International Journal of Advanced Research in Computer and Communication Engineering

Abstract: The selection of foods is highly determined by the emotional and affective conditions of an individual, however prevailing food-recommendation systems are more concerned with the fixed preferences of the users, limitations of food intake or what has been previously consumed without taking into account the dynamic effect of mood [1][5]. In order to overcome this shortcoming, this paper introduces a Mood Adaptive Food Recommendation System which combines affective state analysis method with content-based filtering methods to produce personalized and context sensitive food recommendation [1]. The first step of the proposed framework is to recognize the current mood of the user with the help of affective characteristics based on self- reporting or emotion classification models [5]. The content-based filtering module uses food qualities like ingredients, nutritional value [5], the type of cuisine and health considerations to provide recommendations that are in line with the mood detected and individual food preferences [2]. In contrast to collaborative methods, the given method is not based upon a significant portion of user interaction data, which is effective in cold-start cases [6]. Temporal awareness is also integrated into the system to change the recommendation based on the shifting emotional patterns with time [3][4].

Index Terms: Mood Adaptive Recommendation, Food Recommendation System, Affective State Analysis, Content-Based Filtering, Emotion-Aware Personalization, Temporal Adaptation, Personalized nutrition, Affective computing, cold-start problem, Context-Aware Recommendation.

Downloads: | DOI: 10.17148/IJARCCE.2026.15237