Content suppression mechanisms-based recommendation systems

Personalized recommendation systems enhance user experiences but often lead to information filter bubbles and reduced content diversity. To address these issues, this paper introduces a novel recommendation system based on Content Suppression Mechanisms, centered around Restrain Gated Recurrent Units (RGRU). The core innovation lies in a suppression function that dynamically adjusts the likelihood of recommending items based on their similarity to previously viewed content. This approach effectively mitigates the repetition of similar items andpromotes diversity within recommendation lists. Furthermore, we introduce a novel evaluation metric, the External and Intra-List Similarity ($E\&ILS$), designed to assess both the internal diversity of recommended items and their deviation from previous interactions of users. This metric improves upon existing diversity metrics by addressing systemic diversity and variations among user groups.Validation across KuaiRec, ml_25m, and MRM datasets demonstrates that our approach maintains high precision while significantly enhancing recommendation diversity. This dual improvement facilitates multi-perspective content exploration, mitigating information cocoons and elevating user experience.