mlsanityData to 14 Oct 2026

A Survey of Recommender System Techniques and the Ecommerce Domain

arXiv 2208.07399 ↗First posted 15 Aug 2022cs.IR cs.AIcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 4 of its 77 references that are in the map. First posted on arXiv 15 Aug 2022.

Builds on

4 of 77 references.

References · 4

  1. 2020Dynamic Tensor Recommender Systemscited by 12
  2. 2017A Context-Aware User-Item Representation Learning for Item Recommendationcited by 11
  3. 2017Explainable Entity-based Recommendations with Knowledge Graphscited by 5
  4. 2018A multi-level collaborative filtering method that improves recommendationscited by 2

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