Introduction to Awesome-Deep-Learning-Papers-for-Search-Recommendation-Advertising
The "Awesome-Deep-Learning-Papers-for-Search-Recommendation-Advertising" project is an impressive collection of influential research papers focusing on the application of deep learning within the domains of search, recommendation, and advertising. This project provides an extensive overview of advancements and methodologies that are driving these industries forward. The papers curated in this collection cover a variety of critical areas such as embedding, matching, ranking for click-through rate (CTR) and conversion rate (CVR) prediction, post-ranking processes, the use of large models—like generative recommendations and large language models (LLMs), as well as transfer learning and reinforcement learning.
Embedding
Embedding techniques are essential for transforming complex data into a manageable format that machine learning models can understand. The project highlights several key contributions in this area:
- 2013 Word2vec (Google): Word2vec is a technique developed by Google that produces distributed representations of words, capturing semantic relationships effectively.
- 2014 DeepWalk (KDD): This paper introduced DeepWalk, an approach for generating social representations by learning latent relationships between entities in social networks.
- 2015 LINE (WWW): LINE focuses on embedding large-scale information networks, enabling efficient processing and analysis of network data.
- 2016 Node2vec (KDD): Node2vec offers a way to learn scalable feature representations for nodes in a network, facilitating tasks like network analysis and prediction.
- 2017 GCN (ICLR): Graph Convolutional Networks enable semi-supervised classification on graph-structured data, leveraging node and graph features.
- 2018 Airbnb & Alibaba Embeddings (KDD): These papers delve into how leading companies like Airbnb and Alibaba utilize embeddings for real-time personalization and e-commerce recommendations.
Matching
Matching refers to the process of aligning user needs with potential products or services, a crucial step in e-commerce and content delivery:
- 2013 DSSM (Microsoft): Introduces deep structured semantic models for improving web search results using clickthrough data.
- 2016 Youtube DNN (Google): Describes the application of deep neural networks in generating relevant video recommendations on YouTube.
- 2019 MOBIUS (Baidu): Focuses on enhancing query-ad matching in sponsored search contexts, such as Baidu's search engine.
- 2020 Embedding for Facebook Search (Facebook): Details the development of embedding-based retrieval systems to enhance search functionality on social media platforms.
Ranking (CTR/CVR Prediction)
Ranking plays a vital role in determining the order in which search results or recommendations are presented to users:
- The project includes various models that refine ranking through predictive analysis of CTR, enhancing user engagement and satisfaction.
- It discusses post-ranking adjustments that are integral to maintaining relevance as user interest and external factors change.
Transfer and Reinforcement Learning
Transfer learning allows models to apply knowledge learned from one task to another, while reinforcement learning uses trial-and-error methods to refine decision-making:
- The collection features papers exploring the application of these techniques to improve recommendation systems and search algorithms, offering innovative approaches to boosting model efficiency and effectiveness.
Large Models and Future Trends
- The integration of large models, including generative recommendations and LLMs, is highlighted as a trend shaping the future of search and recommendation technologies.
- Papers from leading industry and academic conferences demonstrate pioneering efforts and case studies from companies like Google, Facebook, and Alibaba.
Overall, the "Awesome-Deep-Learning-Papers-for-Search-Recommendation-Advertising" project serves as a valuable resource for researchers, practitioners, and enthusiasts interested in understanding and applying deep learning in search, recommendation, and advertising realms. Through a comprehensive collection of landmark papers, it provides insights into the theoretical foundations and practical implementations that underpin current industry practices and future innovations.