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https://github.com/mathetake/intergo
A package for interleaving / multileaving ranking generation in go
https://github.com/mathetake/intergo
ab-testing go golang information-retrieval interleaving multileaving ranking ranking-algorithm recommendation-system
Last synced: 6 days ago
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A package for interleaving / multileaving ranking generation in go
- Host: GitHub
- URL: https://github.com/mathetake/intergo
- Owner: mathetake
- License: mit
- Created: 2018-08-08T06:34:42.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2019-07-18T01:21:37.000Z (almost 5 years ago)
- Last Synced: 2024-02-29T22:35:28.850Z (4 months ago)
- Topics: ab-testing, go, golang, information-retrieval, interleaving, multileaving, ranking, ranking-algorithm, recommendation-system
- Language: Go
- Homepage:
- Size: 112 KB
- Stars: 31
- Watchers: 5
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Lists
- awesome-stars - mathetake/intergo - A package for interleaving / multileaving ranking generation in go (Go)
README
# intergo
[](https://circleci.com/gh/mathetake/intergo)
[](LICENSE)
[](http://godoc.org/github.com/mathetake/intergo)
A package for interleaving / multileaving ranking generation in go
It is mainly tailored to be used for generating interleaved or multileaved ranking based on the following algorithm
- Balanced Interleaving/Multileaving (in `github.com/mathetake/itergo/bm` package)
- Greedy Optimized Multileaving (in `github.com/mathetake/intergo/gom` package)
- Team Draft Interleaving/Multileaving (in `github.com/mathetake/itergo/tdm` package)
__NOTE:__ this package aims only at generating a single combined ranking and does not implement the evaluation functions of the given rankings.
## Usage
Make sure that all of your rankings implement `intergo.Ranking` interface defined in `intergo.go`
```go
package intergo
type ID string
type Ranking interface {
GetIDByIndex(int) ID
Len() int
}
```
Then choose one of `bm` or `gom` or `tdm` package which corresponds to the algorithm you want to use.
In each of these packages, there is a type which implements `intergo.Interleaving` interface defined in `intergo.go`,
```go
package intergo
type Result struct {
RankingIndex int
ItemIndex int
}
type Interleaving interface {
GetInterleavedRanking(num int, rankings ...Ranking) ([]*Result, error)
}
```
and you can generate interleaved/multileaved ranking by calling `GetInterleavedRanking`.
The following is an example using Team Draft MultiLeaving (implemented in `tdm` package)
```go
package main
import (
"fmt"
"strconv"
"github.com/mathetake/intergo"
"github.com/mathetake/intergo/tdm"
)
type tRanking []int
func (rk tRanking) GetIDByIndex(i int) intergo.ID {
return intergo.ID(strconv.Itoa(rk[i]))
}
func (rk tRanking) Len() int {
return len(rk)
}
// tRanking implements intergo.Ranking interface
var _ intergo.Ranking = tRanking{}
func main() {
ml := &tdm.TeamDraftMultileaving{}
rankingA := tRanking{1, 2, 3, 4, 5}
rankingB := tRanking{10, 20, 30, 40, 50}
idxToRk := map[int]tRanking{
0: rankingA,
1: rankingB,
}
res, _ := ml.GetInterleavedRanking(4, rankingA, rankingB)
iRanking := tRanking{}
for _, it := range res {
iRanking = append(iRanking, idxToRk[it.RankingIndex][it.ItemIndex])
}
fmt.Printf("Result: %v\n", iRanking)
}
```
## References
1. Radlinski, Filip, Madhu Kurup, and Thorsten Joachims. "How does clickthrough data reflect retrieval quality?." Proceedings of the 17th ACM conference on Information and knowledge management. ACM, 2008.
2. Schuth, Anne, et al. "Multileaved comparisons for fast online evaluation." Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. ACM, 2014.
3. Manabe, Tomohiro, et al. "A comparative live evaluation of multileaving methods on a commercial cqa search." Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2017.
4. Kojiro Iizuka, Takeshi Yoneda, Yoshifumi Seki. "Greedy Optimized Multileaving for Personalization." Proceedings of the 13th International ACM Conference on Recommender Systems. ACM, 2019.
## Author
- [@koiizukag](https://github.com/koiizukag)
- [@mathetake](https://twitter.com/mathetake)
## license
MIT