A Survey of Recommender System Techniques and the Ecommerce Domain

• URL: http://arxiv.org/abs/2208.07399v1
• Date: Mon, 15 Aug 2022 18:30:22 GMT
• Title: A Survey of Recommender System Techniques and the Ecommerce Domain
• Authors: Imran Hossain, Md Aminul Haque Palash, Anika Tabassum Sejuty, Noor A Tanjim, MD Abdullah AL Nasim, Sarwar Saif, Abu Bokor Suraj
• Abstract summary: This paper reviews the different techniques and developments of recommender systems in e-commerce, e-tourism, e-resources, e-government, e-learning, and e-library.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: In this big data era, it is hard for the current generation to find the right data from the huge amount of data contained within online platforms. In such a situation, there is a need for an information filtering system that might help them find the information they are looking for. In recent years, a research field has emerged known as recommender systems. Recommenders have become important as they have many real-life applications. This paper reviews the different techniques and developments of recommender systems in e-commerce, e-tourism, e-resources, e-government, e-learning, and e-library. By analyzing recent work on this topic, we will be able to provide a detailed overview of current developments and identify existing difficulties in recommendation systems. The final results give practitioners and researchers the necessary guidance and insights into the recommendation system and its application.

Related papers

• Embedding in Recommender Systems: A Survey [67.67966158305603]
  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.
  arXiv  Detail & Related papers  (2023-10-28T06:31:06Z)
• 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)
• Recommender Systems in the Era of Large Language Models (LLMs) [62.0129013439038]
  Large Language Models (LLMs) have revolutionized the fields of Natural Language Processing (NLP) and Artificial Intelligence (AI) We conduct a comprehensive review of LLM-empowered recommender systems from various aspects including Pre-training, Fine-tuning, and Prompting.
  arXiv  Detail & Related papers  (2023-07-05T06:03:40Z)
• Recent Developments in Recommender Systems: A Survey [34.810859384592355]
  The study starts with a comprehensive summary of the main taxonomy of recommender systems, including personalized and group recommender systems. The survey analyzes the robustness, data bias, and fairness issues in recommender systems. The study provides insights into the latest trends in the development of recommender systems and highlights the new directions for future research in the field.
  arXiv  Detail & Related papers  (2023-06-22T05:51:49Z)
• Recommender Systems: A Primer [7.487718119544156]
  We provide an overview of the traditional formulation of the recommendation problem. We then review the classical algorithmic paradigms for item retrieval and ranking. We discuss a number of recent developments in recommender systems research.
  arXiv  Detail & Related papers  (2023-02-06T06:19:05Z)
• Fairness in Recommender Systems: Research Landscape and Future Directions [119.67643184567623]
  We review the concepts and notions of fairness that were put forward in the area in the recent past. We present an overview of how research in this field is currently operationalized. Overall, our analysis of recent works points to certain research gaps.
  arXiv  Detail & Related papers  (2022-05-23T08:34:25Z)
• A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directions [40.73124164815037]
  This survey aims to provide a timely and comprehensive overview of the recent trends of deep reinforcement learning in recommender systems. We provide a taxonomy of current DRL-based recommender systems and a summary of existing methods.
  arXiv  Detail & Related papers  (2021-09-08T10:44:55Z)
• The use of Recommender Systems in web technology and an in-depth analysis of Cold State problem [0.0]
  recommender systems provide personalized view for prioritizing items likely to be of keen for users. They have developed over the years in artificial intelligence techniques that include machine learning and data mining. This paper aims to tackle the said cold-start problem with a few methods and challenges.
  arXiv  Detail & Related papers  (2020-09-10T11:32:59Z)
• Recommender Systems for the Internet of Things: A Survey [53.865011795953706]
  Recommendation represents a vital stage in developing and promoting the benefits of the Internet of Things. Traditional recommender systems fail to exploit ever-growing, dynamic, and heterogeneous IoT data.
  arXiv  Detail & Related papers  (2020-07-14T01:24:44Z)
• 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.