Nowadays, we are experiencing a huge growth in the available information, caused by the advent of communication technology, which humans cannot handle by themselves. Personal Assistant Agents can help humans to cope with the task of selecting the relevant information. In order to perform well, these agents should consider not only their preferences, but also their mental states (such as beliefs, intentions and emotions) when recommending information. In this paper, we describe an ongoing Recommender System application, that implements a Multiagent System, with the purpose of gathering heterogeneous information from different sources and selectively deliver it based on: user’s preferences; the community’s trends; and on the emotions that it elicits in the user.
Subject
Recommender Systems
Conference
11th International Conference on Practical Applications of Agents and Multi-Agent Systems, Salamanca, Spain, May 2013
DOI
Cited by
Year 2020 : 1 citations
Panagiotakis, C., Papadakis, H., & Fragopoulou, P. (2020). Unsupervised and supervised methods for the detection of hurriedly created profiles in recommender systems. INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS.
Year 2019 : 5 citations
Cena, F., Likavec, S., & Rapp, A. (2019). Real world user model: Evolution of user modeling triggered by advances in wearable and ubiquitous computing. Information Systems Frontiers, 21(5), 1085-1110.
Mizgajski, J., & Morzy, M. (2019). Affective recommender systems in online news industry: how emotions influence reading choices. User Modeling and User-Adapted Interaction, 29(2), 345-379.
Cai, H., & Zhang, F. (2019). An unsupervised method for detecting shilling attacks in recommender systems by mining item relationship and identifying target items. The Computer Journal, 62(4), 579-597.
Bielozorov, A., Bezbradica, M., & Helfert, M. (2019, July). The role of user emotions for content personalization in e-commerce: literature review. In International Conference on Human-Computer Interaction (pp. 177-193). Springer, Cham.
Almira, C., & Maulidevi, N. U. (2019, September). Travel Itinerary Recommendation for Real World Point of Interests Using Iterated Local Search. In 2019 International Conference of Advanced Informatics: Concepts, Theory and Applications (ICAICTA) (pp. 1-6). IEEE.
Year 2018 : 8 citations
Zhang, F., Zhang, Z., Zhang, P., & Wang, S. (2018). UD-HMM: An unsupervised method for shilling attack detection based on hidden Markov model and hierarchical clustering. Knowledge-Based Systems, 148, 146-166.
Meng, X., Wang, S., Liu, H., & Zhang, Y. (2018, April). Exploiting emotion on reviews for recommender systems. In Thirty-Second AAAI Conference on Artificial Intelligence.
Cena, F., Rapp, A., Musto, C., & Lops, P. (2018, July). Towards a conceptual model for holistic recommendations. In Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization (pp. 207-210).
Panagiotakis, C., Papadakis, H., & Fragopoulou, P. (2018, September). Detection of hurriedly created abnormal profiles in recommender systems. In 2018 International Conference on Intelligent Systems (IS) (pp. 499-506). IEEE.
Deng, F., Ren, P., Qin, Z., Huang, G., & Qin, Z. (2018). Leveraging Image Visual Features in Content-Based Recommender System. Scientific Programming, 2018.
Mustafa, G. (2018). A methodology for contextual recommendation using artificial neural networks. PhD Thesis, University of Bedfordshire
Morisio, M., & Verazzo, G. (2018). Multi-channel Conversational Agent for Personalized Music Recommendations. MSc Thesis, Politecnico di Torino.
Thendral, S. E., & Valliyammai, C. (2018). Understanding Personalization of Recommender System: A Domain Perspective. International Journal of Applied Engineering Research, 13(15), 12422-12428.
Year 2017 : 1 citations
Hadžidedi?, S. (2017). Introducing emotion. Computer Science.
Year 2015 : 4 citations
Parizi, A. H., & Kazemifard, M. (2015, April). Emotional news recommender system. In 2015 Sixth International Conference of Cognitive Science (ICCS) (pp. 37-41). IEEE.
Vodyaho, A., & Zhukova, N. (2015, April). Implementation of Agile Concepts in Recommender Systems for Data Processing and Analyses. In International Conference on Analysis of Images, Social Networks and Texts (pp. 443-457). Springer, Cham.
Wakil, Karzan and Bakhtyar, Rebwar and Ali, Karwan and Alaadin, Kozhin "Improving Web Movie Recommender System Based on Emotions". Int. Journal of Advanced Computer Science and Applications (IJACSA). Vol. 6, No. 2. February, 2015. pp. 218-226
Vodyaho, Alexander, and Nataly Zhukova. "Implementation of Agile Concepts in Recommender Systems for Data Processing and Analyses." International Conference on Analysis of Images, Social Networks and Texts. Springer International Publishing, 2015.
Year 2014 : 1 citations
Tirad, Mohammed Aref Almalahmeh "Semantic recommender system for Malaysian tourism industry". Faculty of Computer Science And Information Technology, University Malaya, 2014. PhD. Thesis, 2014. p.231
