Digital Publishing

Personalizing Content in the Digital Publishing Industry with AI-Driven Recommender Systems

Personalizing Content in the Digital Publishing Industry with AI-Driven Recommender Systems
Photo by Chris Hainto / Unsplash

In this case study  identified a crucial user experience challenge in the digital publishing industry, specifically within online reading platforms. With an exponentially increasing volume of digital content, users often face decision paralysis due to an overwhelming array of reading options. This scenario hinted at the potential benefits of AI-driven personalized recommender systems, encouraging us to probe further.

Equipped with expertise in AI, machine learning, and big data analytics, we embarked on an independent project to construct a recommender system using collaborative filtering and deep learning methodologies. We realized that such a project would necessitate an in-depth understanding of user reading habits and an extensive dataset.

We leveraged a public dataset containing user reading habits, book metadata, and user ratings, storing the raw data in an AWS S3 bucket. The next step involved pre-processing and cleaning the data. We employed PySpark, a Python library for Apache Spark, to handle these large-scale data processing tasks.

A simple PySpark code snippet for data cleaning is shown below:

from pyspark.sql import SparkSession
from pyspark.sql.functions import col, when

spark = SparkSession.builder.appName('DataCleaning').getOrCreate()

# Load data from S3 bucket
df = spark.read.json("s3://bucket-name/data.json")

# Replace null values
df = df.withColumn("user_rating", when(col("user_rating").isNull(), 3).otherwise(col("user_rating")))

# Save cleaned data back to S3
df.write.json("s3://bucket-name/cleaned_data.json")

We implemented a hybrid recommender system, combining collaborative filtering and deep learning-based approaches, using the Python library Surprise for collaborative filtering and TensorFlow for deep learning.

Here is a simplified code sample of the collaborative filtering process using Surprise:

from surprise import SVD
from surprise import Dataset
from surprise import accuracy
from surprise.model_selection import train_test_split

# Load the dataset 
data = Dataset.load_builtin('ml-100k')

# Split the dataset into training and test set
trainset, testset = train_test_split(data, test_size=.25)

# Apply SVD
algo = SVD()

# Train the algorithm on the trainset
algo.fit(trainset)

# Predictions
predictions = algo.test(testset)

# Then compute RMSE
accuracy.rmse(predictions)

Our model’s performance was continually monitored and updated using AWS CloudWatch, ensuring that the model remained accurate as new data was integrated.

This endeavor led to the creation of a robust, AI-driven personalized recommender system, capable of effectively curating reading suggestions based on individual user preferences. This not only demonstrated the potential of AI in enhancing user experience in the digital publishing industry but also added a significant case study to our growing portfolio of innovative solutions.

New Collar continues to break ground, leveraging cutting-edge technologies, conducting autonomous research, and creating data-driven solutions, ensuring we remain at the forefront of the digital transformation journey.

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