Safety in Recommender Systems

--Ongoing Project

Safety in Recommender Systems: Mitigating Sensitive Content Recommendations In an increasingly digital world, recommender systems play a pivotal role in guiding user choices across various platforms. However, the issue of inadvertently recommending sensitive or inappropriate content remains a critical concern. My ongoing project delves into enhancing the safety of recommender systems by actively addressing this concern.

Key Objectives:

Sensitive Content Recognition: Investigating how recommender systems can be trained to recognize and categorize sensitive content or topics based on predefined criteria.

User-Defined Filters: Designing mechanisms that allow users to define and communicate their preferences regarding sensitive categories, ensuring these preferences are integrated into the recommendation process.

Adaptive Learning: Exploring machine learning approaches to dynamically adapt and improve the recommendation model based on user feedback and evolving definitions of sensitivity.

By working towards these objectives, we aim to create a safer online experience for users by empowering them with the ability to shape and refine the recommendations they receive, thereby fostering a more responsible and considerate online environment.