品效合一的增長秘訣- 騰訊廣告KDD2022錄用論文分享之CONFLUX算法

1、品效合一的增長秘訣KDD2022錄用論文分享之CONFLUX算法3品牌廣告業(yè)務介紹預估詢量鎖量召回粗排精排媒體以預售的形式向廣告主保證目標定向的投放量（售賣），并按照合約完成 投放（執(zhí)行），下文品牌廣告和合約廣告交替使用離線-售賣階段在線-執(zhí)行階段廣告訂單預估：提前N天預估不同維度不同粒度的庫存(曝光)詢量：當廣告主有庫存需求時，提供最大的可用庫存鎖量：當廣告下單后，確保廣告的預訂庫存不被搶占召回：根據(jù)廣告請求的屬性和廣告狀態(tài)召回廣告粗排：進行廣告的初步排序和篩選，特殊廣告處理精排：品牌廣告通過虛擬出價和競價廣告競爭曝光機會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