---
title: "Create pipeline"
url: https://docs.catalyst.zoho.com/en/tutorials/movie-recommendation/create-pipeline/
description: "Create a powerful ML pipeline that analyzed historic Movie data and gives meaningful recommendation using the Catalyst QuickML components."
last_updated: 2026-08-20T07:12:54Z
service: All Services
type: tutorial
related_links:
  - name: "Data Cleaning"
    url: /en/quickml/help/data-preprocessing/data-cleaning
  - name: "Data Transformation"
    url: /en/quickml/help/data-preprocessing/data-transformation
  - name: "Data Profiler and Viewer"
    url: /en/quickml/help/data-profiler-and-viewer/
---

# Create a pipeline

Now, follow the steps below to create a unified recommendation pipeline that integrates both data pre-processing and ML operations with the uploaded datasets.

1. Navigate to the Pipelines component in the left menu and click **Create Pipeline**.
![All Datasets](https://catalyst-docs-development.zohostratus.in/images/tutorials/movie-recommendation/create-pipeline.webp)

2. In the Create Pipeline pop-up window, choose **Recommendation** as the pipeline type.
![All Datasets](https://catalyst-docs-development.zohostratus.in/images/tutorials/movie-recommendation/pipeline-type.webp)

3. Set the pipeline name to "Movie Recommendation" and select **Interaction_dataset** as the Transaction dataset. Enable the User dataset option, choose **User_features_dataset**, enable the Item dataset option, choose **Item_features_dataset**, and then click **Create**.

> **Note:** The option to enable the User dataset and Item dataset is optional and can be enabled based on your business requirements.

![All Datasets](https://catalyst-docs-development.zohostratus.in/images/tutorials/movie-recommendation/pipeline-name.webp)

The pipeline builder interface will be opened as shown in the screenshot below.

![All Datasets](https://catalyst-docs-development.zohostratus.in/images/tutorials/movie-recommendation/pipeline-builder.webp)

You'll be performing the following set of data pre-processing and ML operations in different stages in order to clean, refine, and transform the datasets and then execute the pipeline. Each of these operations involves individual data nodes that are used to construct the pipeline.

## Data pre-processing stage

First, select the required fields in the datasets to modify them further.
Transaction dataset
1. Expand the [Data Cleaning](https://docs.catalyst.zoho.com/en/quickml/help/data-preprocessing/data-cleaning/) component in the Operations menu. Drag and drop the Select/Drop node in the pipeline builder and make a connection with the `Transaction dataset` node.
2. In the Select/Drop section in the right panel, select the columns **Unnamed** and **Timestamp**.
3. Choose the operation `Drop` to drop the columns from the Transaction dataset, then click `Save`. In our case, these columns are generic and serve no further purpose, so we're removing them.

![pre-data-processing](https://catalyst-docs-development.zohostratus.in/images/tutorials/movie-recommendation/pre-data-processing.webp)

### User Dataset

1. Expand the [Data Cleaning](https://docs.catalyst.zoho.com/en/quickml/help/data-preprocessing/data-cleaning/) component in the Operations menu. Drag and drop the Select/Drop node in the pipeline builder and make a connection with the **User dataset** node.
2. In the Select/Drop section in the right panel, select the columns **Unnamed** and **Zip-code**.
3. Choose the operation Drop to drop the columns from the User dataset, then click Save. In our case, these columns are generic and serve no further purpose, so we're removing them.

### Item Dataset

1. Expand the [Data Cleaning](https://docs.catalyst.zoho.com/en/quickml/help/data-preprocessing/data-cleaning/) component in the Operations menu. Drag and drop the Select/Drop node in the pipeline builder and make a connection with the `Item dataset` node.
2. In the Select/Drop section in the right panel, select the columns **Unnamed, movie title, release date,** and **unknown**.
3. Choose the operation `Drop` to drop the columns from the Item dataset, then click `Save`. In our case, these columns are generic and serve no further purpose, so we're removing them.

![item-dataset](https://catalyst-docs-development.zohostratus.in/images/tutorials/movie-recommendation/item-dataset.webp)

##  Encoding categorical columns stage

Since the columns Gender and Occupation in the User dataset contain categorical String-type data, we'll [One-Hot Encode](https://docs.catalyst.zoho.com/en/quickml/help/operations-in-quickml/encoding/) them to meet the requirements of standard ML training. Follow the steps below to perform the encoding.

1. Navigate to **ML tab > ML Operations > Encoding > One-Hot Encoder**. 
2. Drag and drop the `One-Hot Encoder` node into the pipeline builder interface and connect it with the `Select/Drop` node of the User dataset. 
3. In the One-Hot Encoder configuration panel on the right, select the columns **Gender** and **Occupation**, and then click `Save`.

![encoding](https://catalyst-docs-development.zohostratus.in/images/tutorials/movie-recommendation/encoding.webp)

This operation will convert each unique value in these String columns into separate binary columns (0s and 1s), ensuring the model can interpret categorical attributes without losing information or introducing unintended ordinal relationships.

## Applying ML Algorithm Stage
We are using the [Light FM](https://docs.catalyst.zoho.com/en/quickml/help/learning-center/recommendation/#evaluation-metrics-for-specific-algorithms) algorithm to build a hybrid recommendation system that combines both collaborative filtering and content-based filtering. This approach allows the model to learn from user-item interaction data (such as ratings or clicks) as well as from user and item feature data.
1. Navigate to **ML tab > ML Operations > Algorithms > Information Retrieval > Light FM**.
2. Drag and drop the `Light FM` node into the pipeline builder interface and connect it to the configured operations for the Transaction, User, and Item datasets as shown in the screenshot below. 

> **Note:**: The Light FM node will get auto-connected to the Destination node.

![light-fm](https://catalyst-docs-development.zohostratus.in/images/tutorials/movie-recommendation/light-fm.webp)

3. In the Light FM configuration panel on the right, choose the **UserID** and **MovieID** columns for the Transaction dataset, choose the **UserID** column for the User dataset, and choose the **movie id** column for the Item dataset. Set the loss function parameter to BPR, leave all other parameters unchanged, and click `Save`.

Now we've finished making the required node connections and configurations. We can execute the pipeline by clicking `Execute` for further evaluation and deployment.

![execute](https://catalyst-docs-development.zohostratus.in/images/tutorials/movie-recommendation/execute.webp)

Click `Execution Stats` to view more details about each stage of the execution in detail.

![execute-stats](https://catalyst-docs-development.zohostratus.in/images/tutorials/movie-recommendation/execute-stats.webp)

![execute-stats-preview](https://catalyst-docs-development.zohostratus.in/images/tutorials/movie-recommendation/execute-stats-preview.webp)

Upon successful pipeline execution, the Movie Recommendation model is created and will be displayed under the Models section with a **Ready** state.

![model](https://catalyst-docs-development.zohostratus.in/images/tutorials/movie-recommendation/model.webp)

You can view the details of the model on the model's Details page by clicking on the model name.

![model-stats](https://catalyst-docs-development.zohostratus.in/images/tutorials/movie-recommendation/model-stats.webp)