Abstract
Recommendation systems can play an important role in today's digital content platforms by supporting the suggestion of relevant content in a personalised manner for each customer. Such content customisation has not been consistent across most media domains, and particularly on radio streaming and gaming aggregators, which are the two real-world application domains focused in this work. The challenges faced in these application areas are the dynamic nature of user preferences and the difficulty of generating recommendations for less popular content, due to the overwhelming choice and polarisation of available top content. We present the design and implementation of a Reinforcement Learning-based Recommendation System (RLRS) for web applications, using a Deep Deterministic Policy Gradient (DDPG) agent and, as a reward function, a weighted sum of the user Click Distribution (CD) across the recommended items and the Dwell Time (DT), a measure of the time users spend interacting with those items. Our system has been deployed in real production scenarios with preliminary but promising results. Several metrics are used to track the effectiveness of our approach, such as content coverage, category diversity, and intra-list similarity. In both scenarios tested, the system shows consistent improvement and adaptability over time, reinforcing its applicability.

Abstract
This review discusses the integration of intelligent technologies into customer interactions in organizations and highlights the benefits of using artificial intelligence systems based on a multimodal approach. Multimodal learning in marketing is explored, focusing on understanding trends and preferences by analyzing behavior patterns expressed in different modalities. The study suggests that research in multimodality is scarce but reveals that it is as a promising field for overcoming decision-making complexity and developing innovative marketing strategies. The article introduces a methodology for accurately representing multimodal elements and discusses the theoretical foundations and practical impact of multimodal learning. It also examines the use of embeddings, fusion techniques, and explores model performance evaluation. The review acknowledges the limitations of current multimodal approaches in marketing and encourages more guidelines for future research. Overall, this work emphasizes the importance of integrating intelligent technology in marketing to personalize customer experiences and improve decision-making processes.

Abstract
This paper contributes to developing a Method for Creating Persona Templates (MCPT), addressing a significant gap in user-centred design methodologies. Utilising qualitative data collection and analysis techniques, MCPT offers a systematic approach to developing robust and context-oriented persona templates. MCPT was created by applying the Design Science Research (DSR) methodology, and it incorporates multiple iterations for template refinement and validation among project stakeholders; all of the proposed steps of this method were based on theoretical contributions. Furthermore, MCPT was tested and refined within a real-life R&D project focusing on developing a digital platform e-marketplace for short agrifood supply chains in two iteration cycles. MCPT fills a critical void in persona research by providing detailed instructions for each step of template development. By involving the target audience, users, and project stakeholders, MCPT adds rigour to the persona creation process, enhancing the quality and relevance of personae casts. This paper contributes to the body of knowledge by offering an initial proposal of a comprehensive method for creating persona templates within diverse projects and contexts. Further research should explore MCPT's adaptability to different settings and projects, thus refining its effectiveness and extending its utility in user-centred design practices.

Abstract
Influential nodes are the important nodes that most efficiently control the propagation process throughout the network. Among various structural-based methods, degree centrality, k-shell decomposition, or their combination identify influential nodes with relatively low computational complexity, making them suitable for large-scale network analysis. However, these methods do not necessarily explore nodes' underlying structure and neighboring information, which poses a significant challenge for researchers in developing timely and efficient heuristics considering appropriate network characteristics. In this study, we propose a new method (IC-SNI) to measure the influential capability of the nodes. IC-SNI minimizes the loopholes of the local and global centrality and calculates the topological positional structure by considering the local and global contribution of the neighbors. Exploring the path structural information, we introduce two new measurements (connectivity strength and effective distance) to capture the structural properties among the neighboring nodes. Finally, the influential capability of a node is calculated by aggregating the structural and neighboring information of up to two-hop neighboring nodes. Evaluated on nine benchmark datasets, IC-SNI demonstrates superior performance with the highest average ranking correlation of 0.813 with the SIR simulator and a 34.1% improvement comparing state-of-the-art methods in identifying influential spreaders. The results show that IC-SNI efficiently identifies the influential spreaders in diverse real networks by accurately integrating structural and neighboring information.

Abstract
Exploring a group of influential spreaders to acquire maximum influence has become an emerging area of research in complex network analysis. The main challenge of this research is to identify the group of important nodes that are scattered broadly, such that the propagation ability of information is maximum to a network. Researchers proposed many centrality-based approaches with certain limitations to identify the influential nodes (spreaders) considering different properties of the networks. To find a group of spreaders, the VoteRank (a voting mechanism) based method produces effective results with low time complexity, wherein each iteration, the node votes for its neighbors by its voting capability, and the node obtaining the maximum vote score is identified as an influential spreader. The major loophole of existing VoteRank methods is measuring the voting capability based on the degree, k-shell index, or contribution of neighbors methods, which does not efficiently identify the spreaders from the diverse regions based on their spreading ability. In this paper, we propose a novel Community-based VoteRank method (CVoteRank) to identify a group of influential spreaders from diverse network regions by which the diffusion process is enhanced. Firstly, we measure every node's spreading ability based on intra- and inter-connectivity structure in a community, which signifies the local and global importance of the node. To identify the seed nodes, we assign the spreading ability to that node's voting capability and iteratively calculate the voting score of anode based on its neighboring voting capability and its spreading ability. Then, the node acquiring the maximum voting score is identified as the influential spreader in each iteration. Finally, to solve the problem of influence overlapping, CVoteRank reduces the voting capability of the neighboring nodes of the identified spreader. The efficiency of CVoteRank is evaluated and compared with the different state-of-the-art methods on twelve real networks. Utilizing the stochastic susceptible-infected-recovered epidemic method, we calculate the infected scale, final infected scale, and the average shortest path length among the identified spreaders. The experimental results show that CVoteRank identifies the most efficient spreaders with the highest spreading ability within a short period and the maximum reachability, and the identified spreaders are situated at diverse portions of the networks.

Abstract