Adaptive Content-Aware Influence Maximization via Online Learning to Rank
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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.
Original language | English |
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Article number | 146 |
Journal | ACM Transactions on Knowledge Discovery from Data |
Volume | 18 |
Issue number | 6 |
Number of pages | 35 |
ISSN | 1556-4681 |
DOIs | |
Publication status | Published - 2024 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:
© 2024 Copyright held by the owner/author(s).
- content recommendation, Influence maximization, online learning, simulation, social networks
Research areas
ID: 392108376