A Framework for Ranking Content Providers Using Prompt Engineering and Self-Attention Network

• URL: http://arxiv.org/abs/2409.11511v1
• Date: Tue, 17 Sep 2024 19:25:58 GMT
• Title: A Framework for Ranking Content Providers Using Prompt Engineering and Self-Attention Network
• Authors: Gosuddin Kamaruddin Siddiqi, Deven Santhosh Shah, Radhika Bansal, Askar Kamalov,
• Abstract summary: We propose a framework that leverages explicit user feedback, such as clicks and reactions, and content-based features, such as writing style and frequency of publishing, to rank Content Providers for a given topic. We evaluate our framework using online experiments and show that it can improve the quality, credibility, and diversity of the content recommended to users.
• Score: 0.0
• License:
• Abstract: This paper addresses the problem of ranking Content Providers for Content Recommendation System. Content Providers are the sources of news and other types of content, such as lifestyle, travel, gardening. We propose a framework that leverages explicit user feedback, such as clicks and reactions, and content-based features, such as writing style and frequency of publishing, to rank Content Providers for a given topic. We also use language models to engineer prompts that help us create a ground truth dataset for the previous unsupervised ranking problem. Using this ground truth, we expand with a self-attention based network to train on Learning to Rank ListWise task. We evaluate our framework using online experiments and show that it can improve the quality, credibility, and diversity of the content recommended to users.

Related papers

• Multi-Facet Counterfactual Learning for Content Quality Evaluation [48.73583736357489]
  We propose a framework for efficiently constructing evaluators that perceive multiple facets of content quality evaluation. We leverage a joint training strategy based on contrastive learning and supervised learning to enable the evaluator to distinguish between different quality facets.
  arXiv  Detail & Related papers  (2024-10-10T08:04:10Z)
• Bypassing the Popularity Bias: Repurposing Models for Better Long-Tail Recommendation [0.0]
  We aim to achieve a more equitable distribution of exposure among publishers on an online content platform. We propose a novel approach of repurposing existing components of an industrial recommender system to deliver valuable exposure to underrepresented publishers.
  arXiv  Detail & Related papers  (2024-09-17T15:40:55Z)
• Uncovering the Interaction Equation: Quantifying the Effect of User Interactions on Social Media Homepage Recommendations [0.5030361857850012]
  We study how prior user interactions influence the content presented on users' homepage feeds across three major platforms: YouTube, Reddit, and X (formerly Twitter) We use a series of carefully designed experiments to gather data capable of uncovering the influence of specific user interactions on homepage content. This study provides insights into the behaviors of the content curation algorithms used by each platform, how they respond to user interactions, and also uncovers evidence of deprioritization of specific topics.
  arXiv  Detail & Related papers  (2024-07-09T20:47:34Z)
• Backtracing: Retrieving the Cause of the Query [7.715089044732362]
  We introduce the task of backtracing, in which systems retrieve the text segment that most likely caused a user query. We evaluate the zero-shot performance of popular information retrieval methods and language modeling methods. Our results show that there is room for improvement on backtracing and it requires new retrieval approaches.
  arXiv  Detail & Related papers  (2024-03-06T18:59:02Z)
• Content Prompting: Modeling Content Provider Dynamics to Improve User Welfare in Recommender Ecosystems [14.416231654089994]
  We tackle this information asymmetry with content prompting policies. A content prompt is a hint or suggestion to a provider to make available novel content for which the RS predicts unmet user demand. We aim to determine a joint prompting policy that induces a set of providers to make content available that optimize user social welfare in equilibrium.
  arXiv  Detail & Related papers  (2023-09-02T13:35:11Z)
• Incentivizing High-Quality Content in Online Recommender Systems [80.19930280144123]
  We study the game between producers and analyze the content created at equilibrium. We show that standard online learning algorithms, such as Hedge and EXP3, unfortunately incentivize producers to create low-quality content.
  arXiv  Detail & Related papers  (2023-06-13T00:55:10Z)
• Can you recommend content to creatives instead of final consumers? A RecSys based on user's preferred visual styles [69.69160476215895]
  This report is an extension of the paper "Learning Users' Preferred Visual Styles in an Image Marketplace", presented at ACM RecSys '22. We design a RecSys that learns visual styles preferences to the semantics of the projects users work on.
  arXiv  Detail & Related papers  (2022-08-23T12:11:28Z)
• FEBR: Expert-Based Recommendation Framework for beneficial and personalized content [77.86290991564829]
  We propose FEBR (Expert-Based Recommendation Framework), an apprenticeship learning framework to assess the quality of the recommended content. The framework exploits the demonstrated trajectories of an expert (assumed to be reliable) in a recommendation evaluation environment, to recover an unknown utility function. We evaluate the performance of our solution through a user interest simulation environment (using RecSim)
  arXiv  Detail & Related papers  (2021-07-17T18:21:31Z)
• Feedback Shaping: A Modeling Approach to Nurture Content Creation [10.31854532203776]
  We propose a modeling approach to predict how feedback from content consumers incentivizes creators. We then leverage this model to optimize the newsfeed experience for content creators by reshaping the feedback distribution. We present a deployed use case on the LinkedIn newsfeed, where we used this approach to improve content creation significantly without compromising the consumers' experience.
  arXiv  Detail & Related papers  (2021-06-21T22:53:16Z)
• Abstractive Summarization of Spoken and Written Instructions with BERT [66.14755043607776]
  We present the first application of the BERTSum model to conversational language. We generate abstractive summaries of narrated instructional videos across a wide variety of topics. We envision this integrated as a feature in intelligent virtual assistants, enabling them to summarize both written and spoken instructional content upon request.
  arXiv  Detail & Related papers  (2020-08-21T20:59:34Z)
• Context-Based Quotation Recommendation [60.93257124507105]
  We propose a novel context-aware quote recommendation system. It generates a ranked list of quotable paragraphs and spans of tokens from a given source document. We conduct experiments on a collection of speech transcripts and associated news articles.
  arXiv  Detail & Related papers  (2020-05-17T17:49:53Z)

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.