Adaptive Content-Aware Influence Maximization via Online Learning to Rank
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Adaptive Content-Aware Influence Maximization via Online Learning to Rank. / Theocharidis, Konstantinos; Karras, Panagiotis; Terrovitis, Manolis; Skiadopoulos, Spiros; Lauw, Hady W.
In: ACM Transactions on Knowledge Discovery from Data, Vol. 18, No. 6, 146, 2024.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Adaptive Content-Aware Influence Maximization via Online Learning to Rank
AU - Theocharidis, Konstantinos
AU - Karras, Panagiotis
AU - Terrovitis, Manolis
AU - Skiadopoulos, Spiros
AU - Lauw, Hady W.
N1 - Publisher Copyright: © 2024 Copyright held by the owner/author(s).
PY - 2024
Y1 - 2024
N2 - How can we adapt the composition of a post over a series of rounds to make it more appealing in a social network? Techniques that progressively learn how to make a fixed post more influential over rounds have been studied in the context of the Influence Maximization (IM) problem, which seeks a set of seed users that maximize a post’s influence. However, there is no work on progressively learning how a post’s features affect its influence. In this article, we propose and study the problem of Adaptive Content-Aware Influence Maximization (ACAIM), which calls to find k features to form a post in each round so as to maximize the cumulative influence of those posts over all rounds. We solve ACAIM by applying, for the first time, an Online Learning to Rank (OLR) framework for IM purposes. We introduce the CATRID propagation model, which expresses how posts disseminate in a social network using click probabilities and post visibility criteria and develop a simulator that runs CATRID via a training-testing scheme based on real posts of the VK social network, so as to realistically represent the learning environment. We deploy three learners that solve ACAIM in an online (real-time) manner. We experimentally prove the practical suitability of our solutions via exhaustive experiments on multiple brands (operating as different case studies) and several VK datasets; the best learner is evaluated on 45 separate case studies yielding convincing results.
AB - How can we adapt the composition of a post over a series of rounds to make it more appealing in a social network? Techniques that progressively learn how to make a fixed post more influential over rounds have been studied in the context of the Influence Maximization (IM) problem, which seeks a set of seed users that maximize a post’s influence. However, there is no work on progressively learning how a post’s features affect its influence. In this article, we propose and study the problem of Adaptive Content-Aware Influence Maximization (ACAIM), which calls to find k features to form a post in each round so as to maximize the cumulative influence of those posts over all rounds. We solve ACAIM by applying, for the first time, an Online Learning to Rank (OLR) framework for IM purposes. We introduce the CATRID propagation model, which expresses how posts disseminate in a social network using click probabilities and post visibility criteria and develop a simulator that runs CATRID via a training-testing scheme based on real posts of the VK social network, so as to realistically represent the learning environment. We deploy three learners that solve ACAIM in an online (real-time) manner. We experimentally prove the practical suitability of our solutions via exhaustive experiments on multiple brands (operating as different case studies) and several VK datasets; the best learner is evaluated on 45 separate case studies yielding convincing results.
KW - content recommendation
KW - Influence maximization
KW - online learning
KW - simulation
KW - social networks
U2 - 10.1145/3651987
DO - 10.1145/3651987
M3 - Journal article
AN - SCOPUS:85192380050
VL - 18
JO - ACM Transactions on Knowledge Discovery from Data
JF - ACM Transactions on Knowledge Discovery from Data
SN - 1556-4681
IS - 6
M1 - 146
ER -
ID: 392108376