WO2015103342A1 - Mécanisme dynamique de vente d'annonces publicitaires en ligne à retour d'informations d'utilisateur - Google Patents

Mécanisme dynamique de vente d'annonces publicitaires en ligne à retour d'informations d'utilisateur Download PDF

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Publication number
WO2015103342A1
WO2015103342A1 PCT/US2014/072904 US2014072904W WO2015103342A1 WO 2015103342 A1 WO2015103342 A1 WO 2015103342A1 US 2014072904 W US2014072904 W US 2014072904W WO 2015103342 A1 WO2015103342 A1 WO 2015103342A1
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advertisement
class
ordered list
feedback
advertisements
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PCT/US2014/072904
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English (en)
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Nadia FAWAZ
Fernando Jorge Silveira Filho
Vijay Sukumar KAMBLE
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Thomson Licensing
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    • 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
    • G06Q30/0251—Targeted advertisements

Definitions

  • the present invention relates generally to the feedback systems. More specifically, the invention relates to a system to insert advertisements into a video relying on user feedback.
  • Video consumption on the web is on a steady rise due to the combination of over-the-top services 1 and Internet-enabled reception devices such as smart TVs and set-top boxes.
  • Over-the-top services 1 e.g., movies, TV shows
  • Internet-enabled reception devices such as smart TVs and set-top boxes.
  • the annual spending in online video advertisement (ads) in the US is still an order of magnitude smaller than that of TV ads.
  • Over-the-top (OTT) services distribute content to an end user without being associated to the user's Internet Service Provider (ISP). Examples, as of this writing, include Hulu, Netflix, and Amazon Instant Video.
  • ISP Internet Service Provider
  • the potential to exploit such fine-grained ad targeting depends on the ability to determine whether an ad is relevant to a user.
  • Current online video services explicitly ask users about the relevance of ads shown in commercial interruptions 2 .
  • users provide binary feedback (i.e., the ad is either relevant or not) which is used by the service to learn about user preferences. While the service tries to exploit that knowledge to decide what ads to show next in order to optimize the user's overall satisfaction, it also seeks to collect revenue from competing advertisers at the same time. It is desirable to design generic mechanisms with provably good properties which synergistically perform both of these two tasks.
  • the lower layer adaptively selects categories of advertisers to optimize the relevance feedback from the user
  • the upper layer is a truthful auction mechanism that allots ad opportunities to advertisers in the categories chosen by the lower layer, charging them contingent on relevance.
  • the relevance optimization problem in the lower layer was analyzed and proven are certain properties of the optimal policy. The instance where the user dynamics in the session is assumed to be Markovian is considered, such that a user stays to see each additional ad with a fixed probability ⁇ independent of the past or leaves the session. Using the knowledge of properties of the optimal policy, proven is that in this case an appropriately defined greedy algorithm achieves a constant factor of the optimal
  • aspects of the invention include a general framework of a two layered mechanism, which can be used to dynamically allocate advertisements when a binary feedback can be obtained from the user after each allocation.
  • the mechanism is structured around the idea that user preferences are sensitive to classes of ads rather than individual ads.
  • One technical contribution is the analysis of the optimal dynamic allocation problem in the lower layer, which captures any such scenario where a designer needs to adaptively make a sequence of relevant recommendations to a user by using his feedback.
  • Figure 1 illustrates a system diagram that serves as an environment for the invention
  • Figure 2 illustrates a flow of activities within the environment
  • Figure 3 illustrates an example media playback device block diagram
  • Figure 4 illustrates an example web publisher block diagram
  • Figure 5 illustrates an example information dependency diagram.
  • a model is considered which assumes that ads can be divided into a finite number of categories (e.g., ads on cars, travel, apparel, or other products or services) and that a user's preference profile is a binary vector where each dimension represents an ad category and its value determines whether ads in the corresponding category are relevant for the user.
  • This preference profile is not known when the user arrives but the service is assumed to know a probability distribution over possible profiles, which are called 'types' of a user.
  • a probabilistic model is assumed for the number of ad opportunities that are available during a viewing session. The challenge is then to find an adaptive mechanism, that fulfils two goals.
  • the mechanism should learn the user profile based on the user feedback obtained after each ad, in order to maximize the number of relevant ads shown during a session. This algorithm needs to take into account the limited tolerance that users have to watching repetitions of a same advertisement (ad). Studies have shown that users are most likely to skip ads that they have seen repeatedly. Second, this mechanism should also prescribe how to assign these ad slots to competing strategic advertisers given their willingness to pay, and how to charge them to generate revenue.
  • One aspect of the invention is the design and analysis of a generic dynamic ad allocation mechanism which achieves these objectives.
  • the design exploits the observation that the preferences of the users are sensitive to categories of ads rather than individual ads.
  • the inventors have developed a mechanism with a two layered alternating structure.
  • the lower layer concerned with relevance optimization, adaptively selects categories of advertisers to maximize the relevance feedback from the user.
  • the upper layer is a truthful auction mechanism that dynamically allots advertisement opportunities to competing advertisers in categories prescribed by the lower layer, charging them contingent on relevance.
  • MAB Learning The allocation problem in the lower layer the mechanism is related to the class of multi-armed bandit (MAB) problems with correlated arms that have been studied in recent years.
  • MAB multi-armed bandit
  • Much of the prior work focuses on a specific correlation structure in which the rewards of the different arms are a linear function of a hidden global state vector.
  • the written art considers the objective of minimizing the rate of the growth of regret due to incomplete information.
  • the rewards are binary, the correlation structure is completely general, and there is a natural discounting on the sequence of rewards.
  • solving this problem in an exact manner requires stochastic dynamic programming over a large state-space.
  • index based optimal policies may not exist.
  • Static Ad Auctions The class of online ad auctions closest to the upper layer setting are position auctions, which are used in the context of sponsored search to sell advertisement space, called ad slots.
  • the publisher In the static setting, the publisher is concerned with allocating at once a block of n impressions to advertisers, who are most commonly charged on a per-click basis.
  • Ad slots differ in the attention they can draw from users and in their click- through rates (CTR), which is the probability of a user clicking on an ad displayed in that position.
  • CTR click- through rates
  • slots located higher on a page attract more attention and more clicks and hence they can be ranked from top to bottom.
  • GSP Generalized Second Price auction
  • GSP ranks all advertisers in the order of their bids, which is their signal of the maximum amount they are willing to pay per click/impression, and allots the ad slots in the order of their ranking: the highest bidder is allotted the highest ranked slot.
  • GSP payment rule is that each advertiser who is allotted a position pays a price equal to the bid of the advertiser in the next highest position per click/impression.
  • VCG Vickrey-Clarke-Groves mechanism
  • FIG. 1 depicts an environment 100 in which the invention may be practiced.
  • the system environment includes a router/gateway 106 connected via cable or wireless connection 111 to an internet service provider (ISP) 110 enabling access via connections 113 to a network 120.
  • the router can be a standard router that is compatible with internet service provider equipment for routing internet protocol packets or a suitable gateway or modem.
  • the router may be either a public or a private router.
  • the router or gateway 106 can provide access to the network 120 via wired or wireless links to multiple user equipment such as user playback device 104. Although only one user playback or content consumption device is shown, many such devices can be supported. Each properly configured playback device 104 can operate independently to access a media service.
  • the publisher is a web-based entity that is providing a download of multimedia content, such as a video to the user media player 104 via the ISP 110 and gateway 106.
  • the publisher 118 can also provide streaming video to the user's media playback device 104.
  • the publisher can inject advertisements into the video stream so that the user can view the advertisements.
  • Advertisers 102a, 102b, and 102c have an interest in placing their product or service advertisements in the video stream of the media playback device 104 so that the user can be afforded the opportunity to see the advertiser' s product or service. Although only three advertisers are shown, many may be present.
  • web-based publisher 118 communicates with advertisers and users via connection 119 to the network 120.
  • Publisher 118 communicates with advertisers 102 a, 102b, and 102c via connections 117a, 117b, and 117c respectively.
  • Publisher 118 requests monetary bids from advertisers 102 to place advertisements (ads) into the users multimedia video stream for playback on users playback device 104.
  • Advertisers respond to the request for bids from publisher and, according to aspects of the invention, the publisher insert the advertisements into the user playback device 104 video stream.
  • the ads may be available to the publisher via a database 121 of advertisements from the advertisers.
  • the user After viewing an ad, the user has an opportunity to rate the add by providing binary feedback as to whether the ad is relevant to the user or not.
  • the selection of advertisements for insertion is calculated according to the bid prices offered by the user and the user' s feedback response.
  • a mathematical basis for ad placement and monetization is as described below.
  • each user type i with an //-length binary vector of the ⁇ qj l ⁇ values for different categories.
  • Q ⁇ qj ⁇
  • Table 1 is an example of a relevance matrix with six types of users labeled 1 to 6 and five ad categories labeled A to E. Each category has the specified number of advertisers.
  • display rules is defined as a set of fixed rules which decide when an ad can be shown to a user during the session. For each possible play dynamics of a user, a display rule dynamically assigns ads to the user at different time instances in the play session.
  • a bound on the number of ad opportunities that may come up in a play session is not limited. However, it is assumed that an advertisement cannot be shown more than once to the same user during the session, a constraint that will be called the matching constraint. Hence the maximum number of ads that can be shown is restricted to L, the number of advertisers. Most of the following analysis may be extended appropriately if there are instead any finite bound on the number of repetitions of an advertisement (ad).
  • a model of the number of display opportunities is a random variable C £ (1,2, ⁇ ⁇ with a probability distribution P c . It is further assumed that the random variable C is independent of the user type X.
  • the publisher has to decide which ad should be shown to the user under the matching constraint.
  • the publisher can elicit feedback from the user after every ad.
  • the feedback for an ad is the same for every ad in the category, eliciting feedback again for every such ad is vacuous.
  • the present approach relies on a dynamic mechanism which iterates between two layers, which are discussed below. More precisely, the lower layer exploits user feedback to allocate ad opportunities to ad categories, in order to maximize relevance.
  • the upper layer runs a truthful ad auction mechanism to allocate each ad opportunity to an advertiser from the categories determined by the lower layer.
  • the lower layer Dynamic relevance maximization.
  • One objective of the present mechanism is to maximize the expected number of relevant ads shown to a user in the session under the constraint that no advertisement is shown more than once. This forms the lower layer of the mechanism.
  • the objective is defined formally as follows.
  • Let ⁇ be the set of all feasible policies.
  • the objective of the publisher is to find a policy which maximizes the expected number of relevant ads shown in a session.
  • U t be the random variable denoting the advertiser chosen at time t under a policy and with some abuse of notation, let g(U t ) be the category of the advertiser. Then the objective of the publisher is:
  • Lemma 1 The optimal dynamic ad allocation policy has the property that if at any opportunity there exists a set of advertisers who will generate a positive feedback with probability 1 conditional on the past observations, then they are all scheduled to be allotted immediately in any order. A proof is given in Appendix 1. This property implies that if a positive feedback is received for an ad belonging to a particular category, then all advertisers of that category are scheduled to be allotted in the immediately following opportunities.
  • S(t) be the set of probable user types conditional on the information obtained till opportunity t— 1.
  • Definition 1 A category j is said to dominate category j' at opportunity t if Mji (t) c Mj (t).
  • the categories which are not dominated by any other category will be called non-dominated categories.
  • A, B, C and D are the only non-dominated categories since A dominates E. Then, one can show that:
  • U is the class of categories found relevant by exactly same set of types.
  • U will be called a non- dominated equivalence class of categories and ydenotes the set of types which find the class U relevant. Allow for singleton categories in the definition and so suppose there are K such non-dominated equivalence classes ⁇ U 1 , U K ] which partition the set of non-dominated categories in the relevance matrix. If furthermore the sets of types ⁇ M 0I , ... , M UK ] are mutually disjoint, then the set of non-dominated equivalence classes partition the type space.
  • the relevance matrix is of the form of a permutation matrix of K smaller block matrices, with each block matrix corresponding to an equivalent non-dominated class.
  • Such a small block is composed of columns of all Is, one for each category in the class, and columns corresponding to the categories that the class dominates.
  • Lemma 3 Consider an initial set of non-dominated classes of ad categories ⁇ U 1 , U K ] (which have not yet been presented) and a category from a class k * is presented. If a negative feedback is received for this category, then the set of non-dominated equivalence classes of ad categories for the new relevance matrix left after computing the posterior distribution results from the removal of class U k * and further can only result from
  • Definition 2 A greedy algorithm is defined as the one in which at each experimentation opportunity, amongst all non-dominated ad categories, any ad category belonging to that non-dominated equivalence class which has the maximum expected number of ads with positive feedback conditional on the history is presented. If the feedback is positive then all the advertisers in the categories in that class are exhausted.
  • Theorem 1 The Greedy algorithm is [ max ⁇
  • L K are the number of ads in class k and V K is the optimal payoff-to-go conditional on the event E given that class k is also used up.
  • An approximate payoff by is defined as
  • Equation (10) Further observe that since the greedy policy chooses k, then ⁇ ⁇ ⁇ ⁇ ⁇ or fe—
  • the optimal policy cannot attain a payoff greater than R.
  • the ratio of the payoff under the greedy policy and that under the optimal policy is at least ? L_1 .
  • the upper layer A dynamic auction for selling ad opportunities.
  • the above was concerned with finding the optimal dynamic ad allocation mechanism for allotting ad opportunities to ad categories.
  • the overall mechanism has a two layered structure: the lower layer is concerned with relevance maximization and the upper layer is a market mechanism. This proposal is facilitated by the following observation.
  • the optimal policy or the greedy heuristic
  • requires one to present a non-dominated equivalence class of ad categories one can do so by presenting any ad from any of the categories in the class.
  • this auction recommends allotting the highest bidder first, then one can test the relevance for the class using this bidder. If the feedback is positive, then one continues the auction since no other class emerges to be relevant with probability 1 after the posterior update.
  • Drop-out price auction An auction mechanism which is called the drop-out price auction is defined which is an implementation of the efficient VCG mechanism for the present setting.
  • Bidding rule Each advertiser i submits a bid bi which is the maximum amount it is willing to pay for an ad shown and deemed relevant.
  • Allocation rule The publisher ranks the advertisers in descending order of their bids and allocates the ad opportunities in the order of the ranking till either the user leaves or all the advertisers in the class S are exhausted.
  • the revenue optimal auction Drop-out price auction with reserve price.
  • a dropout price auction with a reserve price is a modification of the above mechanism so that there is a fixed price r which is the minimum amount any advertiser has to pay if he gets allotted. Thus the only advertisers ranked are the ones whose bid is higher than the reserve price.
  • the optimal auction may prescribe not allotting an advertisement opportunity to an advertiser if his valuation is too low even if he is relevant. Hence this auction goes against the objective of relevance optimality of the lower layer. This is not the case with the drop-out price auction.
  • Lower layer follows an optimal or greedy dynamic allocation policy. If at an experimentation opportunity, the policy prescribes allotting an advertiser from an equivalent non-dominated class S, choose the top advertiser prescribed by the upper layer in the auction mechanism run on the set of advertisers in S. If the feedback is negative, move on to the next experimentation opportunity. If it is positive, switch to the upper layer and continue with the auction on the class S
  • Upper layer At any experimentation opportunity in the lower layer, for a prescribed non- dominated equivalence class S, create an order of advertisers to be presented according to any of the two auction mechanisms. If the top advertiser is deemed relevant, continue allotting according to the order till the user drops out or all advertisers scheduled to be allotted are exhausted and then charge the allotted advertisers according to the payment rule of the auction.
  • FIG. 2 depicts a flow diagram 200 of a method according to aspects of the invention.
  • the method 200 starts at step 205.
  • the ads are to be inserted into the video file at times given by the display rules discussed earlier.
  • the ads may be organized according to categories. Some categories may be indicative of products whereas other categories may be indicative of services.
  • Product categories may include such categories as cars, electronics, stationary, major appliances, personal items and the like.
  • Service categories may include such categories as travel agents, financial planning, catering, event planning, elder care, and the like.
  • Multiple advertisers may have ads in each category. Thus, advertisers can bid on ads in a category. As an option, the advertisers can also bid on ads at step 220 below.
  • the process 200 moves to step 210 where a starting class is chosen.
  • the class determined in step 210 is chosen as the non-dominated equivalence class of ad categories with the maximum expected relevant ads. As such, it may show ads in the same class but from different categories.
  • An equivalence class of ad categories is a set of ad categories. Each category contains multiple ads.
  • the ads related to the chosen class are arranged in order of their bid values from the various advertisers who belong to that class.
  • the first advertisement in the ordered list is the advertisement having the highest bid value in the class chosen. It is then displayed to a user while viewing a video.
  • the user is asked for feedback on whether the ad is relevant to her. The user responds with a binary yes or no response. The user response in this instance is solicited and provided only once per ad equivalence class.
  • step 240 the presentation of ads to the user in the category of ads continues. As such, the insertion of ads into the video stream is continued.
  • the ads presented are from different advertisers derived from the ordered list of ads created in step 220.
  • the presentation of ads continues until the class of ads is exhausted or until the user is determined to have stopped viewing the video (i.e. the user exits).
  • the class of ads becomes exhausted when all of the ads from all of the advertisers in that class have been inserted into the video being viewed by the user.
  • the advertisers are charged according to the drop out price rule or the drop out price with reserve price rule.
  • step 230 If at step 230, it is determined that the ad was not relevant to the user, then the method 200 moves to step 235 where a fee may be charged to the advertiser for presentation of his ad in the video. Even though the user found it non-relevant, the service of advertisement placement was provided and the single fee is appropriate. After registering the fee, the method moves to step 250 where it is determined if the user has exited the video viewing session. If the user has exited, then the process stops at step 265. If the user has not exited, then it is determined that the class of ads is not of interest to the viewer and a different class of ads may be chosen in step 260. The detection of an exit can be automatically sensed by observing a users' on-line presence.
  • step 260 the current class of ads is removed because it has no further relevance to the user viewing the video.
  • a new relevance matrix is computed.
  • the system returns to step 210 for a new class of categories of ads to be selected by finding the class of categories that has the next highest expected relevance. After selection of the next class of categories of ads to display to the user, the method returns to step 220.
  • step 220 the order of advertisers in the newly selected class of advertisement is generated.
  • the first advertisement from the highest bidder in this new class of ads is then displayed to a user in the video being viewed by the user.
  • Step 220 flows to step 230 and the steps are repeated according to their functions described above.
  • Figure 3 is an example block diagram of the media device 104 of Figure 1.
  • the block diagram configuration includes a bus-oriented 350 configuration interconnecting a processor 320, and a memory 345.
  • the configuration of Figure 3 also includes a network interface 101 to a gateway, such as router or gateway 106 of Figure 1.
  • the router or gateway may utilize either a wired or a wireless interface to the media device.
  • Processor 320 provides computation functions for the media device, such as the one depicted in Figure 1.
  • the processor 320 can be any form of CPU or controller that utilizes communications between elements of the media device to control communication and computation processes.
  • bus 350 provides a communication path between the various elements of embodiment 104 and that other point-to- point interconnection options (e.g. non-bus architecture) are also feasible.
  • Memory 345 can act as a repository for memory related to any of the methods that incorporate the functionality of the media device. Memory 345 can provide the repository for storage of information such as program memory, downloads, uploads, or scratchpad calculations as well as the storage of streamed video and inserted advertisements. Those of skill in the art will recognize that memory 345 may be incorporated all or in part of processor 320. Network interface 325 has both receiver and transmitter elements for communication as known to those of skill in the art.
  • User interface and display 310 is driven by interface circuit 315.
  • the interface 310 is used as a multimedia interface having both audio and video capability to display streamed audio and/or video obtained via network interface 325 and connection 101 to a network.
  • the user interface also allows the user of the media device to supply feedback to the publisher 118 concerning the relevance of an advertisement placed in the streaming multimedia being rendered for the user.
  • the publisher sends a request for feedback to the media device after display of an advertisement.
  • the User interface and display 310 allows the user of the media device to respond to the publishers request for feedback.
  • Figure 4 is an example block diagram of the publisher of Figure 1.
  • the block diagram configuration is simplified and is depicted as if the publisher were a single device.
  • a web-entity such as the publisher 118 may include multiple electronic systems interconnected to function as a web server or other equivalent web entity. If configured as a single device, the publisher 118 of Figure 1 may take the embodiment of Figure 4.
  • the publisher depicted in Figure 4 is only one embodiment and that many embodiments, including those of a multi- element system may include some of the features shown in Figure 4 even if publisher 118 is a distributed entity.
  • the publisher includes a bus-oriented 450 configuration interconnecting a processor 420, and a memory 445.
  • the configuration of Figure 4 also includes a network interface 425 to a network link 119, such as an interface to a wide area public or private network.
  • the network interface 425 may utilize either a wired or a wireless interface to connect the publisher with the network 120.
  • Processor 420 provides computation functions for the publisher. However, one of skill in the art recognizes that such processor 420 may be a single processing device or a distributed capability. If located in one device, the processor 420 can be any form of CPU or controller that utilizes communications between elements of the publisher to control communication and computation processes. Those of skill in the art recognize that bus 450 provides a communication path between the various elements of embodiment 118 and that other point-to-point interconnection options (e.g. non-bus type architecture) are also feasible that could support distributed elements over a public or private network.
  • point-to-point interconnection options e.g. non-bus type architecture
  • Memory 445 can act as a repository for memory related to any of the methods that incorporate the functionality of the publisher. Memory 445 can provide the repository for storage of information such as program memory, downloads, uploads, or scratchpad calculations as well as the storage of streamed video and inserted advertisements. Those of skill in the art will recognize that memory 445 may be incorporated all or in part of processor 420.
  • Network interface 425 has both receiver and transmitter elements for communication as known to those of skill in the art.
  • Database interface 435 is used to connect the publisher to a database of advertisements. In one embodiment, as shown in Figure 1, the advertisement database 121 is external to the publisher. In alternate embodiments, the database 121 can be accessed via a public or private network and the database interface 435 represents the network interface connecting to the database. In another embodiment, the database interface 435 is used to connect to a local database.
  • User interface 410 and its respective display driver 415 is used to locally manage the publisher.
  • This policy is the following:
  • the policy generates the sequence of allocations ⁇ k', u t+1 , ⁇ , u c ⁇ ⁇ and gets the feedback ⁇ 0, 3 ⁇ 4( Ut+1 ), ⁇ ⁇ ⁇ , y g Uc ) ⁇ -
  • Y j 1 by alloting k and then continues to exhaust all the advertisers in j before switching to the prescriptions of ⁇ .
  • the number of relevant ads shown under policy is at least as high as that under policy for every such event W from a set of disjoint events whose union is the entire probability space.
  • the expected number of relevant ads shown is also at least as high.
  • An auction mechanism consists of two rules specified by the publisher: the allocation rule and the payment rule.
  • the set of possible rules are defined as follows.
  • the set of possible bids for advertiser i is same as his set of possible valuations, which is justified by the revelation principle in mechanism design (see discussion below).
  • Denote the probability of choosing a particular ordering by ⁇ - ⁇ 1 , ⁇ , b L ) and with some abuse of notation denote the random variable denoting the ordering chosen by Q) 1 , cdots, b L ' ).
  • the interpretation of the allocation rule is that, once the bids have been collected, the publisher draws the ordering xf
  • Mi b lt ⁇ , b L ) is the payment made by advertiser i to the publisher given the reported bids.
  • the bidding strategies of the advertisers constitute a Nash equilibrium in the resulting game.
  • the expectation is over the probability space of possible valuations of the advertisers and the possible number of advertisement opportunities C.
  • V i 1, ⁇ , Land ⁇ , b t G V i
  • An auction mechanism consists of analyzing the problem in a similar way as in the classical analysis, a mechanism is incentive compatible if and only if the associated n t is non- decreasing and the associated interim payments satisfy
  • publisher's objective is to find a mechanism that maximizes
  • Dynamic programs for finding an optimal ordering of n objects are typically solved by computing the optimal 'payoff-to-go' for every fixed choice of first k elements in the order, for each k ⁇ n and inducting backwards.
  • a type i node is connected to a category node j if S t £ Mj .
  • This is called the information dependency graph.
  • the information dependency graph is shown in Figure 5.
  • the information dependency graph has an important property. Since all the category nodes are non-dominated, there does not exist a category node in the graph such that all the type nodes connected to it are also connected to some other category node.
  • a summary of the algorithm is as follows. First decide the optimal order in which the categories in J are presented to the user. Continue presenting the categories in this order till one continues to get a negative feedback, at each time pruning off the node corresponding to the shown category along with the type nodes connected to it. The process of pruning a category node may lead to some other category node being dominated. It may also lead to a category node being redundant, which means that no type node is connected to it. Thus all such nodes have to be removed from the information dependency graph after the pruning of a node.
  • this function returns an information graph with all the nodes of categories in J stripped off. It also removes redundant category nodes and dominated category nodes that result from this removal.

Abstract

La présente invention concerne un procédé permettant de présenter des annonces publicitaires à un observateur d'une vidéo en flux continu consistant à sélectionner une classe d'annonces publicitaires à présenter dans la vidéo en flux continu et à organiser de multiples annonces publicitaires de la classe sélectionnée en une liste ordonnée par valeur décroissante. La valeur se rapporte à une valeur d'enchère que ledit publicitaire place sur n'importe quelle annonce publicitaire pour insertion dans la vidéo. L'annonce publicitaire ayant la plus grande valeur est insérée en premier dans la vidéo et est présentée à un observateur. L'observateur fournit un retour d'informations concernant l'annonce publicitaire insérée. Si le retour d'informations de l'observateur est positif, alors davantage d'annonces publicitaires de la classe sélectionnée sont présentées. Si le retour d'informations est négatif, alors une nouvelle classe est sélectionnée et l'annonce publicitaire ayant la plus grande valeur de la nouvelle classe est présentée à l'observateur.
PCT/US2014/072904 2013-12-31 2014-12-30 Mécanisme dynamique de vente d'annonces publicitaires en ligne à retour d'informations d'utilisateur WO2015103342A1 (fr)

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