Content-Based Personalized Recommender System Using Entity Embeddings
Recommender systems are a class of machine learning algorithms that provide relevant recommendations to a user based on the user's interaction with similar items or based on the content of the item. In settings where the content of the item is to be preserved, a content-based approach would be beneficial. This paper aims to highlight the advantages of the content-based approach through learned embeddings and leveraging these advantages to provide better and personalised movie recommendations based on user preferences to various movie features such as genre and keyword tags.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningEntity EmbeddingsRecommendation SystemsSimilar Papers 제목 키워드 기반
PMG : Personalized Multimodal Generation with Large Language Models
The emergence of large language models (LLMs) has revolutionized the capabilities of text comprehension and generation. Multi-modal generation attracts great attention from both the industry and academia, but there is li…
multimodal generationReading ComprehensionRecommendation SystemsEnd-to-End Personalization: Unifying Recommender Systems with Large Language Models
Recommender systems are essential for guiding users through the vast and diverse landscape of digital content by delivering personalized and relevant suggestions. However, improving both personalization and interpretabil…
Collaborative FilteringSimplifying Content-Based Neural News Recommendation: On User Modeling and Training Objectives
The advent of personalized news recommendation has given rise to increasingly complex recommender architectures. Most neural news recommenders rely on user click behavior and typically introduce dedicated user encoders t…
News RecommendationPersonalized Counterfactual Fairness in Recommendation
Recommender systems are gaining increasing and critical impacts on human and society since a growing number of users use them for information seeking and decision making. Therefore, it is crucial to address the potential…
counterfactualDecision MakingFairnessRecommendation SystemsEffects of Foraging in Personalized Content-based Image Recommendation
A major challenge of recommender systems is to help users locating interesting items. Personalized recommender systems have become very popular as they attempt to predetermine the needs of users and provide them with rec…
Recommendation Systems