Abstract
The ongoing improvements of technology worldwide helped humans and businesses in different aspects by enhancing human-computer interactions. Especially after the outbreak of the COVID-19, people head to the virtual world by shopping online instead of going to the actual store, watching movies on platforms like “Netflix” instead of going to cinemas, or companies are applying different methods to continue their internal operations online. So most companies now invest much effort to enhance their online platforms to cope with the occurring situation. One way of enhancing the online system, especially in E-commerce, E-learning, and entertainment platforms, is by building robust recommendation algorithms. Recommender systems play a massive role in improving the online experience by suggesting to the user relevant items. However, treating all users the same by applying recommendation strategies that do not include the user her-/himself as the center of the algorithm may lead to an unpleasant user experience as each user has a different personality, taste, and different needs. Thus, in this paper, a structured review of the efforts invested in creating personalized recommendation systems is studied to explore the personal factors included in previous trials. Accordingly, we propose XReC, a generic framework for building character-based recommender systems based on Character Computing principles, including all the aspects that influence human behavior. As a result of integrating multiple human aspects, the system’s complexity will arise. A personalized explanation accompanying each recommendation is provided to improve our framework’s transparency, trustworthiness, persuasiveness, effectiveness, and user satisfaction.
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Elazab, F., El Bolock, A., Herbert, C., Abdennadher, S. (2021). XReC: Towards a Generic Module-Based Framework for Explainable Recommendation Based on Character. In: De La Prieta, F., El Bolock, A., Durães, D., Carneiro, J., Lopes, F., Julian, V. (eds) Highlights in Practical Applications of Agents, Multi-Agent Systems, and Social Good. The PAAMS Collection. PAAMS 2021. Communications in Computer and Information Science, vol 1472. Springer, Cham. https://doi.org/10.1007/978-3-030-85710-3_2
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