Focus on user micro multi-behavioral states: Time-sensitive User Behavior Conversion Prediction and Multi-view Reinforcement Learning Based Recommendation Approach

Abstract

In recommender systems, user behavior conversion implies user interest drifts and behavior patterns. However, current research has paid little attention to the correlation between target behavior conversion rate and user behavior patterns, and the impact of highly time-sensitive multi-behavior analysis on target behavior conversion rate is neglected. Meanwhile, compared to normal behavior conversions, user deviant behavior conversions are seldom studied. The behavior conversion rate that balances normal behavior patterns and deviant behavior patterns can more accurately reflect user interest drifts and real-time needs, thereby improving recommendation performance. Based on the above motivations, we propose a Time-sensitive Behavior Conversion Prediction and Multi-view Reinforcement Learning Based Recommendation Approach (TCMR), aiming to achieve more accurate and adaptive recommendations by analyzing user interest drifts, demand timings and behavior stability. First, we construct a hyper-behavior spatial model of highly collaborative temporal signals, and propose a subnet collaborative method to obtain normal behavior patterns, in which, core subnet, similarity subnet and behavior subnet are extracted from the hyper-behavior spatial model. Subsequently, we design a multi-level user behavior trajectory tree to perceive potential user deviant behaviors by comparing behavior conversions within the single behavior modality and across different behavior modality. By integrating normal behaviors and deviant behaviors, we evaluate user interest drifts, demand timings, and behavior stability, and ultimately obtain a prediction of behavior conversion rate. Finally, a multi-perspective asynchronous reinforcement learning is proposed, enabling TCMR to provide recommendations by considering multiple user perspectives and purposes. Experimental results demonstrate that TCMR exhibits superior recommendation performance and effectiveness.