Trustworthy Recommender Systems

• URL: http://arxiv.org/abs/2208.06265v3
• Date: Fri, 17 Nov 2023 12:38:19 GMT
• Title: Trustworthy Recommender Systems
• Authors: Shoujin Wang, Xiuzhen Zhang, Yan Wang, Huan Liu, Francesco Ricci
• Abstract summary: Recommender systems (RSs) aim to help users to effectively retrieve items of their interests from a large catalogue. Recent years have witnessed an increasing number of threats to RSs, coming from attacks, system and user generated noise, system bias. For end users, a trustworthy RS (TRS) should not only be accurate, but also transparent, unbiased and fair as well as robust to noise or attacks.
• Score: 27.27498627500375
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recommender systems (RSs) aim to help users to effectively retrieve items of their interests from a large catalogue. For a quite long period of time, researchers and practitioners have been focusing on developing accurate RSs. Recent years have witnessed an increasing number of threats to RSs, coming from attacks, system and user generated noise, system bias. As a result, it has become clear that a strict focus on RS accuracy is limited and the research must consider other important factors, e.g., trustworthiness. For end users, a trustworthy RS (TRS) should not only be accurate, but also transparent, unbiased and fair as well as robust to noise or attacks. These observations actually led to a paradigm shift of the research on RSs: from accuracy-oriented RSs to TRSs. However, researchers lack a systematic overview and discussion of the literature in this novel and fast developing field of TRSs. To this end, in this paper, we provide an overview of TRSs, including a discussion of the motivation and basic concepts of TRSs, a presentation of the challenges in building TRSs, and a perspective on the future directions in this area. We also provide a novel conceptual framework to support the construction of TRSs.

Related papers

• On the Need for Configurable Travel Recommender Systems: A Systematic Mapping Study [3.029887797752315]
  There is a trend to build TRSs from scratch for different contexts rather than supporting developers with configuration approaches that promote reuse minimize errors and accelerate timetomarket. The conducted analysis reveals the lack of configuration support assisting TRSs providers in developing TRSs closely tied to their operational context.
  arXiv  Detail & Related papers  (2024-07-16T10:33:59Z)
• The Trajectory of Romance Scams in the U.S [0.0]
  This study examines RS trends in the U.S. through a quantitative analysis of web searches, news articles, research publications, and government reports from 2004 to 2023. Results reveal increasing public interest and media coverage contrasted by a recent decrease in incidents reported to authorities. Findings suggest RS escalation despite declining official reports, which are likely obscured by low victim reporting rates.
  arXiv  Detail & Related papers  (2024-05-06T20:18:33Z)
• Causal Learning for Trustworthy Recommender Systems: A Survey [46.38233864851038]
  This paper presents an overview of Trustworthy RS (TRS) from the perspective of causal learning. We identify potential trustworthiness challenges at each stage and link them to viable causal solutions.
  arXiv  Detail & Related papers  (2024-02-13T06:12:17Z)
• A Conversation is Worth A Thousand Recommendations: A Survey of Holistic Conversational Recommender Systems [54.78815548652424]
  Conversational recommender systems generate recommendations through an interactive process. Not all CRS approaches use human conversations as their source of interaction data. holistic CRS are trained using conversational data collected from real-world scenarios.
  arXiv  Detail & Related papers  (2023-09-14T12:55:23Z)
• Alleviating the Long-Tail Problem in Conversational Recommender Systems [72.8984755843184]
  Conversational recommender systems (CRS) aim to provide the recommendation service via natural language conversations. Existing CRS datasets suffer from the long-tail issue, ie a large proportion of items are rarely (or even never) mentioned in the conversations. This paper presents textbfLOT-CRS, a novel framework that focuses on simulating and utilizing a balanced CRS dataset.
  arXiv  Detail & Related papers  (2023-07-21T15:28:47Z)
• Methodologies for Improving Modern Industrial Recommender Systems [19.296314932479497]
  Recommender system (RS) is an established technology with successful applications in social media, e-commerce, entertainment, and more. This paper explores the methodology for improving modern industrial RSs.
  arXiv  Detail & Related papers  (2023-07-21T03:33:07Z)
• KECRS: Towards Knowledge-Enriched Conversational Recommendation System [50.0292306485452]
  chit-chat-based conversational recommendation systems (CRS) provide item recommendations to users through natural language interactions. external knowledge graphs (KG) have been introduced into chit-chat-based CRS. We propose the Knowledge-Enriched Conversational Recommendation System (KECRS) Experimental results on a large-scale dataset demonstrate that KECRS outperforms state-of-the-art chit-chat-based CRS.
  arXiv  Detail & Related papers  (2021-05-18T03:52:06Z)
• Advances and Challenges in Conversational Recommender Systems: A Survey [133.93908165922804]
  We provide a systematic review of the techniques used in current conversational recommender systems (CRSs) We summarize the key challenges of developing CRSs into five directions. These research directions involve multiple research fields like information retrieval (IR), natural language processing (NLP), and human-computer interaction (HCI)
  arXiv  Detail & Related papers  (2021-01-23T08:53:15Z)
• Sequential Recommender Systems: Challenges, Progress and Prospects [50.12218578518894]
  sequential recommender systems (SRSs) try to understand and model the sequential user behaviors, the interactions between users and items, and the evolution of users preferences and item popularity over time. We first present the characteristics of SRSs, then summarize and categorize the key challenges in this research area, followed by the corresponding research progress consisting of the most recent and representative developments on this topic.
  arXiv  Detail & Related papers  (2019-12-28T05:12:28Z)

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.