Why is this ‘program recommended’ to me?
Exploring Recommendation System Algorithms and Applications: A recommendation system is an algorithmic solution that suggests additional products and services to consumers. These systems help users discover products and services that may not have otherwise been found.
There are three main types of recommendation systems: content-based, collaborative, and hybrid. Content-based builds on the features that explain the user-item interactions. Content-based contrasts with collaborative filtering which is focused on user preferences. The hybrid approach computes the similarity between distinct items by combining user and item-based approaches.
Prominent recommendation systems such as Netflix, Amazon, and LinkedIn, each use the power of AI algorithms to make accurate recommendations based on user’s view history, prior purchases, and skills and career history, respectively.
Improve User Acceptance of Recommendations
Recommendation systems experience a loss of user engagement when they produce unexpected results, empty recommendations, or irrelevant results that are without explanation. However, instant verification through explicit connections from recommendation to the underlying source data solves this critical problem. Content-based solutions built with knowledge graphs that work with the item, user profiles, and purchase histories deliver personalized and verifiable recommendations. Such recommendations are likely to be readily accepted by consumers.
TextDistil powers Verifiable Recommendations for the Media vertical
TextDistil Recommendations (TR) is a GenAI (LLM) and Knowledge Graph based solution that generates accurate recommendations predicted by advanced ML algorithms and supported by the entities, features, and relationships that are found in the content of the program. TR provides an instant verification of each recommendation by highlighting the relevant connections in the Knowledge Graph.
Conclusion: The blog explores recommendation systems’ importance and types, emphasizing AI’s role. Users prefer verifiable recommendations, achieved through instant verification. TextDistil Recommendations utilizes ML algorithms and knowledge graphs, ensuring accurate suggestions. Examples include Netflix, Amazon, and LinkedIn, showcasing AI’s impact on personalized experiences.
Send us an email or contact us to check out the TextDistil Recommendations solution for your production needs.
