen, female, 40sportscosmeticsluxuryGDTargeting

7、AllocationBipartite graphFeatures In AdvanceDifficulty PredictionBackground - Real-time BiddingBid for impression: RTB focuses on instant effects and allows advertisers to bid for each opportunity without guaranteeing the total volume.Cost-per-action: The selling price varies with auctions depending

8、 on advertisers valuation (e.g., possibility of click or buying). = .Platform income: The highest bid wins, and the charge for impression becomes the publishers revenue. Therefore, we only cares about in our problem.9The highest bid wins the gameBackground - Target ProblemProblem: Impression allocat

9、ion based on request-level featureArbitrage space: The selling price varies in RTB market while stay fixed in GD market.Impression quality: GD advertisers also pursue personalization and performanceComplex targeting: 63,901 user targeting and 4,254,119 request targeting are supported.Optimal Allocat

10、ion of Real-Time-Bidding and Direct Campaigns, ACM SIGKDD 2018The contract is viewed as a bidder, and the bid is given by optimization algorithms.The modelling is at an advertisement granularity.Impression Allocation and Policy Search in Display Advertising, IEEE ICDM 2021Each contract participates

11、in the auction,and the bids are given based on the primal-dual relaxation theorem.Online traffic and request attributes arevariable, and ad granularity is insufficient to capture the dynamic.ChallengesCONFLUX: Request-level allocation via cascade distillationUnsupervisedlearningComplex competitionSt

12、ringent delayOverall income maximizationOne by one decisionModeldegradation Between GD&RTBAmong GD adsOne million ads within millisecondsUnavoidable tradeoffDistribution shift of bid landscape and user trafficFormulation and Solution0213Solution - Ad-systemAdvertising funnel: A structure composed of

13、 retrieval, scoring, and reranking to handle a million-level ad corpus.Parallel server: Feature server stores all necessary attributes of and ad. Log server records the impressions along with eligible ads and bid prices.Module at confluence: CONFLUX aggregate both outputs and build a unified competi

14、tive stage to balance the gain and expense.RequestAdRetrievalScoringRerankingCorpusCONFLUX1 Million1010GD RTBAd FeatureUser Feature10 Feature ServerTrack Log Log ServerSolution - OverviewCONFLUX: Request-level allocation via cascade distillationParadigm generationCompetition modelingModel distillati

15、onReal-time logLinear programmingHistorical training samplesUser FieldContext FieldCandidate AdContract 1Contract 2Contract MBid ChargeconcatenateconcatenateconcatenateconcatenateconcatenateconcatenateThreat ScoringThreat ScoringThreat ScoringMicro-Env.SUM PoolingSUM PoolingMacro-Env.Concatenate & F

16、lattenconcatenate & FlattenconcatenateOnline calibration Weighted pooling within GD adsSum-pooling between GD and RTBFeature crossingModel compressionProblem decompositionParadigm dataflowPeriodic fine-tuningTemporal distillationSolution - Paradigm GenerationLinear programming: generate the paradigm

17、 of optimal allocation plan on historical log data.Labelled samples: contracts + 1 bid winner = training samples with as the label.Solution - Competition ModelingMicro-competition vector: the competition among overlapped contracts depends on the demand-supply conditions, thus a weighted pooling is a

18、dopted.Macro-competition vector: sum-pooling is used due to all contracts compete with the bid winner as a whole.Solution - Model DesignProblem decomposition: prediction of the probability that one contract is chosen is furtherdecomposed according to = .Thus, we train GD net and RTB net to predict t

19、he conditional probability and , which the product equals .Knowledge distillation: Teacher net adopts a complicated structure with feature crossing for better representation power. Its intermediate outputs are used to transfer such knowledge to a shallower student network.Solution - Model DesignOnli

20、ne calibration: periodical calibration of the student net is conducted via a much cheaper fine-tuning using the newly generated paradigm.Temporal distillation: model distillation between the target model and the model before fine-tuning to prevent the model from going too far and “misled” by the new

21、 samples. The life-cycle is set to 24 hr.Evaluation and Conclusion03Evaluation - PerformanceOffline evaluation:Three datasets: splash screen, pre-roll, and in- feed ads, 94.6 million impression from 1 week.Baselines: contract first, fixed parameter without calibration, ad-level modeling, and PID controller.Metric: accumulated income/theoretically optimal incomeOnline A/B test:Deployment on Tencent ad-system over half a year.Raise the advertising of the GD