Corpus ID: 60289378

Towards Recommender Engineering: tools and experiments for identifying recommender differences

@inproceedings{Ekstrand2014TowardsRE,
  title={Towards Recommender Engineering: tools and experiments for identifying recommender differences},
  author={Michael D. Ekstrand},
  year={2014}
}
Since the introduction of their modern form 20 years ago, recommender systems have proven a valuable tool for help users manage information overload. Two decades of research have produced many algorithms for computing recommendations, mechanisms for evaluating their effectiveness, and user interfaces and experiences to embody them. It has also been found that the outputs of different recommendation algorithms differ in user-perceptible ways that affect their suitability to different tasks and… Expand
Identifying Users with Atypical Preferences to Anticipate Inaccurate Recommendations
Meta-analysis of evaluation methods and metrics used in context-aware scholarly recommender systems
Diffusion filtering of graph signals and its use in recommendation systems

References

SHOWING 1-10 OF 118 REFERENCES
Collaborative Filtering Recommender Systems
User perception of differences in recommender algorithms
Shilling recommender systems for fun and profit
Evaluating collaborative filtering over time
Explaining the user experience of recommender systems
A Survey of Accuracy Evaluation Metrics of Recommendation Tasks
Hybrid Recommender Systems: Survey and Experiments
  • R. Burke
  • Computer Science
  • User Modeling and User-Adapted Interaction
  • 2004
E-Commerce Recommendation Applications
...
1
2
3
4
5
...