Impression-Aware Recommender Systems

• URL: http://arxiv.org/abs/2308.07857v1
• Date: Tue, 15 Aug 2023 16:16:02 GMT
• Title: Impression-Aware Recommender Systems
• Authors: Fernando B. P\'erez Maurera, Maurizio Ferrari Dacrema, Pablo Castells, Paolo Cremonesi
• Abstract summary: 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.
• Score: 57.38537491535016
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Novel data sources bring new opportunities to improve the quality of recommender systems. Impressions are a novel data source containing past recommendations (shown items) and traditional interactions. Researchers may use impressions to refine user preferences and overcome the current limitations in recommender systems research. The relevance and interest of impressions have increased over the years; hence, the need for a review of relevant work on this type of recommenders. We present a systematic literature review on recommender systems using impressions, focusing on three fundamental angles in research: recommenders, datasets, and evaluation methodologies. We provide three categorizations of papers describing recommenders using impressions, present each reviewed paper in detail, describe datasets with impressions, and analyze the existing evaluation methodologies. Lastly, we present open questions and future directions of interest, highlighting aspects missing in the literature that can be addressed in future works.

Related papers

• Review of Explainable Graph-Based Recommender Systems [2.1711205684359247]
  This review paper discusses state-of-the-art approaches of explainable graph-based recommender systems. It categorizes them based on three aspects: learning methods, explaining methods, and explanation types.
  arXiv  Detail & Related papers  (2024-07-31T21:30:36Z)
• 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)
• 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)
• Tag-Aware Document Representation for Research Paper Recommendation [68.8204255655161]
  We propose a hybrid approach that leverages deep semantic representation of research papers based on social tags assigned by users. The proposed model is effective in recommending research papers even when the rating data is very sparse.
  arXiv  Detail & Related papers  (2022-09-08T09:13:07Z)
• A Review on Pushing the Limits of Baseline Recommendation Systems with the integration of Opinion Mining & Information Retrieval Techniques [0.0]
  Recommendation Systems allow users to identify trending items among a community while being timely and relevant to the user's expectations. Deep Learning methods have been brought forward to achieve better quality recommendations. Researchers have tried to expand on the capabilities of standard recommendation systems to provide the most effective recommendations.
  arXiv  Detail & Related papers  (2022-05-03T22:13:33Z)
• 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)
• Knowledge Transfer via Pre-training for Recommendation: A Review and Prospect [89.91745908462417]
  We show the benefits of pre-training to recommender systems through experiments. We discuss several promising directions for future research for recommender systems with pre-training.
  arXiv  Detail & Related papers  (2020-09-19T13:06:27Z)
• 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.