Paper Info
Title
User's activity driven short-term context inference
Abstract
Customer decision making process is not invariant. Actual circumstances have a great influence on user's preference adjustments, therefore an absence of incorporating contextual information leads to sub-optimal prediction performance. A popular approach in recommender systems is to treat a context as a set of identifiable and observable attributes while assuming their full separability from an activity. In contrast, we believe that the context emerges from the activity and its change can be perceived and possibly predicted by using mined patterns of its evolution on multiple levels, starting at individual sessions. This paper presents concepts, ideas and motivation for our PhD research project.
Year
DOI
Venue
2019
10.1145/3298689.3346950
Proceedings of the 13th ACM Conference on Recommender Systems
Keywords
Field
DocType
context-aware systems, interactional context, preference evolution, sequence-aware systems
Inference,Computer science,Artificial intelligence,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4503-6243-6
0
0.34
References 
Authors
0
1
Name
Order
Citations
PageRank
Miroslav Rac100.68