Improvement of marketing communications methods and strategy of the building decoration industry enterprises in China

This work takes China Construction Oriental Decoration Co., Ltd. as an example to conduct targeted research, provides rationalized suggestions for the company's development, formulates marketing strategies, and helps company to achieve long-term healthy development goals. With the changes in the market situation, problems such as outdated business model, low resource concentration and imperfect pricing mechanism of the company began to appear, which seriously hindered the future development of the company. Through the analysis of the current situation and internal and external environment of China Construction Oriental Decoration Co., Ltd., this paper summarizes the company's existing problems in marketing, formulates a marketing strategy that meets its future development needs, and implements safeguards such as strengthening corporate culture construction

AdaRec: Adaptive Sequential Recommendation for Reinforcing Long-term User Engagement

Growing attention has been paid to Reinforcement Learning (RL) algorithms when optimizing long-term user engagement in sequential recommendation tasks. One challenge in large-scale online recommendation systems is the constant and complicated changes in users' behavior patterns, such as interaction rates and retention tendencies. When formulated as a Markov Decision Process (MDP), the dynamics and reward functions of the recommendation system are continuously affected by these changes. Existing RL algorithms for recommendation systems will suffer from distribution shift and struggle to adapt in such an MDP. In this paper, we introduce a novel paradigm called Adaptive Sequential Recommendation (AdaRec) to address this issue. AdaRec proposes a new distance-based representation loss to extract latent information from users' interaction trajectories. Such information reflects how RL policy fits to current user behavior patterns, and helps the policy to identify subtle changes in the recommendation system. To make rapid adaptation to these changes, AdaRec encourages exploration with the idea of optimism under uncertainty. The exploration is further guarded by zero-order action optimization to ensure stable recommendation quality in complicated environments. We conduct extensive empirical analyses in both simulator-based and live sequential recommendation tasks, where AdaRec exhibits superior long-term performance compared to all baseline algorithms.Comment: Preprint. Under Revie