Indirect Context Suggestion
| Title | Indirect Context Suggestion |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Authors | Zheng, Yong |
| Conference Name | Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-4635-1 |
| Keywords | Context, context suggestion, Human Behavior, human factors, pubcrawl, recommender systems, resilience, Resiliency, Scalability |
| Abstract | Context suggestion refers to the task of recommending appropriate contexts to the users to improve the user experience. The suggested contexts could be time, location, companion, category, and so forth. In this paper, we particularly focus on the task of suggesting appropriate contexts to a user on a specific item. We evaluate the indirect context suggestion approaches over a movie data collected from user surveys, in comparison with direct context prediction approaches. Our experimental results reveal that indirect context suggestion is better and tensor factorization is generally the best way to suggest contexts to a user when given an item. |
| URL | https://dl.acm.org/citation.cfm?doid=3079628.3079654 |
| DOI | 10.1145/3079628.3079654 |
| Citation Key | zheng_indirect_2017 |