ABSTRACT
In this paper we introduce the concept of holistic recommendations, namely a set of suggestions generated by exploiting a more comprehensive representation of the user that relies on the personal information coming from different heterogeneous data sources (e.g., social networks, wristbands, smartphones, etc.) and considers the diverse relations and constraints among the data encoded in the profiles. Specifically, in this article we provide the following contributions: i) we outline a conceptual model for providing holistic recommendations built on the ground of such richer user profiles; ii) we present some challenges related to holistic recommendations that can inspire further research in the field.
- Gediminas Adomavicius, Ramesh Sankaranarayanan, Shahana Sen, and Alexander Tuzhilin. 2005. Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans. Inf. Syst. 23, 1 (January 2005), 103--145. Google ScholarDigital Library
- Debjanee Barua, Judy Kay, Bob Kummerfeld, Cecile Paris. 2014. Modelling long term goals. In Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, G. (Eds.) Proceedings of the 22nd International Conference on User Modeling, Adaptation, and Personalization, UMAP 2014, Lecture Notes Computer Science, (Vol. 8538 pp. 1--12): Springer.Google Scholar
- Debjanee Barua, Judy Kay, Bob Kummerfield, Cecile Paris. 2012. A framework for modelling goals in personal lifelong informatics. In CHI 2012 Workshop on Personal Informatics in Practice: Improving Quality of Life Through Data.Google Scholar
- Aaron Beach, Mike Gartrell, Xinyu Xing and Han Richard. 2009. SocialFusion: Context-Aware Inference and Recommendation By Fusing Mobile, Sensor, and Social Data. Computer Science Technical Reports. 988. https://scholar.colorado.edu/csci_techreports/988Google Scholar
- Claudio Biancalana, Fabio Gasparetti, Alessandro Micarelli, and Giuseppe Sansonetti 2013. An approach to social recommendation for context-aware mobile services. ACM Trans. Intell. Syst. Technol. 4, 1, Article 10 (February 2013), 31 pages. DOI: Google ScholarDigital Library
- Federica Cena, Silvia Likavec, Amon Rapp. 2018. Real World User Model: Evolution of User Modeling Triggered by Advances in Wearable and Ubiquitous Computing. State of the Art and Future Directions, Information Systems Frontiers.Google Scholar
- Hernani Costa and Luis Macedo. 2013. Emotion-based recommender system for overcoming the problem of information overload. In Corchado, et al. (Eds.) Highlights on Practical Applications of Agents and Multi-Agent Systems: Proc. of Int. Workshop PAAMS 2013 pp. 178--189.Google Scholar
- Christos Emmanouilidis, Remous-Aris Koutsiamanis, and Aimilia Tasidou. 2013. Mobile guides: Taxonomy of architectures, context awareness, technologies and applications Journal of network and computer applications 36.: 103--125. Google ScholarDigital Library
- Rui Guo, Shuangjiang Li, Li He, Wei Gao, Hairong Qi, Gina Owens. 2013. Pervasive and unobtrusive emotion sensing for human mental health. In Proceedings of the 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops 2013. pp. 436--439. Google ScholarDigital Library
- Lauma Jokste. 2015. Towards a Model of Context-Aware Recommender System. CAiSE ForumGoogle Scholar
- Lee BH., Kim HN., Jung JG., Jo GS. 2006. Location-Based Service with Context Data for a Restaurant Recommendation. In: Bressan S., Küng J., Wagner R. (eds) Database and Expert Systems Applications. DEXA 2006. Lecture Notes in Computer Science, vol 4080. Springer, Berlin, Heidelberg Google ScholarDigital Library
- Ante Odić, Marko Tkalcic, Jurij F. Tasič, Andrej Košir. 2013. Predicting and Detecting the Relevant Contextual Information in a Movie-Recommender System, Interacting with Computers, Volume 25, Issue 1, 1 January 2013, Pages 74--90.Google Scholar
- Mohamed Ramzi Haddad, Hajer Baazaoui, Djemel Ziou, Henda Ben Ghézala. 2012. Towards a new model for context-aware recommendation. Intelligent Systems (IS) 2012 6th IEEE International Conference, pp. 021-027.Google ScholarCross Ref
- Mina Razghandi, Seyyed Alireza Hashemi Golpaygani. 2017. A Context-Aware and User Behavior-Based Recommender System with Regarding Social Network Analysis, e-Business Engineering (ICEBE) 2017 IEEE 14th Int. Conference on, pp. 208--213.Google Scholar
- Saiph Savage N., Baranski M., Elva Chavez N., Höllerer T. 2012. I'm feeling LoCo: A Location Based Context Aware Recommendation System. In: Gartner G., Ortag F. (eds) Advances in Location-Based Services. Lecture Notes in Geoinformation and Cartography. Springer, Berlin, Heidelberg.Google Scholar
- Schwinger, W., Grun, Ch., Proll, B., Retschitzegger, W., Schauerhuber, A. 2005. Context- Awareness in Mobile Tourism Guides -- A Comprehensive Survey, Technical Report, Vienna University of Technology.Google Scholar
- Unger, Moshe, Ariel Bar, Bracha Shapira, and Lior Rokach. 2016. Towards latent context-aware recommendation systems. Know.-Based Syst. 104, C (July 2016), 165--178. Google ScholarDigital Library
- Markus Van Setten, Stanislav Pokraev, and Johan Koolwaaij. 2004. Context-aware recommendations in the mobile tourist application COMPASS. In International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, pp. 235--244.Google ScholarCross Ref
- Wolfang Woerndl and Georg Groh. 2007. Utilizing physical and social context to improve recommender systems. In Web Intelligence and Intelligent Agent Technology Workshops, 2007 IEEE/WIC/ACM International Conferences. pp. 123--128. Google ScholarDigital Library
- Yong Zheng, Robin Burke, Bamshad Mobasher. 2012. Differential Context Relaxation for Context-Aware Travel Recommendation. In: Huemer C., Lops P. (eds) E-Commerce and Web Technologies. EC-Web 2012. Lecture Notes in Business Information Processing, vol 123. Springer, Berlin, Heidelberg.Google Scholar
- Karl Ulrich Mayer, and Urs Schoepflin.1989. The State and the Life Course. Annual Review of Sociology Vol. 15:187--209.Google ScholarCross Ref
Index Terms
- Towards a Conceptual Model for Holistic Recommendations
Recommendations
Privacy Issues in Holistic Recommendations
UMAP'19 Adjunct: Adjunct Publication of the 27th Conference on User Modeling, Adaptation and PersonalizationIn this paper we point out some relevant issues in relation to privacy when providing holistic recommendations. We emphasize that a holistic recommender should be fair, explainable and privacy-preserving to ensure the ethicality of the recommendation ...
Latent Probabilistic Model for Context-Aware Recommendations
WI-IAT '13: Proceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 01Recommender systems (RS) are software tools that provide personalized recommendations of relevant items to individual users. However, most of them do not take into account additional contextual information that may affect user preferences, such as place,...
Naïve filterbots for robust cold-start recommendations
KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data miningThe goal of a recommender system is to suggest items of interest to a user based on historical behavior of a community of users. Given detailed enough history, item-based collaborative filtering (CF) often performs as well or better than almost any ...
Comments