AI-Powered News Article Recommendation System
News Article Recommendation Systems are at the forefront of modern journalism, leveraging cutting-edge technology to deliver personalized and engaging content to readers. These systems are powered by a complex blend of algorithms, data analytics, and machine learning techniques to sift through vast amounts of news articles and curate a tailored newsfeed for each user.
By analyzing a user’s historical reading habits, preferences, and behavior, these systems aim to deliver content that matches individual interests while also ensuring exposure to a diverse range of topics.
At the core of a news recommendation system lies the art of content filtering and recommendation algorithms. These systems employ natural language processing (NLP) to understand the content of news articles and match them with users’ profiles.
Collaborative filtering techniques are often used to identify patterns in user behavior and recommend news articles that users with similar preferences have found interesting. Additionally, machine learning models continuously adapt to changing user interests, making real-time predictions about the relevance of news articles.
Ethical considerations, transparency, and user privacy are essential aspects of these systems, as they navigate the delicate balance between personalization and the need for unbiased, reliable news delivery in the digital age.
Getting Started
In the last blog post, I talked about the House Recommendation System for Nearby Homes where I mentioned a content based recommendation system to recommend similar houses.
Today we will discover how we can recommend news articles based on topic modeling and Latent Dirichlet Allocation (LDA) methods.
Latent Dirichlet Allocation (LDA) and topic modeling have gained prominence as powerful tools for enhancing recommendation systems. LDA, originally developed for uncovering latent topics in text data, can be leveraged in recommendation systems to provide users with personalized content suggestions that align with their interests and preferences.
LDA-based recommendation systems work by extracting latent topics from the textual content associated with items or user interactions. Instead of relying solely on explicit user-item interactions (such as ratings or clicks), these systems consider the semantic context of the items.
LDA identifies the underlying themes or topics within the items and user profiles, allowing the system to recommend items that share thematic similarities. For instance, in a news recommendation system, LDA can discover topics like “politics,” “technology,” or “sports,” and then recommend articles to users based on their historical interests in these topics.
This approach enhances the quality of recommendations by capturing the nuances of user preferences and the content’s intrinsic characteristics, making it especially valuable in scenarios where user-item interactions are sparse or unavailable.
Topic Modelling And Latent Dirichlet Allocation (LDA)
Topic modeling is a crucial technique in the field of natural language processing and text analysis, offering a powerful way to uncover hidden thematic structures within large collections of textual data.
One of the most widely used methods for topic modeling is Latent Dirichlet Allocation (LDA). LDA is a probabilistic model that aims to discover topics from a corpus of documents and assign each document a distribution over these topics.
It assumes that documents are composed of a mixture of topics, and words in the documents are generated based on these topic proportions.
At its core, LDA operates by iteratively estimating two key components: the topic distributions for each document and the word distributions for each topic.
Through this iterative process, LDA identifies patterns in word co-occurrence and infers meaningful topics that represent clusters of related words.
This technique has numerous applications, ranging from text summarization and information retrieval to content recommendation and sentiment analysis. It has proven invaluable in extracting insights from unstructured textual data, making it an essential tool for understanding and organizing large volumes of text in various fields, including academia, industry, and social media analysis.
Exploring Ktrain Library
Ktrain is an open-source Python library designed to simplify the process of implementing deep learning models with the popular deep learning framework, Keras, and the machine learning library, scikit-learn.
It is particularly well-suited for tasks related to natural language processing (NLP) and text classification, but it can be used for a wide range of machine learning tasks. Ktrain was developed to make it easier for both beginners and experienced data scientists to build, train, and deploy deep learning models.
One of the standout features of Ktrain is its high-level interface for training deep learning models. It provides a simple API that abstracts many of the complexities associated with designing and training neural networks, making it accessible to those who may not have extensive experience in deep learning.
Ktrain also includes a set of pre-processing utilities and integration with popular pre-trained embeddings like Word2Vec, GloVe, and BERT, further simplifying the development of NLP models. Additionally, it offers support for tasks such as text classification, text regression, and sequence labeling, among others.
Overall, Ktrain is a valuable tool for rapidly prototyping and deploying deep learning models for various machine learning applications.
Dataset
Data is scraped from several news providers to get news information such as Title & Content. It is then processed by ScrapeHero’s Machine Learning algorithm to get Sentiment of the article, Category of the News etc.
If you want to scrape news, use ScrapeHero News API to get News Sources, Date, Sentiment, categories, or any other search keywords.
Find the other listed APIs on Scrapehero Cloud.
Sample Dataset:
Columns: Date, Sentiment, Title, Content, Parent Classification and Child Classification
Requirements
pip install numpy pandas ktrain
Topic Modeling:
We will be using the content of the article to recommend similar articles. First we will be extracting the topics from the article and then running the similarity or nearest neighbor approach on the selected cluster of topics.
Now we are going to represent our data as meaningful vectors using LDA Topic Modeling.
data = df.content
tm = ktrain.text.get_topic_model(data, n_features=10000)To cut out the low probability matches, we set a threshold of 75%.
tm.build(data, threshold=0.75)Save and Load the Topic Model
# Save the topic model
tm.save(‘/topic_modeling_news’)# Load the model
tm = ktrain.text.load_topic_model(‘/topic_modeling_news’)Train Recommendation Model
tm.train_recommender(metric= “minkowski”)Results
article = data[0]A Sulphur man was killed Wednesday night in a two-vehicle accident in the 3400 block of Edgerly Road in DeQuincy.
Calcasieu Parish Sheriff’s Office spokesperson Kayla Vincent said an SUV was traveling northbound on Edgerly Road at about 6:15 p.m. when for unknown reasons it crossed the center line while approaching a curve. The SUV struck a truck that was headed southbound, head-on.
The driver of the SUV, Gay L. Dugas, 65, who was not wearing a seatbelt, was pronounced dead at the scene, Vincent said.
The driver of the truck, who was wearing a seatbelt, suffered minor injuries.
As mandated by state law a toxicology report will be conducted on both drivers, although impairment is not suspected.Recommendations:
recommendations = tm.recommend(text= article, n=5)for idx, recommend_text in enumerate(recommendations):
print(f”Recommended News {idx+1}: {recommend_text[‘text’]}”)
You can clearly see the great results from this method! 😍
Conclusion
News recommendation systems powered by topic modeling, such as Latent Dirichlet Allocation (LDA), have revolutionized the way we consume news.
These systems excel at understanding the underlying themes and content structures within news articles, enabling them to deliver personalized and relevant news feeds to users.
By employing techniques like LDA, these systems enhance user engagement and satisfaction by ensuring that content aligns with individual interests and preferences, even in cases where explicit user-item interactions are limited.
However, the field of news recommendation is continually evolving, and the quest for more advanced techniques is ongoing. Incorporating cutting-edge deep learning techniques can further elevate the quality of recommendations.
Hope you learned something new today, Happy Learning!
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