Understand the project
Practical overview
When it fits
Use SparkCTR when you want to explore or compare the included CTR model implementations through its Spark-based example.
Who it is for
- Developers working with Scala, Maven and Apache Spark.
- Researchers or practitioners comparing traditional and neural approaches to click-through-rate prediction.
Documented uses
- Train a selection of click-through-rate prediction models.
- Compare metrics across included model families.
- Explore Spark MLlib implementations of linear, tree-based, factorization-machine and neural models.
Before you choose
Requirements & limits
The repository is public and lists an Apache-2.0 license.
- The documented project stack is Scala 2.11, Maven and Spark 2.3.0.
- The example runs all listed CTR models and compares their metrics.
- Included approaches range from Naive Bayes and logistic regression to GBDT + LR, IPNN and OPNN.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Source-based practical guide
Train and compare the included CTR models
Run the documented model-selection example to train the included CTR models and produce a metrics comparison.
Before you start
- Maven project dependencies imported by Maven
- Spark 2.3.0
- Scala 2.11
Steps
Obtain the SparkCTR Maven project.
Allow Maven to import the project dependencies.
Run the example function com.ggstar.example.ModelSelection.
Expected outcome
The example is described as training all included CTR models and providing metrics comparison among them.
Guide limitations
- The README describes this example workflow but does not specify input-data preparation or a production deployment procedure.
Evidence and transparency
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 5 November 2026
- Original website title
- wzhe06/SparkCTR · Official homepage title · retained in its source language