Feedback-based Approach to Introduce Freshness in Recommendations
Recommender systems usually face the problem of serving the same recommendations across multiple sessions regardless of whether the user is interested in them or not, thereby reducing their effectiveness. To add freshness to the recommended products, we introduce a feedback loop where the set of recommended products in the current session depend on the user's interaction with the previously recommended sets. We also describe ways of addressing freshness when there is little or even no direct user interaction. We define a metric to quantify freshness by reducing the problem to measuring temporal diversity.
Code (0)
등록된 구현이 없습니다.
Tasks
DiversityRecommendation SystemsSimilar Papers 제목 키워드 기반
Freshness or Accuracy, Why Not Both? Addressing Delayed Feedback via Dynamic Graph Neural Networks
The delayed feedback problem is one of the most pressing challenges in predicting the conversion rate since users' conversions are always delayed in online commercial systems. Although new data are beneficial for continu…
Graph Neural NetworkA Survey of Freshness-Aware Wireless Networking with Reinforcement Learning
The age of information (AoI) has become a central measure of data freshness in modern wireless systems, yet existing surveys either focus on classical AoI formulations or provide broad discussions of reinforcement learni…
Reinforcement LearningTrajectory PlanningInference Time Feature Injection: A Lightweight Approach for Real-Time Recommendation Freshness
Many recommender systems in long-form video streaming reply on batch-trained models and batch-updated features, where user features are updated daily and served statically throughout the day. While efficient, this approa…
Freshness-Aware Thompson Sampling
To follow the dynamicity of the user's content, researchers have recently started to model interactions between users and the Context-Aware Recommender Systems (CARS) as a bandit problem where the system needs to deal wi…
Recommendation SystemsThompson SamplingMovie Recommendation using Web Crawling
In today's digital world, streaming platforms offer a vast array of movies, making it hard for users to find content matching their preferences. This paper explores integrating real time data from popular movie websites …
Collaborative FilteringMovie Recommendation