A Preference-Guided Multi-Objective Recommendation Algorithm with an Adaptive Tchebycheff Environmental Selection

Traditional recommendation algorithms primarily focus on accuracy, often overlooking diversity and long-tail item recommendations, which are inherently conflicting objectives. Balancing these objectives remains a significant challenge. To address this issue, we propose refined objective functions for diversity and long-tail item recommendation, aiming to more accurately evaluate the diversity of recommendation lists and improve the exposure of long-tail items. To solve the resulting multi-objective optimization problem, we introduce a preference-guided multi-objective recommendation algorithm incorporating an adaptive Tchebycheff environmental selection strategy. This algorithm combines a preference-guided local search to enhance solution quality with an adaptive Tchebycheff selection mechanism that dynamically adjusts objective weights to improve both convergence and stability. Experimental results demonstrate that the proposed algorithm effectively balances accuracy, diversity, and long-tail item recommendation.