This research aims to explore the impact of Machine Learning (ML) on the
evolution and efficacy of Recommendation Systems (RS), particularly in the
context of their growing significance in commercial business environments.
Methodologically, the study delves into the role of ML in crafting and refining
these systems, focusing on aspects such as data sourcing, feature engineering,
and the importance of evaluation metrics, thereby highlighting the iterative
nature of enhancing recommendation algorithms. The deployment of Recommendation
Engines (RE), driven by advanced algorithms and data analytics, is explored
across various domains, showcasing their significant impact on user experience
and decision-making processes. These engines not only streamline information
discovery and enhance collaboration but also accelerate knowledge acquisition,
proving vital in navigating the digital landscape for businesses. They
contribute significantly to sales, revenue, and the competitive edge of
enterprises by offering improved recommendations that align with individual
customer needs. The research identifies the increasing expectation of users for
a seamless, intuitive online experience, where content is personalized and
dynamically adapted to changing preferences. Future research directions include
exploring advancements in deep learning models, ethical considerations in the
deployment of RS, and addressing scalability challenges. This study emphasizes
the indispensability of comprehending and leveraging ML in RS for researchers
and practitioners, to tap into the full potential of personalized
recommendation in commercial business prospects.

本研究探索了机器学习对推荐系统的影响，特别是在商业环境中它们日益重要的背景下。揭示了机器学习在数据获取、特征工程和评估指标等方面对推荐系统的塑造和优化作用，并强调了增强推荐算法的迭代性质。探索了推荐引擎在各个领域的应用，以展示它们对用户体验和决策过程的重要影响。该研究还提到了用户对个性化和根据偏好动态调整的无缝在线体验的不断增加的期望。未来的研究方向包括探索深度学习模型的进展、推荐系统部署中的伦理考虑以及可扩展性挑战。本研究强调了了解和利用机器学习在个性化推荐商业前景中的不可或缺性，对于研究人员和实践者来说，这有助于充分发挥个性化推荐的潜力。

从数据到决策：机器学习在商业推荐中的变革性能量

From Data to Decisions: The Transformational Power of Machine Learning  in Business Recommendations

Music streaming services heavily rely on their recommendation engines to
continuously provide content to their consumers. Sequential recommendation
consequently has seen considerable attention in current literature, where state
of the art approaches focus on self-attentive models leveraging contextual
information such as long and short-term user history and item features;
however, most of these studies focus on long-form content domains (retail,
movie, etc.) rather than short-form, such as music. Additionally, many do not
explore incorporating negative session-level feedback during training. In this
study, we investigate the use of transformer-based self-attentive architectures
to learn implicit session-level information for sequential music
recommendation. We additionally propose a contrastive learning task to
incorporate negative feedback (e.g skipped tracks) to promote positive hits and
penalize negative hits. This task is formulated as a simple loss term that can
be incorporated into a variety of deep learning architectures for sequential
recommendation. Our experiments show that this results in consistent
performance gains over the baseline architectures ignoring negative user
feedback.

使用基于 transformer 的自注意力架构来学习顺序音乐推荐中的隐式会话级信息，同时提出一项对比学习任务来纳入负面反馈，以促进正面命中和惩罚负面命中，并通过实验证明这种方法相比忽略负面用户反馈的基准架构具有一致的性能提升。

利用自注意力机制来加强负向信号在序列音乐推荐中的作用

Leveraging Negative Signals with Self-Attention for Sequential Music  Recommendation

Recommendation engines are integral to the modern e-commerce experience, both
for the seller and the end user. Accurate recommendations lead to higher
revenue and better user experience. In this paper, we are presenting our
solution to ECML PKDD Farfetch Fashion Recommendation Challenge. The goal of
this challenge is to maximize the chances of a click when the users are
presented with set of fashion items. We have approached this problem as a
binary classification problem. Our winning solution utilizes Catboost as the
classifier and Bayesian Optimization for hyper parameter tuning. Our baseline
model achieved MRR of 0.5153 on the validation set. Bayesian optimization of
hyper parameters improved the MRR to 0.5240 on the validation set. Our final
submission on the test set achieved a MRR of 0.5257.

本文介绍了一种基于二元分类方法的推荐引擎，使用 Catboost 作为分类器以及贝叶斯优化来进行超参数调整，同时展示了在 ECML PKDD Farfetch 时尚推荐挑战中获胜的解决方案，验证集 MRR 为 0.5240，测试集 MRR 为 0.5257。