EP3682403A1 - Procédé et système d'enchères adaptatives intelligentes dans un réseau d'échange en ligne automatisé - Google Patents

Procédé et système d'enchères adaptatives intelligentes dans un réseau d'échange en ligne automatisé

Info

Publication number
EP3682403A1
EP3682403A1 EP18769115.9A EP18769115A EP3682403A1 EP 3682403 A1 EP3682403 A1 EP 3682403A1 EP 18769115 A EP18769115 A EP 18769115A EP 3682403 A1 EP3682403 A1 EP 3682403A1
Authority
EP
European Patent Office
Prior art keywords
offer
level
bid
offers
user
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP18769115.9A
Other languages
German (de)
English (en)
Inventor
Rodrigo Acuna Agost
Alejandro Ricardo Mottini D'oliveira
David Renaudie
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Amadeus SAS
Original Assignee
Amadeus SAS
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from FR1758516A external-priority patent/FR3071086A1/fr
Priority claimed from US15/704,647 external-priority patent/US20190080363A1/en
Application filed by Amadeus SAS filed Critical Amadeus SAS
Publication of EP3682403A1 publication Critical patent/EP3682403A1/fr
Withdrawn legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0241—Advertisements
    • G06Q30/0273—Determination of fees for advertising
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0283—Price estimation or determination

Definitions

  • the present invention relates to automated bidding systems, and in particular to an intelligent, adaptive method and system for generating bid pricing based upon predictions of user interaction behaviour and a variable
  • Embodiments of the invention employ machine learning models for predicting behaviour of online users, and are able to automatically determine likelihood of user interaction with online content elements based upon aggregated behaviour of prior users in similar contexts.
  • the invention may be applied in online advertising systems, for example to determine a bid price for placement of an advertisement to be presented to a user, e.g. via a web page on within a mobile app.
  • Online (e.g. web-based, mobile, or in-app) advertising differs from advertising in traditional media in its degree of personalised audience targeting.
  • broadcast media advertising such as television advertising
  • online advertising aims to reach individuals having a particular interest in the product, service, or information that is presented.
  • Advertisers whose
  • advertisements appear on these websites may pay the operator on the basis of viewing opportunities or impressions (commonly measured as 'cost per thousand impressions', a.k.a. CPM), on the basis of a cost per click (CPC), or according to some other measure of performance.
  • CPM cost per thousand impressions'
  • CPC cost per click
  • the actual selection of an advertisement to be placed on a web page presented to an individual user may be based, at least in part, on a bidding process whereby an advertiser who is willing to pay a higher CPM, CPC, or other cost measure, is more likely to have its advertisement presented to the user.
  • an ad exchange is a technology platform that implements a digital marketplace allowing advertisers and publishers of web sites and other online content to buy and sell advertising space, often through real-time auctions.
  • Well-known ad exchange platforms include DoubleClickTM (owned by GoogleTM), AppNexusTM, MicrosoftTM Ad ExchangeTM, and OpenXTM.
  • An ad exchange platform maintains a 'pool' of ad slots. Publishers contribute their ad impressions, e.g. available advertising slots embedded within web pages served to users, into the pool. Buyers can then bid for the
  • each bid request received at a DSP from an ad exchange comprises ad-level information in relation to an available ad slot.
  • the ad-level information may include slot size (e.g. dimensions in pixels), the URL of the website, position of the slot on the web page, an identifying ad slot key, and so forth.
  • the bid request may also include context information, such as browser information, the type of user device, and so forth.
  • user-level information may be available, such as a cookie id from a previous visit, IP address, and so forth.
  • a typical DSP may receive several hundred million such requests per day. Accordingly, the DSP much be capable of handling thousands of bid requests per second.
  • the expected response from the DSP is a bid price, in a currency supported by the ad exchange, for each proposed ad slot. If the DSP is too slow to respond, or offers a low bid price, it may be beaten in the bidding by a competing DSP, and will therefore lose the opportunity to place an ad in the offered slot. On the other hand, if the DSP responds quickly with a high bid price, it may win the opportunity to place selected ad content within the offered slot. However, for the DSP to function successfully overall, the bid price must be reasonable, and the selected ad content must be well-targeted to the end user, in order to ensure a sufficiently high click through rate (CTR).
  • CTR click through rate
  • the total revenue generated by the DSP given by the sum of the CPC paid by advertisers for all clicked ads, will be less than the total operating cost, which includes the cost to the DSP operator of all successful bids.
  • an ad slot can potentially be populated by a number of distinct offers.
  • an ad slot comprises a
  • 'banner' consisting of a horizontally- or vertically-oriented rectangular region (depending upon layout within a web page), and distinct offers may be arranged in a grid layout within the slot. While the offers may all be related to a common user interest, each may have quite different characteristics. For example, in the context of travel-related advertising, different offers within an ad slot may relate to accommodation, dining, car rental, travel upgrades, and so forth. The CPC revenue generated from a user interaction (i.e. click) event may be different for each offer within the ad slot. However, a DSP is required to respond to a bid request with a corresponding bid price at the ad slot level.
  • the methods employed by the DSP be capable of computing an ad-level bid price based upon offer-level probabilities of user interaction.
  • the methods employed by the DSP be dynamically-configurable to vary a degree of 'aggressiveness' when computing ad-level bid price.
  • embodiments of the present invention are directed to addressing the above-mentioned desirable characteristics, i.e. computing offer- level probabilities of interaction and ad-level bid prices and implementing variable aggression, while also meeting technical requirements of speed and accuracy.
  • the invention provides a computer-implemented method comprising:
  • ad exchange server via a data communications network, a message comprising a bid request which includes site information and user information relating to an available ad slot; generating a ranked list of offers selected from an active offers database, wherein ranking of the offers is based at least in part on the site information and the user information;
  • ad-level bid price is based on at least the computed offer-level estimates of probability of user interaction, corresponding offer-level interaction revenues, and an aggressiveness parameter that controls aggressiveness of bid pricing
  • ad exchange server transmitting, to the ad exchange server via the data communications network, a message comprising a bid response including a bid-priced ad which comprises the combination of offers and the ad-level bid price.
  • embodiments of the invention are thereby able to compute ad-level bid pricing based upon offer-level information and estimates of probability of user interaction with individual offers.
  • CTR click through rate
  • the aggressiveness factor is variable between two limits.
  • a first limit may be a 'conservative' bidding limit, while a second limit may be an 'aggressive' bidding limit.
  • the 'conservative' bidding limit may be based upon a weighted average of estimated probability of user interaction, while the 'aggressive' bidding limit may be based upon an expectation that a user interacts with an offer having a highest combination of estimated probability of user interaction and offer-level interaction revenue.
  • the aggressiveness parameter limits may both be finite values, and in an exemplary embodiment the aggressiveness parameter is continuously-variable, e.g. between zero ('conservative' limit) and one ('aggressive' limit).
  • embodiments of the invention employ a machine learning model for computation of the offer-level estimates of probability of user interaction with each offer.
  • the machine learning model may be trained based upon matching of aggregated content placement events with aggregated user interaction events, and may be configured for efficient representation to enable rapid computation of the offer-level estimates of probability of user interaction with each offer, e.g. in under a few tens of milliseconds.
  • the machine learning model is continuously or periodically trained online, and the representation used for computation of the offer-level estimates of probability is periodically-updated to ensure that the estimates are based upon sufficiently current information.
  • the invention provides a computing apparatus which implements a demand side platform, the computing apparatus comprising:
  • the memory device contains a body of program instructions including instructions which, when executed by the processor, cause the computing apparatus to implement a method comprising steps of:
  • a message comprising a bid request which includes site information and user information relating to an available ad slot
  • ad exchange server transmitting, to the ad exchange server via the data communications interface, a message comprising a bid response including a bid-priced ad which comprises the combination of offers and the ad-level bid price.
  • the aggressiveness parameter may comprise a continuous numerical value a , for which the program instructions cause the computing apparatus to implement the step of computing the ad-level bid price BP based upon a formula:
  • P [Pi , Pi, ... , P n ] is a vector of the computed offer-level estimates of probability of user interaction
  • n is a number of offers to be included in the available ad slot
  • ⁇ ' denotes an element-wise product of vectors.
  • the aggressiveness parameter a may be varied over a continuous range, enabling substantially greater control over behaviour of the system between discrete aggressiveness setups such as have been employed previously.
  • the DSP is thereby able to select bidding behaviour using a smooth aggressiveness control method, rather than being constrained to specific categorical behaviours.
  • the offer-level interaction revenues comprise cost-per-click (CPC) values agreed between an operator of the demand side platform and respective advertisers of the offers selected from the active offers database.
  • CPC cost-per-click
  • the invention provides a computer program comprising program code instructions for executing the steps of the method according to the first aspect when said program is executed on a computer.
  • the program code instructions may, for example, be stored on tangible machine- readable media.
  • Figure 1 is a schematic diagram illustrating an exemplary networked system embodying the invention
  • Figure 2 shows a timeline of communications between a user device, a web server, and ad exchange server, and a DSP embodying the invention
  • Figure 3 is a block diagram illustrating schematically a number of code modules comprising an online user interaction prediction and ad-level bidding engine embodying the invention
  • Figure 4 shows a flowchart of a method of online updating of a machine learning model for online user interaction prediction
  • Figure 5 shows a flowchart of a method of feature engineering and model hyperparameter optimisation of the machine learning model
  • Figure 6 is a block diagram illustrating schematically a number of code components of the real-time bidding module shown in Figure 3
  • Figure 7 is a flowchart of a method of estimation of expected CTR using the machine learning model for online user interaction prediction
  • Figure 8 is a flowchart of a method of generating a bid response, including a bid price, according to an embodiment of the invention
  • Figure 9 is a flowchart of a method of generating a bid-priced ad comprising one or more offers according to an embodiment of the invention.
  • Figure 10 is a flowchart of a method of operating a real-time bidding module embodying the invention.
  • Figure 11 shows a chart illustrating performance of a real-time bidding module embodying the invention.
  • FIG. 1 is a block diagram illustrating an exemplary networked system 100 including a demand side platform (DSP) server 102, which is configured to implement a method of bidding for placement of advertising content in DSP.
  • DSP demand side platform
  • the DSP server 102 may comprise a computer system having a conventional architecture.
  • the DSP server 102 comprises a processor 104.
  • the processor 104 is operably associated with a non-volatile memory/storage device 106, e.g. via one or more data/address busses 108 as shown.
  • the non-volatile storage 106 may be a hard disk drive, and/or may include a solid-state non-volatile memory, such as ROM, flash memory, solid-state drive (SSD), or the like.
  • the processor 104 is also interfaced to volatile storage 110, such as RAM, which contains program instructions and transient data relating to the operation of the DSP server 102.
  • the storage device 106 maintains known program and data content relevant to the normal operation of the DSP server 102.
  • the storage device 106 may contain operating system programs and data, as well as other executable application software necessary for the intended functions of the authentication server 102.
  • the storage device 106 also contains program instructions which, when executed by the processor 104, cause the DSP server 102 to perform operations relating to an embodiment of the present invention, such as are described in greater detail below, and with reference to Figures 2 and 6-10 in particular. In operation, instructions and data held on the storage device 106 are transferred to volatile memory 110 for execution on demand.
  • the processor 104 is also operably associated with a communications interface 112 in a conventional manner.
  • the communications interface 112 facilitates access to a wide-area data communications network, such as the Internet 116.
  • the volatile storage 110 contains a corresponding body 114 of program instructions transferred from the storage device 106 and configured to perform processing and other operations embodying features of the present invention.
  • the program instructions 114 comprise a specific technical
  • DSP server 102 and other processing systems and devices described in this specification, terms such as 'processor', 'computer', and so forth, unless otherwise required by the context, should be understood as referring to a range of possible implementations of devices, apparatus and systems comprising a combination of hardware and software.
  • Physical processors may include general purpose CPUs, digital signal processors, graphics processing units (GPUs), and/or other hardware devices suitable for efficient execution of required programs and algorithms.
  • Computing systems may include conventional personal computer architectures, or other general-purpose hardware platforms.
  • Software may include open-source and/or commercially-available operating system software in combination with various application and service programs.
  • computing or processing platforms may comprise custom hardware and/or software architectures.
  • computing and processing systems may comprise cloud computing platforms, enabling physical hardware resources to be allocated dynamically in response to service demands. While all of these variations fall within the scope of the present invention, for ease of explanation and understanding the exemplary embodiments described herein are based upon single-processor general-purpose computing platforms, commonly available operating system platforms, and/or widely available consumer products, such as desktop PCs, notebook or laptop PCs, smartphones, tablet computers, and so forth.
  • processing unit' is used in this specification (including the claims) to refer to any suitable combination of hardware and software configured to perform a particular defined task, such as accessing and processing offline or online data, executing training steps of a machine learning model, or executing prediction steps of a machine learning model.
  • a processing unit may comprise an executable code module executing at a single location on a single processing device, or may comprise cooperating executable code modules executing in multiple locations and/or on multiple processing devices.
  • classification and bid pricing/decision processing may be performed entirely by code executing on DSP server 102, while in other embodiments corresponding processing may be performed is a distributed manner over a plurality of DSP servers.
  • Software components e.g. program instructions 114, embodying features of the invention may be developed using any suitable programming language, development environment, or combinations of languages and development environments, as will be familiar to persons skilled in the art of software engineering.
  • suitable software may be developed using the C programming language, the Java programming language, the C++ programming language, the Go programming language, and/or a range of languages suitable for implementation of network or web-based services, such as JavaScript, HTML, PHP, ASP, JSP, Ruby, Python, Perl, and so forth. These examples are not intended to be limiting, and it will be appreciated that convenient languages or development systems may be employed, in accordance with system requirements.
  • the system 100 further comprises additional DSP servers, e.g. 118, 120 that, in use, compete with DSP server 102 to bid for placement of advertising content within online ad slots offered via an ad exchange server 122.
  • the ad exchange server 122 implements a digital marketplace allowing advertisers and publishers of web sites and other online content to buy and sell advertising space in the form of a real-time, online auction in which each DSP server 102, 118, 120 is an automated, high-speed, bidder.
  • the ad exchange server 122 comprises a database 124 in which it maintains details of online content providers (web servers) and advertisers (DSPs) for the purpose of operating a digital advertising marketplace.
  • the system 100 further includes user terminal devices, exemplified by terminal device 126.
  • the terminal devices 126 may be, for example, desktop or portable PCs, smartphones, tablets, or other personal computing devices, and each comprise a processor 128 interfaced, e.g. via address/data bus 130, with volatile storage 132, non-volatile storage 134, and at least one data
  • the volatile storage 132 contains program instructions and transient data relating to the operation of the terminal device 126.
  • the terminal device storage 132, 134 may contain program and data content relevant to the normal operation of the device 126. This may include operating system programs and data (e.g. associated with a Windows, Android, iOS, MacOS, Linux, or other operating system), as well as other executable application software generally unrelated to the present invention.
  • the storage 132 also includes program instructions 138 which, when executed by the processor 128 enable the terminal device to provide a user with access to online content. While many applications are known for providing such access, for simplicity in the present description it is assumed that the program instructions 138 implement a web browser having a graphical user interface (GUI) presented via the user I/O interface 140.
  • GUI graphical user interface
  • a corresponding web page display 144 is generated via the device Ul 140.
  • the display 144 include website content 146, and one or more advertising slots, e.g. 148, 150.
  • each advertising slot 148, 150 may comprise a plurality of specific Offers' on behalf of an advertiser. These offers are commonly arranged in a grid layout, e.g. as indicated by dashed rectangles 148a, 148b, 148c, 150a, 150b, 150c in Figure 1.
  • a number of communications steps then take place in order to populate these slots, i.e. to provide online advertisers with ad impressions within the web page display 144. These communications steps will now be described with reference to the timeline 200 illustrated in Figure 2.
  • the user terminal 126 via the executing web browser application 138 and responsive to user input, transmits 202 an HTTP request to the web server 142 which includes a URL of desired web content.
  • the web server 142 responds by transmitting 204 content, e.g. a web page in HTML format, to the user device 126.
  • content e.g. a web page in HTML format
  • the complete population and rendering of web page display 144 may require multiple requests and responses, and may involve further transactions with the web server 142 and/or with other online servers, such as content distribution network (CDN) servers and other web servers providing embedded content.
  • CDN content distribution network
  • the web page transmitted by the web server 142 to the user device 126 typically includes a hypertext reference ('href) directing the browser 138 to retrieve content from the ad exchange server 122 in accordance with an application programming interface (API) defined and provided by the relevant operator of the server 122.
  • the user device 126 transmits 208 an HTTP request to the ad exchange server 122.
  • the request includes web site information and user information relating to the user of the terminal device 126.
  • Available user information may include information that the web server 142 has gathered, and may include client-side information, such as device and browser identity and technical details, identifying information and contents of browser cookies, and the like.
  • the ad exchange server 122 receives the request, identifies relevant DSP servers 102, 118, 120 in its database 124, and transmits 210 bid request messages to each selected DSP server.
  • One such bid request message including site and user information, is received at DSP server 102 embodying the present invention, which executes a process 212 in accordance with its specific programming 114 in order to predict a likelihood of user interaction with a selected ad including one or more offers, placed within one or more of the available slots 148, 150, and arrive at a bid decision.
  • the DSP server 102 transmits 214 the bid to the ad exchange server 122.
  • the ad exchange server 122 receives all bids transmitted from DSP servers, including server 102, and selects a winning bid. It then retrieves ad content corresponding with the winning bid from its database 124, and transmits 216 the ad content to the user device 126 for rendering within the corresponding ad slot, e.g. 148 or 150.
  • This decision must be made with limited user information, and in view of the fact that a bad decision may have significant consequences for the advertiser. For example, if the DSP server wrongly determines that the user is a desirable target for a particular ad (i.e. computes a 'false positive'), it may place a relatively high winning bid and incur a real cost with little or no prospect of any return. Conversely, if the DSP server wrongly determines that the user is not a desirable target for the ad (i.e. computes a 'false negative'), it may place no bid, or a low losing bid, and cause the advertiser to miss an opportunity to obtain an impression with a real prospect of a return.
  • embodiments of the present invention may employ a machine learning approach.
  • the system 100 further includes a machine learning server ('ML server') 152, which is configured to process raw data relating to placement of content (i.e. ads/offers) along with user interactions (i.e. user clicks on ads/offers), to generate training data sets for a machine learning model, and to train the machine learning model for deployment to the DSP server 102.
  • 'ML server' machine learning server
  • the processing, training and deployment steps are described in greater detail below, with reference to Figures 3 and 4, and may be carried out continuously, periodically and/or on-demand in order to maintain currency of the machine learning model.
  • the ML server 152 may comprise a computer system having a conventional architecture, e.g. comprising a processor 154 that is operably associated with a non-volatile memory/storage device 156, via one or more data/address busses 158 as shown.
  • the processor 154 is also interfaced to volatile storage 160 which contains program instructions and transient data relating to the operation of the ML server 152.
  • the storage device 156 contains operating system programs and data, as well as other executable application software necessary for the intended functions of the ML server 152, and including program instructions which, when executed by the processor 154, cause the ML server 152 to perform operations described in greater detail below, with reference to Figures 3 and 4 in particular.
  • instructions and data held on the storage device 156 are transferred to volatile memory 150 for execution on demand.
  • the processor 154 is operably associated with a communications interface 162 in a conventional manner, providing access to the Internet 116.
  • the volatile storage 160 contains a corresponding body 164 of program instructions transferred from the storage device 156 and configured to perform processing, training and deployment steps for a machine learning model.
  • the program instructions 164 comprise a further specific technical contribution to the art in accordance with this embodiment.
  • the system 100 further includes at least one database 166, which is configured to store raw historical data relating to placement of content (i.e.
  • a log of data for a single day comprises on the order of 20 million lines (i.e.
  • the database 166 is preferably implemented using
  • the database 166 is accessible to both the DSP server 102 and the ML server 152.
  • logical access is illustrated by corresponding arrows.
  • physical access between the database 166 and the DSP and ML servers 102, 152 may be via the Internet 116, and/or via other dedicated communications links or networks, such as a local storage area network (SAN).
  • the DSP server 102 is configured to update the database 166, in real time, with raw data relating to placement and interaction events.
  • the ML server 152 is configured to retrieve the raw data from the database 166 and to carry out processing, training and deployment steps, based on the retrieved data.
  • FIG. 3 is a block diagram illustrating schematically a number of code modules that together comprise an online user interaction prediction and real-time bidding engine 300 embodying the invention.
  • Implementation of the user interaction prediction and bidding engine 300 is distributed across the ML server 152 and DSP server 102, as shown by the dashed boxes in Figure 3.
  • Three code modules make up the ML server component of the engine 300, namely a matching module 302, a feature enrichment module 304 and a machine learning module 306. These three modules are all implemented within the program instructions 164 executing on the ML server 152. The functionality implemented within each of these modules will now be described in greater detail.
  • the purpose of the matching module 302 is to match placement events (i.e. display of ads, and offers within ads, in ad slots 148, 150 of the display 144 of the user device 126) to subsequent interaction events (i.e. instances of a user clicking on an offer within an ad placed on the display 144 of the user device 126).
  • Matching enables placement events to be tagged as 'clicked' or 'not clicked', so that they can be used by machine learning module 306 in training of a supervised machine learning model for prediction of user interaction events based upon placement event data. Additionally, matching enables placement event data to be combined with corresponding interaction event data to create a record for clicked ads containing all available information regarding placement and interaction.
  • Matching presents a challenge because there is no explicit link between a placement event (ad impression) and a subsequent user interaction (ad click).
  • a user interaction may occur at any time following placement, e.g. following a substantial delay. Since new placement and/or interaction events may occur at a very high rate (e.g. hundreds or thousands of times per second) in a live system, corresponding placement and interaction events may become widely separated in the database 166. Additionally, the rate of interaction events may be very low, e.g. it is generally reported that the click through rate (CTR) for web-based display advertising is on the order of 0.05%. Furthermore, it is desirable to link placement and interaction events at offer level, rather than only at ad level.
  • CTR click through rate
  • the general approach employed for matching is to identify, in the database 166, placement events and subsequent interaction events within a predetermined time window that have a selected set of matching parameters.
  • the time window should be of sufficient duration to capture a substantial majority of all interactions, and the number and choice of parameters should be sufficient to ensure unique matching in a substantial majority of cases. Perfect matching may be difficult to achieve, because it is impossible to know if or when an interaction will occur.
  • a time window of longer duration will capture interactions that occur after longer delays, but will also increase the risk of erroneous matching where, for example, a user interacts with a subsequently-presented ad having similar parameters.
  • the risk of erroneous matching can be reduced by using a larger selected set of parameters to distinguish between presented ads, at the expense of making the matching process more complex.
  • an embodiment of the invention has been implemented in the context of a domain-specific DSP server operating on behalf of advertisers, using event data captured from a live system.
  • a heuristic approach was taken to design of the matching module, with a number of experiments being conducted to determine a suitable time window, and a selected set of parameters.
  • An 80 second time window was found to be effective in combination with matching the following event parameters:
  • publisher identifier i.e. the ad exchange/distribution network through which the ad was placed
  • user segment a combination of a user product segment, based upon a product such as flight, hotel or restaurant previously viewed by the user, and a user time segment, indicating how long it has been since the last activity of the user
  • a measure of distance between a destination (location) about which the user was seeking information and a destination that was the subject of a specific offer • ad slot key (a stable identifier for the combination of publisher, ad slot and page).
  • matching is performed using an Impala SQL query to select and join tables of records of placement and interaction events on the values of fields corresponding with the parameters listed above.
  • placement records are LEFT JOINed to interaction records, such that the resulting table includes a row for each placement event.
  • Each row comprises a set of values of raw features derived from the matched events, along with an indicator of whether or not an interaction event, i.e. ad/offer click, occurred.
  • the table of matched data is input to the feature enrichment module 304.
  • the function of the feature enrichment module 304 is to derive, from the values of raw features in the matched data table generated by the matching module 302, a corresponding set of enriched feature vectors for use by the machine learning module 306.
  • a process for determining a suitable set of enriched features i.e. feature engineering
  • definitions of enriched features for use by the feature enrichment module 304 are shown as being stored in a file 310 within data store 308, however this may be regarded as a schematic convenience. In a practical configuration, feature definitions may be stored in this way, may be compiled into a code module and linked to the feature enrichment module 304, or may be hard-coded into the feature enrichment module.
  • each of these implementation options (and others that will be apparent to persons skilled in the art) potentially offers a different trade-off between flexibility, code complexity and execution speed.
  • all of the enriched features are of categorical type (i.e. take on one of a number of discrete values), and are one-hot encoded.
  • the resulting feature vectors are therefore generally relatively sparse, and comprise binary elements.
  • each feature vector corresponds with an offer within an ad presented to a user, and is associated with a binary tag indicating whether or not the user interacted with (i.e. clicked on) the offer.
  • the resulting table of feature vectors and tags is input to the machine learning module 306.
  • the machine learning module 306 comprises program code executing on the ML server 152, and configured in the exemplary experimental
  • the machine learning module 306 of the exemplary configuration implements a regularised logistic regression algorithm, with 'follow-the-regularised-leader' (FTRL)-proximal learning.
  • this machine learning algorithm is known to be effective in the case of highly unbalanced datasets (noting that only around 0.05% of samples in the table of feature vectors are tagged as 'clicked'). Further details of the algorithm, and its application to click-prediction, can be found in H. Brendan McMahan et al, 'Ad Click Prediction: a View from the Trenches', KDD'13, August 11-14, 2013, Chicago, Illinois, USA.
  • the algorithm has a number of hyperparameters that can be adjusted in order to optimise its learning accuracy on the training data for a specific problem.
  • a process for determining a suitable set of values for the hyperparameters is described in detail below with reference to Figure 5.
  • fixed values of the hyperparameters for use by the machine learning module 306 are shown as being stored in a file 312 within data store 308.
  • alternative implementations are possible, such as hard-coding the parameters into the machine learning module 306.
  • Execution of the machine learning module 306 on a particular dataset results in the generation of a model that can be executed by the DSP server 102, as will be described in greater detail below with reference to Figure 7.
  • a logistic regression model is wholly characterised by a set of coefficients associated with elements of the input feature vector.
  • a particularly efficient representation of the model is employed, to enable the DSP server 102 to compute a prediction of the likelihood of a user interaction very rapidly, i.e. well within the 30 ms target window 220 for generating a bid decision.
  • the coefficients are stored as a dictionary in which each entry is defined by a key and a value.
  • the key is a hashed representation of a concatenation of the feature name (i.e. column label in the feature table) and a corresponding feature value (i.e. categorical values prior to one-hot coding).
  • the associated value in the dictionary is simply the
  • This type of data structure is known to provide extremely fast lookup, particularly for sparse feature sets.
  • a limit on the number of hashed features may be imposed (a scheme sometimes referred to as the 'hashing trick').
  • This scheme can be used to greatly speed lookup and computation, at the expense of possible collisions in dictionary key values.
  • the statistical effect of these collisions can be neglected from the perspective of overall performance of the algorithm.
  • the model data structure is serialised in a binary format (in the exemplary configuration the Python 'pickle' format is used), and stored in a model file 314 in data store 308.
  • the ML server 152 executes the modules 302, 304, 306 repeatedly, e.g. continuously, periodically, or on-demand. This is illustrated by the flowchart 400 shown in Figure 4.
  • Raw data is retrieved from the database 166 at step 402.
  • a predetermined period of recent data may be used, which is considered to be representative of the behaviour of current online users of the system 100. For example, raw data from the most recent one-month period may be employed.
  • the matching module 302 performs matching of placement and interaction events, as has been described. In practice, retrieval 402 and matching 404 steps may be combined as a single query, e.g. an Impala SQL query.
  • the ML server 152 executes the feature enrichment module, which uses the enriched feature definitions 310 to compute enriched feature vectors corresponding with the matched data. These are transferred to the machine learning module 306 which trains the model using the tagged feature vectors and the predetermined hyperparameters defined in the configuration file 312. The resulting model coefficients are hashed, serialised and published 410 to the model file 314. [0060] Optionally, the ML server then waits 412, before recommencing the process at step 402. Exit from the wait condition 412 may be triggered by a number of different events. For example, the ML server may be configured to run the modules 302, 304, 306 periodically, e.g. once per day.
  • the ML server may run the modules 302, 304, 306 on-demand, e.g. upon receipt of a signal from a controller (not shown) within the system 100.
  • the ML server may run the modules 302, 304, 306 continuously, thereby updating the model file 314 as frequently as possible based upon the time required for data matching, feature enrichment and model training.
  • updates based upon 30-minute batches of data provided a suitable trade-off between quality of the output of the matching module 302 (i.e. the need to reconcile interaction and placement events accurately for a good training dataset), and reactivity to the real-time changes in the ad exchange network (e.g. new campaign launches, entry/exit of competitors, changes in user demand for some contents, and so forth).
  • FIG. 5 there is shown a flowchart 500 of a process of feature engineering and model hyperparameter optimisation.
  • the process 500 is partially automated, and operated under human supervision.
  • the development of suitable features with strong predictive capability, and the selection of appropriate ranges of model hyperparameters involves significant experience, judgment, creativity and ingenuity, and in most cases cannot efficiently be fully-automated.
  • the process 500 requires a set of test data, which is retrieved at step 502, and which may be obtained in the same manner as described above in relation to the functionality of the matching module 302.
  • data may be extracted from the database 166 for a selected test period using an Impala SQL query of the same form as that used by the matching module 302.
  • a set of enriched features is defined and configured. This step typically involves application of judgment, creativity and ingenuity of an experienced data scientist. In practice, a number of experiments have been performed, according to the process 500 and supported by further analysis of the test data set, in order to identify an effective set of enriched features. At step 506, values of the defined enriched features are computed from the raw test data set.
  • a set of hyperparameter values is selected and a machine learning model is configured with the selected values.
  • the resulting model is trained using the enriched test data. Typically, a portion of the test data is held back in the training step 510, which is then used in a cross-validation step 512 to assess the performance of the trained model on data that was not seen during the training step 510.
  • Performance of the trained model is then assessed at decision step 514, to determine whether or not it is acceptable, for example by reaching some optimal or sufficient level of performance.
  • the choice of criteria for assessing performance may be important to identifying an acceptable model.
  • Various known criteria may be employed, such as Area Under the Receiver Operating Curve (AUROC), log loss, or Gini (which is related to the AUROC).
  • AUROC Area Under the Receiver Operating Curve
  • Gini which is related to the AUROC.
  • a combination of Gini which takes values between -1 and 1 , and is desirably as high as possible
  • log loss which is desirably as low as possible
  • a further decision 516 is made as to whether to update the model hyperparameters.
  • the resulting loop of configuring hyperparameters, training and testing the model is typically automated using an algorithm such as grid search, or similar.
  • the role of the supervising data scientist in this case is to determine suitable ranges for the grid of hyperparameters.
  • an outer loop implemented via decision 518, allows for the testing of alternative sets of enriched features. If available selections and values of model algorithms, hyperparameters and enriched features have been exhausted without identifying an acceptable model, then the process 500 may be regarded as having failed, and a reconsideration of strategy may be required. For the purposes of the exemplary configuration, however, the process 500 led to a model with
  • step 520 therefore, the identified enriched feature definitions and model hyperparameters are written to the data files 310, 312 in the data store 308.
  • a summary of the enriched features developed via the process 500 is presented in Table 1.
  • Figure 6 is a block diagram 600 illustrating schematically a number of code components which comprise the real-time bidding module 316 described above with reference to Figure 3.
  • code components which collectively comprise a technical contribution to the art specifically developed for the presently-described embodiment of the invention, is implemented within the program instructions 114 executing on the DSP server 102. Details of the algorithms implemented by the code components illustrated in Figure 6 are described below with particular reference to the flowcharts shown in Figures 7 to 10, while the advantageous technical effects achieved by an exemplary embodiment are illustrated in the chart of Figure 11.
  • Input to the real-time bidding module 316 includes bid requests 210 received from the ad exchange server 122.
  • An offer-level selection and ranking component 602 employs user information from an active users database 604, offer information from an active offers database 606 and, optionally, estimated offer-level CTR generated by a machine learning CTR estimator component 608, in order to generate a ranked set of offers 610 for possible inclusion in an ad to be generated in response to a bid request 210. Operation of the offer-level selection and ranking component 602 is described in greater detail below with reference to Figure 8.
  • the ranked offers 610 are passed to an ad-level bid-price computation component 612, which employs the machine learning CTR estimator component 608 in order to generate a bid-priced ad. Operation of the ad-level bid-price computation component 612 is described in greater detail below, with particular reference to Figures 8 and 9.
  • the bid-priced ad may be used to generate a bid response 214.
  • FIG. 7 is a flowchart 700 of a method of estimation of expected CTR by the CTR estimation component 608, using the machine learning model for online user interaction prediction described above with reference to Figures 3 to 5.
  • site, offer and user information is received, i.e. via transmission 210 from the ad exchange server 122, in conjunction with information retrieved from the active offers database 606 and any available information retrieved from the active users database 604. This information is used at step 704 to compute a corresponding enriched feature vector according to the definitions 310.
  • the real-time bidding module accesses the model representation 314 which, as has been described, comprises a set of coefficients stored in a highly efficient dictionary structure for rapid coefficient lookup.
  • the model may be updated from time-to-time by the ML server 152.
  • the model representation 314 may be stored in a shared storage medium 308, and be asynchronously readable by the DSP server 102.
  • the DSP server may maintain a cached copy of the model representation 314 for rapid access, which is updated upon update of the stored file by the ML server 152.
  • the output of the model, generated at step 708, is an estimate of likelihood of user interaction with the offer, based on the enriched feature vector.
  • the output is a value representing a probability that the user will click on the offer.
  • the model may equivalently be regarded as providing an estimated offer-level CTR, i.e. for a large ensemble of identical, independent users to which an offer is presented, the CTR is equal to the probability that each individual user will click on the offer.
  • the terms probability of interaction and CTR are used interchangeably.
  • FIG. 8 is a flowchart 800 of a method of generating a bid response, including a bid price, by the real-time bidding module 316.
  • the flowchart 800 shows the steps implemented by the offer-level selection and ranking component 602, and the high-level steps implemented by the ad-level bid-price computation component 612. Details of a bid-price computation algorithm implemented by the ad-level bid-price computation component 612 are set out below, with reference to Figure 9.
  • a bid request 210 is received.
  • the offer-level selection and ranking component 602 executes one or more procedures to select and rank offers for possible inclusion within an ad generated in response to the bid request 210.
  • the significance of the offer-level selection and ranking component 602 is that it produces a ranked listing of offers selected from those available in the active offers database 606. Any suitable methods for doing so may be employed. Nonetheless, to assist in understanding of the invention, exemplary methods of offer-level selection and ranking are now outlined. As has been noted, the exemplary embodiment is implemented in the context of travel booking and related services, however the principles described may be applied to other contexts and subject matter.
  • an ad may be dynamically built by the DSP 102, and may comprise up to n different offers.
  • the maximum number of offers that may be placed can be greater or less than three.
  • n 4.
  • the maximum number of offers may be larger where more space is available, such as where large banners are provided on a site.
  • a number of offers, up to the maximum n, and the contents of each offer, are preferably selected in order to optimise the purchased space on the display 144, and increase a probability that the user interacts with (i.e. clicks on) at least one of the offers.
  • the exemplary offer-level selection and ranking module 602 is configured by specific programming to select and rank, from active offers within the database 606, a set of candidate offers ⁇ , O2, On to fill the available ad slot in the bid request 210.
  • This step is mainly driven by domain-specific heuristics (i.e. for the travel domain, in the exemplary embodiment), designed based upon input from domain experts.
  • the heuristics may include matching between attributes including characteristics derived from the request (e.g. website URL, a user travel destination derived from user search terms, a user origin location derived from an IP address of the user device 126, and so forth), and characteristics of offers present in the active offers database 606 (destination of the offer, price, type of product, and so forth).
  • characteristics derived from the request e.g. website URL, a user travel destination derived from user search terms, a user origin location derived from an IP address of the user device 126, and so forth
  • characteristics of offers present in the active offers database 606 destination of the offer, price, type of product, and so forth.
  • campaigns may also be applied, such as campaign activity begin and end dates, remaining budget, and so forth.
  • Matching heuristics may be implemented using suitable filters.
  • a first set of filters is applied using business rules in order to determine a first set of eligible offers.
  • the object of these filters is to eliminate past campaign material and/or offers that may be inactive or unavailable for some other business reason (e.g. offer expired, or budget exhausted).
  • a second set of travel-domain-specific filters is then employed for geographical matching between the destination of interest for the user, and the destinations associated with available offers.
  • Hierarchical filtering may be employed, to support matching at greater and/or lesser degrees of specificity. For example, if a user's search terms indicate an interest in Antonio as a destination, but there are no active offers specific to this destination, filters for 'Spain' may be applied, or even filters for 'Europe' if there are no active offers specific to 'Spain'.
  • offers matching characteristics of the request are then selected among the system-specified maximum n, to avoid over-computation costs both in CPU and time, and ordered by decreasing matching quality.
  • a larger number m > n offers may be selected.
  • the ad-level bid-price computation component 612 may be required to evaluate all possible choices of n offers among m, e.g. according to the method described below with reference to Figure 9.
  • This embodiment has the advantage of extending the search domain for optimising bid price, thus allowing for discovery of potentially more effective offer combinations.
  • a limitation, however, is that this approach increases the computing time, and it is therefore important to ensure availability of sufficient processing power, since required response time is short.
  • a ranking of selected offers 610 is thereby generated, and made available to the ad-level bid-price computation component 612 which, at step 806, computes an ad-level bid price using aggressiveness-factor parameters 808, to produce a bid-priced ad 810, for use in generating 812 a bid response 214.
  • Figure 9 is a flowchart 900 showing details of the ad-level bid price computation 806 using one or more aggressiveness-factor parameters 808.
  • the ad-level bid price computation component 612 combines offer-related attributes with current bid request 210 user and context information, and executes the CTR estimation component 608 to compute a probability of click for each offer O, generated by the offer-level selection and ranking component 602, according to the process 900.
  • user and offer attributes are retrieved.
  • user-related information is retrieved from the active users database, based on one-to-one exact matching (e.g. using user cookies), or on other matching of user characteristics where one- to-one matching is not possible (e.g. because the user has not been previously encountered).
  • offer-related information e.g. destination of the offer, price, type of product, and so forth
  • the ERPO corresponds to the average expected gain per offer to be shown in the ad slot, for each offer / ' .
  • This vector comprises
  • weights being the respective revenue per offer, which can be computed as—Y ERPO j ;
  • a convenient way to define the full range of bidding aggression may be derived by first defining the p-norm of the ERPO:
  • a is an aggressiveness-factor parameter 808 for which:
  • a simple bid-price multiplier may be applied to the BP value computed above, in order to convert the value to an actual bid price in a currency supported by the ad exchange server 122.
  • a price cap may also be applied to limit the actual bid price in case of obviously outlying values of click probability and/or bid prices, and to avoid excessive DSP expenditure on individual bids.
  • step 912 the final bid-priced ad 810 is produced, which may be employed in the generation 812 of a bid response 214.
  • FIG 10 is a flowchart 1000 of a method of overall operation of the real-time bidding module 316 embodying the invention, including post-bid processing to support online operation of the ML server 152.
  • a bid request is received, while at step 1004 a bid response is determined.
  • a decision may be made on whether or not to transmit a bid response for the ad slot presented in the bid request 210. For example, if the computed bid price is unduly high (e.g. exceeds a cap price, or available budget constraint) or low (e.g. reflects a low probability of success and/or revenue generation) a decision may be made not to transmit the bid response.
  • a decision may be made to bid for the slot.
  • control passes to step 1008 wherein the bid response is transmitted 214 back to the ad exchange server 122.
  • control is directed 1010 to step 1012, in which the database 166 is updated with details of the placement event.
  • FIG. 11 shows a chart 1100 illustrating performance of the experimental real-time bidding module embodying the invention.
  • the chart 1100 shows click through rate (CTR) on the vertical axis 1102, with the corresponding performance of ten bidding modules shown as a series of bars.
  • a real-time bidding module embodying the invention is programmed to carry out technical steps, in response to a bid request message received from an ad exchange server, of performing domain-specific filtering of database records to select and rank offers, and computing a corresponding ad-level bid price based upon offer- level estimates of CTR, associated revenue values, and aggressiveness factor parameters.
  • an algorithm is employed that enables continuous control of bidding aggressiveness between extremes of 'conservative' bidding (based upon a weighted average of estimated offer CTR) and 'aggressive' bidding (based upon expectation that a user interacts with an offer having the highest
  • the predictions of offer-level interactions are determined using a machine learning model trained using data derived from a database of placement and interaction events. Further technical steps implemented by such embodiments include matching of events to generate combined placement/interaction records that are tagged for use by supervised learning algorithms, calculation of enriched feature vectors for online learning, and training of a machine learning model based upon continuously updating event data to maintain a current and periodically-updating model representation having an efficient format usable by the real-time bidding module to make rapid decisions, e.g. in under 30 ms.
  • ts_day_of_week The day of the week (Sun-Sat) of the placement event.
  • ts_hour_of_day The hour of the day (00-23) of the placement event.
  • publisherj ' d Identifier of publisher i.e. operator of ad exchange server.
  • advertiserjd Identifier of advertiser i.e. operator of ad exchange server.
  • offer_key A unique offer identifier, created by combining advertiserjd
  • ad_dst_top199 A destination associated with an offer. Limited to the top 199 destinations, which were found in feature engineering experiments to capture 92% of all clicks.
  • nb_offers__per_ad Number of offers included with the ad slot nb_offers__per_ad Number of offers included with the ad slot.
  • mq_dst Proximity/distance of destination of interest to the user and destination associated with an offer A categorical value indicating closeness of match on a set scale.
  • user_pseg Identifier of a product segment previously viewed by user e.g.
  • user_tseg Identifier of a time segment of the user's previous activity e.g.
  • domain_name_top99 Domain name of site in which ad slot is displayed Limited to the top 99 domains, which were found in feature engineering experiments to capture 95% of all clicks.
  • fmt_device An engineered feature comprising a combination of offer
  • ad_slot_key_top499 A unique identifier for the combination of publisher, ad slot, and page. Limited to the top 499 values, which were found in feature engineering experiments to capture 97% of all clicks. camp_type Categorical identifier of campaign type associated with an
  • user_cou ntry_top3 The country from which the user accessed a site. Limited to the top three countries, which were found in feature engineering experiments to capture over 99% of all traffic. Note, however, that the number and identity of top countries is specific to a publisher/ad exchange, which may be region and language specific.
  • offer_pos A categorical value indicating the placement of an offer within an ad slot.
  • browser Identifier of user browser e.g. Chrome, IE, Safari, etc.

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Abstract

L'invention concerne un procédé mis en œuvre par ordinateur qui consiste à recevoir, en provenance d'un serveur d'échange de publicités par l'intermédiaire d'un réseau de communication de données, un message comprenant un appel d'offres qui comprend des informations de site et des informations d'utilisateur concernant un créneau publicitaire disponible. Pour chaque offre dans la liste classée générée, une estimation de niveau de probabilité d'interaction d'utilisateur avec l'offre pour chaque offre est calculée. Pour une combinaison d'offres incluses dans la liste classée, un prix d'offre par niveau de publicité est calculé, sur la base d'au moins les estimations de niveau d'offre calculées de probabilité d'interaction d'utilisateur, les revenus d'interaction de niveau d'offre correspondants, et un paramètre d'agressivité. Une réponse à l'appel d'offres comprend une publicité à prix déterminé aux enchères qui comprend la combinaison d'offres et le prix d'offre par niveau de publicité. Des modèles d'apprentissage machine pour prédire le comportement d'utilisateurs en ligne permettent de déterminer automatiquement des estimations de probabilité d'interaction d'utilisateur avec des éléments de contenu en ligne sur la base d'un comportement agrégé d'utilisateurs précédents dans des contextes similaires.
EP18769115.9A 2017-09-14 2018-09-05 Procédé et système d'enchères adaptatives intelligentes dans un réseau d'échange en ligne automatisé Withdrawn EP3682403A1 (fr)

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