Embedding in Recommender Systems: A Survey

• URL: http://arxiv.org/abs/2310.18608v2
• Date: Thu, 21 Dec 2023 09:11:48 GMT
• Title: Embedding in Recommender Systems: A Survey
• Authors: Xiangyu Zhao, Maolin Wang, Xinjian Zhao, Jiansheng Li, Shucheng Zhou, Dawei Yin, Qing Li, Jiliang Tang, Ruocheng Guo
• Abstract summary: A crucial aspect is embedding techniques that covert the high-dimensional discrete features, such as user and item IDs, into low-dimensional continuous vectors. Applying embedding techniques captures complex entity relationships and has spurred substantial research. This survey covers embedding methods like collaborative filtering, self-supervised learning, and graph-based techniques.
• Score: 67.67966158305603
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recommender systems have become an essential component of many online platforms, providing personalized recommendations to users. A crucial aspect is embedding techniques that coverts the high-dimensional discrete features, such as user and item IDs, into low-dimensional continuous vectors and can enhance the recommendation performance. Applying embedding techniques captures complex entity relationships and has spurred substantial research. In this survey, we provide an overview of the recent literature on embedding techniques in recommender systems. This survey covers embedding methods like collaborative filtering, self-supervised learning, and graph-based techniques. Collaborative filtering generates embeddings capturing user-item preferences, excelling in sparse data. Self-supervised methods leverage contrastive or generative learning for various tasks. Graph-based techniques like node2vec exploit complex relationships in network-rich environments. Addressing the scalability challenges inherent to embedding methods, our survey delves into innovative directions within the field of recommendation systems. These directions aim to enhance performance and reduce computational complexity, paving the way for improved recommender systems. Among these innovative approaches, we will introduce Auto Machine Learning (AutoML), hash techniques, and quantization techniques in this survey. We discuss various architectures and techniques and highlight the challenges and future directions in these aspects. This survey aims to provide a comprehensive overview of the state-of-the-art in this rapidly evolving field and serve as a useful resource for researchers and practitioners working in the area of recommender systems.

Related papers

• Interactive Graph Convolutional Filtering [79.34979767405979]
  Interactive Recommender Systems (IRS) have been increasingly used in various domains, including personalized article recommendation, social media, and online advertising. These problems are exacerbated by the cold start problem and data sparsity problem. Existing Multi-Armed Bandit methods, despite their carefully designed exploration strategies, often struggle to provide satisfactory results in the early stages. Our proposed method extends interactive collaborative filtering into the graph model to enhance the performance of collaborative filtering between users and items.
  arXiv  Detail & Related papers  (2023-09-04T09:02:31Z)
• Impression-Aware Recommender Systems [57.38537491535016]
  Novel data sources bring new opportunities to improve the quality of recommender systems. Researchers may use impressions to refine user preferences and overcome the current limitations in recommender systems research. We present a systematic literature review on recommender systems using impressions.
  arXiv  Detail & Related papers  (2023-08-15T16:16:02Z)
• Guided Depth Map Super-resolution: A Survey [88.54731860957804]
  Guided depth map super-resolution (GDSR) aims to reconstruct a high-resolution (HR) depth map from a low-resolution (LR) observation with the help of a paired HR color image. A myriad of novel and effective approaches have been proposed recently, especially with powerful deep learning techniques. This survey is an effort to present a comprehensive survey of recent progress in GDSR.
  arXiv  Detail & Related papers  (2023-02-19T15:43:54Z)
• Recent Advances in Heterogeneous Relation Learning for Recommendation [5.390295867837705]
  We review the development of recommendation frameworks with the focus on heterogeneous relational learning. The objective of this task is to map heterogeneous relational data into latent representation space. We discuss the learning approaches in each category, such as matrix factorization, attention mechanism and graph neural networks.
  arXiv  Detail & Related papers  (2021-10-07T13:32:04Z)
• A Survey on Neural Recommendation: From Collaborative Filtering to Content and Context Enriched Recommendation [70.69134448863483]
  Research in recommendation has shifted to inventing new recommender models based on neural networks. In recent years, we have witnessed significant progress in developing neural recommender models.
  arXiv  Detail & Related papers  (2021-04-27T08:03:52Z)
• Cluster Based Deep Contextual Reinforcement Learning for top-k Recommendations [2.8207195763355704]
  We propose a novel method for generating top-k recommendations by creating an ensemble of clustering with reinforcement learning. We have incorporated DB Scan clustering to tackle vast item space, hence in-creasing the efficiency multi-fold. With partial updates and batch updates, the model learns user patterns continuously.
  arXiv  Detail & Related papers  (2020-11-29T20:24:39Z)
• A Deep Hybrid Model for Recommendation Systems [0.0]
  We propose a new deep neural network architecture which consists of not only ID embeddings but also auxiliary information such as features of job postings and candidates for job recommendation system. Experimental results on the dataset from a job-site show that the proposed method improves recommendation results over deep learning models utilizing ID embeddings.
  arXiv  Detail & Related papers  (2020-09-21T10:41:28Z)
• Recommendation system using a deep learning and graph analysis approach [1.2183405753834562]
  We propose a novel recommendation method based on Matrix Factorization and graph analysis methods. In addition, we leverage deep Autoencoders to initialize users and items latent factors, and deep embedding method gathers users' latent factors from the user trust graph.
  arXiv  Detail & Related papers  (2020-04-17T08:05:33Z)
• A Survey on Knowledge Graph-Based Recommender Systems [65.50486149662564]
  We conduct a systematical survey of knowledge graph-based recommender systems. We focus on how the papers utilize the knowledge graph for accurate and explainable recommendation. We introduce datasets used in these works.
  arXiv  Detail & Related papers  (2020-02-28T02:26:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.