Context-Aware Recommender Systems 2012 (In conjunction with the 6th ACM Conference on Recommender Systems (RecSys 2012))
I usually think of recommender systems as attempts to deliver content based on clues about my interests or context. If I dial 911, the location of the nearest pizza vendor probably isn’t high on my lists of interests, etc.
As I looked over these proceedings, it occurred to me that subject identity, for merging purposes, isn’t limited to the context of the subject in question.
That is some merging tests could depend upon my context as a user.
Take my 911 call for instance. For many purposes, a police substation, fire station, 24 hour medical clinic and a hospital are different subjects.
In a medical emergency situation, for which a 911 call might be a clue, all of those could be treated as a single subject – places for immediate medical attention.
What other subjects do you think might merge (or not) depending upon your context?
Table of Contents
- Preface
Gediminas Adomavicius, Linas Baltrunas, Ernesto William de Luca, Tim Hussein, Alexander Tuzhilin.- Contextualizing Recommendations (keynote)
Francesco Ricci
- Optimal Feature Selection for Context-Aware Recommendation Using Differential Relaxation
Yong Zheng, Robin Burke, Bamshad Mobasher.- Relevant Context in a Movie Recommender System: Users’ Opinion vs. Statistical Detection
Ante Odic, Marko Tkalcic, Jurij Franc Tasic, Andrej Kosir.- Improving Novelty in Streaming Recommendation Using a Context Model
Doina Alexandra Dumitrescu, Simone Santini.- Towards a Context-Aware Photo Recommender System
Fabricio Lemos, Rafael Carmo, Windson Viana, Rossana Andrade.- Context and Intention-Awareness in POIs Recommender Systems
Hernani Costa, Barbara Furtado, Durval Pires, Luis Macedo, F. Amilcar Cardoso.- Evaluation and User Acceptance Issues of a Bayesian-Classifier-Based TV Recommendation System
Benedikt Engelbert, Karsten Morisse, Kai-Christoph Hamborg.- From Online Browsing to Offline Purchases: Analyzing Contextual Information in the Retail Business
Simon Chan, Licia Capra.