Difference between revisions of "MyMediaLite/Workshop projects"
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== Parallelization == | == Parallelization == | ||
* [https://github.com/zenogantner/MyMediaLite/issues/73 parallel similarity computation] | * [https://github.com/zenogantner/MyMediaLite/issues/73 parallel similarity computation] | ||
| − | * | + | * [https://github.com/zenogantner/MyMediaLite/issues/232 'naive' parallel learning for BPR-MF] |
| − | == | + | == Correlations == |
| − | * [ | + | * [[MyMediaLite/Dice and Tyversky|Dice and Tyversky]] |
| − | |||
* [https://github.com/zenogantner/MyMediaLite/issues/222 Jaccard index for binary feedback data] | * [https://github.com/zenogantner/MyMediaLite/issues/222 Jaccard index for binary feedback data] | ||
* similarity based on the [[Euclidean distance]] | * similarity based on the [[Euclidean distance]] | ||
* [https://github.com/zenogantner/MyMediaLite/issues/68 factor-based similarity] | * [https://github.com/zenogantner/MyMediaLite/issues/68 factor-based similarity] | ||
| − | + | ||
| − | + | == Algorithms == | |
* [https://github.com/zenogantner/MyMediaLite/issues/43 determine the learn rate for SGD] | * [https://github.com/zenogantner/MyMediaLite/issues/43 determine the learn rate for SGD] | ||
* [https://github.com/zenogantner/MyMediaLite/issues/207 SGD learning for WRMF] | * [https://github.com/zenogantner/MyMediaLite/issues/207 SGD learning for WRMF] | ||
* [https://github.com/zenogantner/MyMediaLite/issues/82 ALS-based matrix factorization] | * [https://github.com/zenogantner/MyMediaLite/issues/82 ALS-based matrix factorization] | ||
| + | * [https://github.com/zenogantner/MyMediaLite/issues/269 fold-in for WRMF] | ||
| + | * [https://github.com/zenogantner/MyMediaLite/issues/120 BPR-kNN] | ||
| + | * [https://github.com/zenogantner/MyMediaLite/issues/244 WRMF with item bias] | ||
* [https://github.com/zenogantner/MyMediaLite/issues/135 k-means clustering of users and/or items] | * [https://github.com/zenogantner/MyMediaLite/issues/135 k-means clustering of users and/or items] | ||
* [https://github.com/zenogantner/MyMediaLite/issues/125 Eigentaste 5.0] | * [https://github.com/zenogantner/MyMediaLite/issues/125 Eigentaste 5.0] | ||
| Line 43: | Line 45: | ||
== Under the Hood == | == Under the Hood == | ||
| − | * | + | * [https://github.com/zenogantner/MyMediaLite/issues/60 experiment with Math.Net Numerics matrix data types] |
== Compatibility == | == Compatibility == | ||
Latest revision as of 13:24, 12 September 2012
Some projects you could work on.
Contents
Visualization
- evaluation graphs
- dataset visualization
Input Formats
- SQLite
Parallelization
Correlations
- Dice and Tyversky
- Jaccard index for binary feedback data
- similarity based on the Euclidean distance
- factor-based similarity
Algorithms
- determine the learn rate for SGD
- SGD learning for WRMF
- ALS-based matrix factorization
- fold-in for WRMF
- BPR-kNN
- WRMF with item bias
- k-means clustering of users and/or items
- Eigentaste 5.0
- integrating TrueSkill
- your favorite algorithm
- some algorithm from this year's conference or its workshops
Evaluation
- expected reciprocal rank
- Kendall's Tau
- Spearman rank coefficient
- add fold-in evaluation to the command-line tools
- your favorite missing evaluation protocol or measure
Item Recommendation
Under the Hood
Compatibility
- Comparing abstractions and APIs in LensKit, Mahout, and MyMediaLite
- data models and storage
- rating scales
- recommenders
- training and updates
- correlations and similarities
- evaluation protocols
- handling of attributes and relations
- portability and interfacing
