Allocation algorithms for personal TV advertisements

Abstract

In this paper we consider the problem of allocating personal TV advertisements to viewers. The problem's input consists of ad requests and viewers. Each ad is associated with a length, a payment, a requested number of viewers, a requested number of allocations per viewer and a target population profile. Each viewer is associated with a profile and an estimated viewing capacity which is uncertain. The goal is to maximize the revenue obtained from the allocation of ads to viewers for multiple periods while satisfying the ad constraints. First, we present the integer programming (IP) models of the problem and several heuristics for the deterministic version of the problem where the viewers' viewing capacities are known in advance. We compare the performances of the proposed algorithms to those of the state-of-the-art IP solver. Later, we discuss the multi-period uncertain problem and, based on the best heuristic for the deterministic version, present heuristics for low and high uncertainty. Through computational experiments, we evaluate our heuristics. For the deterministic version, our best heuristic attains 98 % of the possible revenue and for the multi-period uncertain version our heuristics performances are very high, even in cases of high uncertainty, compared to the revenue obtained by the deterministic version.

Scheduling TV advertisements via genetic algorithm

“…They developed an algorithm to maximise the revenue of service-providers based on the embedding of user-mapping advertisements, which were selected according to the users' preferences. Adany et al (2013) formulated the deterministic ads allocation problem, in order to maximise the reward from ads scheduling, as a knapsack problem. Several heuristic algorithms were presented for the solution.…”

“…Our model can also be compared to Adany et al (2013) and Kwarteng and Asante (2017). Both of them formulate the advertisement allocation problem as a knapsack from the TV-company's point of view, whereas our research considers the advertiser's perspective.…”

Bandwidth and Resource Allocation Optimization Through a Probabilistic Algorithm for Mobile TV

“…A summary is presented in section 5. References in the current literature ( [1], [2], [6]) do not apply directly to the exact circumstance described so we understand our methods to be novel in the application area.…”

“…By considering multicast to be similar to broadcasting, and taking into account the bandwidth constraints of carriers as akin to available channel constraints of traditional television distribution, we can transfer results from the field of fixed channel delivery to solving the problems of mobile TV. Herein, we use the language of traditional TV delivery, but under general assumptions of resource constraint the results apply as well to mobile TV ( [1], [2]).…”

CAVVA: Computational Affective Video-in-Video Advertising

“…We measure the various dimensions [20] of advertisement/ brand recall (cued/uncued, immediate/day-after), which quan -TABLE IV RESULTS FROM THE TWO-SAMPLE KOLMOGOROV-SMIRNOV TEST FOR THE FOUR SUBJECTIVE QUESTIONS: Q1-UNIFORM DISTRIBUTION OF ADVERTISEMENTS, Q2-DISTURBANCE TO THE PROGRAM FLOW, Q3-RELEVANCE OF THE ADVERTISEMENT, Q4- tifies how much of the advertisement content was assimilated by the user and how well the user remembers the advertisement/brand immediately after the session and after some time has passed (day-after recall). Fig.…”