Deep Dynamic Neural Network to trade-off between Accuracy and Diversity in a News Recommender System

• URL: http://arxiv.org/abs/2103.08458v2
• Date: Wed, 17 Mar 2021 00:30:07 GMT
• Title: Deep Dynamic Neural Network to trade-off between Accuracy and Diversity in a News Recommender System
• Authors: Shaina Raza, Chen Ding
• Abstract summary: We propose a deep neural network that jointly learns informative news and readers' interests into a unified framework. We learn a reader's long-term interests from the reader's click history, short-term interests from the recent clicks via LSTMSs and the diversified reader's interests through the attention mechanism.
• Score: 1.3126169294309855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The news recommender systems are marked by a few unique challenges specific to the news domain. These challenges emerge from rapidly evolving readers' interests over dynamically generated news items that continuously change over time. News reading is also driven by a blend of a reader's long-term and short-term interests. In addition, diversity is required in a news recommender system, not only to keep the reader engaged in the reading process but to get them exposed to different views and opinions. In this paper, we propose a deep neural network that jointly learns informative news and readers' interests into a unified framework. We learn the news representation (features) from the headlines, snippets (body) and taxonomy (category, subcategory) of news. We learn a reader's long-term interests from the reader's click history, short-term interests from the recent clicks via LSTMSs and the diversified reader's interests through the attention mechanism. We also apply different levels of attention to our model. We conduct extensive experiments on two news datasets to demonstrate the effectiveness of our approach.

Related papers

• SCStory: Self-supervised and Continual Online Story Discovery [53.72745249384159]
  SCStory helps people digest rapidly published news article streams in real-time without human annotations. SCStory employs self-supervised and continual learning with a novel idea of story-indicative adaptive modeling of news article streams.
  arXiv  Detail & Related papers  (2023-11-27T04:50:01Z)
• From Nuisance to News Sense: Augmenting the News with Cross-Document Evidence and Context [25.870137795858522]
  We present NEWSSENSE, a novel sensemaking tool and reading interface designed to collect and integrate information from multiple news articles on a central topic. NEWSSENSE augments a central, grounding article of the user's choice by linking it to related articles from different sources. Our pilot study shows that NEWSSENSE has the potential to help users identify key information, verify the credibility of news articles, and explore different perspectives.
  arXiv  Detail & Related papers  (2023-10-06T21:15:11Z)
• Towards Corpus-Scale Discovery of Selection Biases in News Coverage: Comparing What Sources Say About Entities as a Start [65.28355014154549]
  This paper investigates the challenges of building scalable NLP systems for discovering patterns of media selection biases directly from news content in massive-scale news corpora. We show the capabilities of the framework through a case study on NELA-2020, a corpus of 1.8M news articles in English from 519 news sources worldwide.
  arXiv  Detail & Related papers  (2023-04-06T23:36:45Z)
• Designing and Evaluating Interfaces that Highlight News Coverage Diversity Using Discord Questions [84.55145223950427]
  This paper shows that navigating large source collections for a news story can be challenging without further guidance. We design three interfaces -- the Annotated Article, the Recomposed Article, and the Question Grid -- aimed at accompanying news readers in discovering coverage diversity while they read.
  arXiv  Detail & Related papers  (2023-02-17T16:59:31Z)
• Unveiling the Hidden Agenda: Biases in News Reporting and Consumption [59.55900146668931]
  We build a six-year dataset on the Italian vaccine debate and adopt a Bayesian latent space model to identify narrative and selection biases. We found a nonlinear relationship between biases and engagement, with higher engagement for extreme positions. Analysis of news consumption on Twitter reveals common audiences among news outlets with similar ideological positions.
  arXiv  Detail & Related papers  (2023-01-14T18:58:42Z)
• Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks [49.29141811578359]
  We propose to leverage a hypergraph to represent group-wise interaction among news, while focusing on important news relations with its dual-level attention mechanism. Our approach yields remarkable performance and maintains the high performance even with a small subset of labeled news data.
  arXiv  Detail & Related papers  (2022-12-24T00:19:32Z)
• Modeling Multi-interest News Sequence for News Recommendation [0.6787897491422114]
  A session-based news recommender system recommends the next news to a user by modeling the potential interests embedded in a sequence of news read/clicked by her/him in a session. This paper proposes a multi-interest news sequence (MINS) model for news recommendation. In MINS, a news based on self-attention is devised on learn an informative embedding for each piece of news, and then a novel parallel interest network is devised to extract the potential multiple interests embedded in the news sequence in preparation for the subsequent next-news recommendations.
  arXiv  Detail & Related papers  (2022-07-15T08:03:37Z)
• A News Recommender System Considering Temporal Dynamics and Diversity [0.0]
  In a news recommender system, a reader's preferences change over time. Some preferences drift quite abruptly (short-term preferences), while others change over a longer period of time. Our system should be able to: (i) accommodate the dynamics in reader behavior; and (ii) consider both accuracy and diversity in the design of the recommendation model.
  arXiv  Detail & Related papers  (2021-03-23T13:45:34Z)
• Beyond Optimizing for Clicks: Incorporating Editorial Values in News Recommendation [10.458414681622799]
  We study an automated news recommender system in the context of a news organization's editorial values. We find that our recommender system yields more diverse reading behavior and yields a higher coverage of articles.
  arXiv  Detail & Related papers  (2020-04-21T13:24:49Z)
• Graph Enhanced Representation Learning for News Recommendation [85.3295446374509]
  We propose a news recommendation method which can enhance the representation learning of users and news. In our method, users and news are both viewed as nodes in a bipartite graph constructed from historical user click behaviors.
  arXiv  Detail & Related papers  (2020-03-31T15:27:31Z)
• SirenLess: reveal the intention behind news [31.757138364005087]
  We present SirenLess, a visual analytical system for misleading news detection by linguistic features. The system features article explorer, a novel interactive tool that integrates news metadata and linguistic features to reveal semantic structures of news articles. We use SirenLess to analyze 18 news articles from different sources and summarize some helpful patterns for misleading news detection.
  arXiv  Detail & Related papers  (2020-01-08T20:36:17Z)

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.