A Content Recommendation Policy for Gaining Subscribers
Konstantinos Theocharidis, Manolis Terrovitis, Spiros Skiadopoulos, Panagiotis Karras · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval · 2022
How can we recommend content for a brand agent to use over a series of rounds so as to gain new subscribers to its social network page? The Influence Maximization (IM) problem seeks a set of~k users, and its content-aware variants seek a set of~k post features, that achieve, in both cases, an objective of expected influence in a social network. However, apart from raw influence, it is also relevant to study gain in subscribers, as long-term success rests on the subscribers of a brand page; classic IM may select~k users from the subscriber set, and content-aware IM starts the post's propagation from that subscriber set. In this paper, we propose a novel content recommendation policy to a brand agent for Gaining Subscribers by Messaging (GSM) over many rounds. In each round, the brand agent messages a fixed number of social network users and invites them to visit the brand page aiming to gain their subscription, while its most recently published content consists of features that intensely attract the preferences of the invited users. To solve GSM, we find, in each round, which content features to publish and which users to notify aiming to maximize the cumulative subscription gain over all rounds. We deploy three GSM solvers, named \sR, \sSC, and \sSU, and we experimentally evaluate their performance based on VKontakte (VK) posts by considering different user sets and feature sets. Our experimental results show that \sSU provides the best solution, as it is significantly more efficient than \sSC with a minor loss of efficacy and clearly more efficacious than \sR with competitive efficiency.