The digital world today thrives on data deluges, making effective filtering mechanisms indispensable tools for users navigating through an ocean of choices. Among such solutions, recommender systems (RS), particularly sequential RS, have garnered immense interest thanks to their ability to learn over time while catering to individual preferences. Reinforcement Learning (RL)-powered sequential recommenders showcase exceptional potential in striking a chord by maximizing long-term benefits, yet often face hurdles arising out of complexities inherent in the system. This impetus led researchers at City University of Hong Kong, Kuaishou Technology, and affiliated institutions towards engineering the groundbreaking 'DT4IER': a gamechanger in bridging instant gratification and enduring engagements in modern day recommender algorithms.
**Meeting the Challenge Head On:** The proposed solution, DT4IER, tackles two major issues plaguing existing reinforcement learning based sequential recommenders – instabilities during the learning phase resulting from the convolutional nature of bootstrapping, off-policy training, and functional approximations; coupled with the difficulty in creating a balanced reward structure amid multiple concurrent objectives. By employing a novel combination of decision transformation techniques, high dimensional encoding strategies, along with contrasitive learning approaches, DT4IER offers a cohesively unified framework to address these concerns head-on.
At the heart of DT4IER lies a sophisticated Multi-Reward Design, meticulously crafted to strike a delicate equilibrium between prompt feedback satisfaction ('Short Term') and sustainable long-lasting interaction ('Long Term'). User-centric parameters further enrich the reward sequences, fostering highly personalized experiences tailored according to individual tastes. Consequently, the enhanced context awareness promotes better-informed decisions throughout the entire recommendation journey.
Further amplifying the model's efficacy, a high-dimensional encoder is strategically integrated into DT4IER, allowing seamless identification of underlying correlations spanning different domains or tasks. Leveraging this deep understanding empowers the algorithm to deliver precise, targeted suggestions even under dynamic circumstances.
To give DT4IER a competitive edge, the research team introduces a unique twist via Contrastive Action Embedding Prediction. Here, the focus shifts toward comparing actions instead of merely estimating them, thus enhancing the overall performance of the system drastically.
Experimental validation conducted using authentic real-life datasets underscores the superiority of DT4IER relative to contemporary Sequential Recommenders as well as Multitask Learning Models, proving its prowess in delivering accurate forecasts alongside excelling in handling distinct facets of the problem space simultaneously. Open availability of the source code ensures wider applicability and scope for future advancements.
As a pinnacle achievement in the field of next generation sequential recommenders, DT4IER presents a compelling case study in harnessing cutting-edge technologies like Deep Learning, Reinforcement Learning, and Contrastive Learning to create a symbiotic relationship between momentary fulfillment and sustained delight in our ever evolving technological ecosystems.
Source arXiv: http://arxiv.org/abs/2404.03637v1