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User-centric evaluation of a K-furthest neighbor collaborative filtering recommender algorithm
This paper presents an inverted neighborhood model, k-Furthest Neighbors, to identify less ordinary neighborhoods for the purpose of creating more diverse recommendations and shows that even though the proposed furthest neighbor model is outperformed in the traditional evaluation setting, the perceived usefulness of the algorithm shows no significant difference in the results of the user study. Expand
Putting things in context: Challenge on Context-Aware Movie Recommendation
The Challenge on Context-Aware Movie Recommendation (CAMRa) was conducted as part of a join event at the 2010 ACM Recommender Systems conference and provided anonymized datasets from two real world online movie recommendation communities. Expand
A hybrid approach to item recommendation in folksonomies
This paper extends the probabilistic latent semantic analysis (PLSA) approach and presents a unified recommendation model which evolves from item user and item tag co-occurrences in parallel, which reduces known collaborative filtering problems related to overfitting and allows for higher quality recommendations. Expand
Comparative recommender system evaluation: benchmarking recommendation frameworks
This work compares common recommendation algorithms as implemented in three popular recommendation frameworks and shows the necessity of clear guidelines when reporting evaluation of recommender systems to ensure reproducibility and comparison of results. Expand
The Magic Barrier of Recommender Systems - No Magic, Just Ratings
The inconsistencies of the user impose a lower bound on the error the system may achieve when predicting ratings for that particular user. Expand
Group recommendation in context
An overview of the tracks and datasets of CAMRa2011 is presented, which focused on group-based recommendation for households, as well as identification of household members who had rated specific movies. Expand
Towards Health (Aware) Recommender Systems
Progress made is shown towards RS helping users find personalized, complex medical interventions or support them with preventive healthcare measures, and key challenges that need to be addressed are identified. Expand
Users and noise: the magic barrier of recommender systems
This work investigates the inconsistencies of the user ratings and estimates the magic barrier in order to assess the actual quality of the recommender system, and presents a mathematical characterization of themagic barrier based on the assumption that user ratings are afflicted with inconsistencies - noise. Expand
Increasing Diversity Through Furthest Neighbor-Based Recommendation
The experiments show that the proposed furthest neighbor method provides more diverse recommendations with a tolerable loss in precision in comparison to traditional nearest neighbor methods. Expand
Do recommendations matter?: news recommendation in real life
We present a study of how recommendations are received in real life by users across different news domains (traditional online newspapers, hobbyist websites, forums, etc.). Our analysis shows thatExpand