Citation : Liu, J., Dolan, P. and Pederson, E.R. Personalized news recommendation based on click behavior. Google Inc. Available at: https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/35599.pdf
For this assignment, we will using Google News as our topic of discussion for recommender systems.
A recommender system is a technology to information filtering system that predicts the preferences or ratings that a user would give to an item. It is commonly used in e-commerce, social media, and other web applications to suggest products, movies, music, or other items of interest to users. A recommender system is designed to help users discover new items or products that they may like based on their past behavior or preferences. Google News is a personalized news dashboard that uses machine learning algorithms to recommend news articles and topics to its users based on their interests and behavior. Google News collects information about the user’s reading history, location, search history, and other relevant data to deliver a personalized news feed. By using a recommender system, Google News aims to deliver a relevant and engaging news experience to its users while saving them time and effort in finding the articles that interest them the most.
Google News aims to serve a broad range of users who are interested in staying up-to-date with the latest news and information. The target users range from casual readers who want to stay informed about current events to professionals who rely on the latest news and information for their work. Google News can be tailored to a user’s preferences based on their interests and search history, making it a versatile platform that can appeal to a wide range of users. The platform is available globally, which means that it can cater to users from different backgrounds and cultures. The user base for Google News is diverse, ranging from students, professionals, journalists, academics, researchers, and many more. Therefore, the platform needs to cater to the diverse needs and interests of its target users.
In addition to keeping users informed and saving them time, another key goal for Google News is to provide users with a seamless and personalized news experience. They aim to do this by using machine learning algorithms to understand users’ reading habits, interests, and preferences, and then delivering tailored news content to each individual user. By achieving this goal, Google News hopes to not only provide users with relevant news content, but also to create a more engaging and enjoyable experience for them. Additionally, Google News aims to build trust with its users by providing accurate and reliable news content from reputable sources.
One way to help them acheive their goals is to present the news in a clean and easy to read format the helps the users to quickly scan headlines and access the articles when desired. Another way, is to improve the recommendation algorithm based on their preferences to better cater to their interests.
Once Google starts collecting your data through your click history, it uses an algorithm to personalize the news feed according to your preferences. This algorithm helps to create a unique experience for each user, with a customized feed that shows news stories based on their interests. The algorithm analyzes a user’s click behavior and other interactions with the news stories to predict what other topics or stories that user might be interested in. The more information Google has, the more accurate their models can predict what future headline might interest you. This process only happens if you sign in with a google account in Google News and enabling webhistory in your browser settings, otherwise it will not collect your clicking data. Google News also provides users with the ability to customize their news feed by selecting specific topics, sources, and points of interest. This feature allows users to further refine their news feed and make it more personalized to their specific interests.