Incorporating Negative Feedback to Improve Recommendation Quality

Recommender systems traditionally focus on positive user interactions, such as purchases or likes, often overlooking the valuable information contained in negative feedback signals—such as dislikes, skips, or low ratings. This thesis explores approaches to incorporate negative feedback into user modeling and recommendation algorithms. We aim to analyze datasets containing explicit or implicit negative feedback and implement baseline models that integrate these signals through penalization or preference ranking methods.