2021
Tsitseklis, K.; Vitoropoulou, M.; Karyotis, V.; Papavassiliou, S.
Socio-Aware Recommendations under Complex User Constraints Journal Article
In: IEEE Transactions on Computational Social Systems, vol. 8, no. 2, pp. 377-387, 2021, ISSN: 2329924X, (cited By 1).
Abstract | Links | BibTeX | Tags: Complex networks, Constrained recommendations; Information diffusion process; Information overloads; Joint behavior; Online social networks (OSN); Two-step procedure; User constraints; Users' -constraints, Heuristic methods; Social networking (online)
@article{Tsitseklis2021377,
title = {Socio-Aware Recommendations under Complex User Constraints},
author = {K. Tsitseklis and M. Vitoropoulou and V. Karyotis and S. Papavassiliou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099574885&doi=10.1109%2fTCSS.2020.3046686&partnerID=40&md5=dd9c5d680b886d8e4fcaf5abba0c7d4e},
doi = {10.1109/TCSS.2020.3046686},
issn = {2329924X},
year = {2021},
date = {2021-01-01},
journal = {IEEE Transactions on Computational Social Systems},
volume = {8},
number = {2},
pages = {377-387},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {In this work, we consider the joint behavior of an information diffusion process integrated with a recommender system (RecSys) over an online social network (OSN), where the typical users' resilience to information varies, leading to potential information overloads. We assume that each user has a threshold over the information that she can process in a meaningful way, and exceeding it could lead to user dissatisfaction or the user remaining idle. In order to efficiently tackle this issue, while considering complex user constraints, we consider two types of users' capacity to information, that is, capacity for distinct items and capacity for duplicate items. In this setting, we aim to allocate the items in the OSN in order to maximize users' total relevance to the former while ensuring that no user exceeds any type of capacity. We show that this problem is NP-complete and present various heuristic methods to address it. A novel framework, called socially constrained recommendations (SCoRe), is developed for the final assignment of items to users, consisting of a two-step procedure. We present and evaluate two different approaches for each step and discuss the usability of SCoRe for the efficient diffusion of items in the network while respecting the users' constraints. © 2014 IEEE.},
note = {cited By 1},
keywords = {Complex networks, Constrained recommendations; Information diffusion process; Information overloads; Joint behavior; Online social networks (OSN); Two-step procedure; User constraints; Users' -constraints, Heuristic methods; Social networking (online)},
pubstate = {published},
tppubtype = {article}
}
In this work, we consider the joint behavior of an information diffusion process integrated with a recommender system (RecSys) over an online social network (OSN), where the typical users' resilience to information varies, leading to potential information overloads. We assume that each user has a threshold over the information that she can process in a meaningful way, and exceeding it could lead to user dissatisfaction or the user remaining idle. In order to efficiently tackle this issue, while considering complex user constraints, we consider two types of users' capacity to information, that is, capacity for distinct items and capacity for duplicate items. In this setting, we aim to allocate the items in the OSN in order to maximize users' total relevance to the former while ensuring that no user exceeds any type of capacity. We show that this problem is NP-complete and present various heuristic methods to address it. A novel framework, called socially constrained recommendations (SCoRe), is developed for the final assignment of items to users, consisting of a two-step procedure. We present and evaluate two different approaches for each step and discuss the usability of SCoRe for the efficient diffusion of items in the network while respecting the users' constraints. © 2014 IEEE.