News Recommender System Considering Temporal Dynamics and News Taxonomy

Raza, Shaina

doi:10.1109/bigdata47090.2019.9005459

Cited by 27 publications

(25 citation statements)

References 36 publications

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“…Matrix factorization can be used to discover the latent features that exhibit in the interactions between two different types of entities (e.g., users and items). In a recent NRS (Raza and Ding 2019), the MF is extended to include the news-related information and to model the temporal dynamics in readers' behaviors. This work introduces a novel predictor to include various temporal effects in the MF model, including time bias, user bias, and item bias.…”

Section: Matrix Factorization (Mf)mentioning

confidence: 99%

News recommender system: a review of recent progress, challenges, and opportunities

2021

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Nowadays, more and more news readers read news online where they have access to millions of news articles from multiple sources. In order to help users find the right and relevant content, news recommender systems (NRS) are developed to relieve the information overload problem and suggest news items that might be of interest for the news readers. In this paper, we highlight the major challenges faced by the NRS and identify the possible solutions from the state-of-the-art. Our discussion is divided into two parts. In the first part, we present an overview of the recommendation solutions, datasets, evaluation criteria beyond accuracy and recommendation platforms being used in the NRS. We also talk about two popular classes of models that have been successfully used in recent years. In the second part, we focus on the deep neural networks as solutions to build the NRS. Different from previous surveys, we study the effects of news recommendations on user behaviors and try to suggest possible remedies to mitigate those effects. By providing the state-ofthe-art knowledge, this survey can help researchers and professional practitioners have a better understanding of the recent developments in news recommendation algorithms. In addition, this survey sheds light on the potential new directions.

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Section: Matrix Factorization (Mf)mentioning

confidence: 99%

News recommender system: a review of recent progress, challenges, and opportunities

2021

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“…Accounting for individual-level selectivity constitutes a valuable starting point for diversityaware NRS design, but its influence on news selection is also contingent on a broad range of situational factors (Raza and Ding, 2019;Constantinides and Dowell, 2018). While our goal is not to develop an exhaustive model for all relevant situational moderators, we do want to highlight the ways in which individual reading goals as well as presentational factors can inform personalised diversity nudges.…”

Section: The Moderating Roles Of Situational Factorsmentioning

confidence: 99%

Nudging towards diversity: A theoretical framework for facilitating diverse news consumption through recommender design

Mattis¹,

Masur²,

Moeller³

et al. 2021

Preprint

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Growing concern about the democratic impact of automatically curated news platforms urges us to reconsider how such platforms should be designed. We propose a theoretical framework for personalised diversity nudges that can stimulate diverse news consumption on the individual level. To examine potential benefits and limitations of existing diversity nudges, we conduct an interdisciplinary literature review that synthesizes theoretical work on news selection mechanisms with hands-on tools and implementations from the fields of computer science and recommender systems. Based thereupon, we propose five diversity nudges that researchers and practitioners can build on. We provide a theoretical motivation of why, when, and for whom such nudges could be effective, critically reflect on their potential backfire effects and the need for algorithmic transparency, and sketch out a research agenda for diversity-aware news recommender design. Thereby, we develop concrete, theoretically grounded avenues toward facilitating diverse news consumption on algorithmically curated platforms.

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“…Застосування рекомендацій значно спрощує для споживача вирішення проблеми вибору з набору однотипних товарів з схожими характеристиками. Тому рекомендації широко використовуються при онлайн-продажах товарів, перегляді фільмів за допомогою стрімінгових сервісів, пропозиціях туристичних турів, виборі наукових конференцій, огляді новин [1,2].…”

Section: вступunclassified

“…Для підтримки довіри користувача рекомендації доповнюють поясненнями [6], яке має давати відповідь на запитання «чому отримана така рекомендація?» [7][8][9]. Оцінка впливу пояснень на вибір користувачів показала, що користувачі рідше відмовляються від рекомендованого предмета після отримання пояснення [6,10].…”

Section: вступunclassified

“…чи є можливість вибрати альтернативний предмет?». Для того, щоб користувач переконався в раціональності отриманої рекомендації, в поясненні необхідно порівняти темпоральну динаміку [2,12] для декількох альтернатив. На основі такого порівняння споживач отримає мотивацію для вибору найбільш популярної в даний момент альтернативи, що відповідає його інтересам.…”

Section: вступunclassified

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Temporal Patterns Of User Preferences In The Tasks Of Forming Explanations In The Recommender System

Чалий¹,

Лещинський²

2020

Scientific journal “Bionics of Intelligence”

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The problem of taking into account changes in the user’s behavior of the recommendation system whenconstructing explanations for recommendations is considered. This problem occurs as a result of cyclical changes in userrequirements. Its solution is associated with the construction of an explanation comparing the alternative choices of theuser of the recommendation system. The developed models of temporal patterns consist of a set of temporal relationshipsbetween the events of users’ choice of goods and services. The first pattern contains an alternative in the form of sequential selection in time of several objects or the selection of only a pair - the first and the last object. The second pattern,sequential-alternative choice, consists of a sequence of choices over time, which ends with the first pattern. The proposedapproach to the formation of patterns is based on the construction of data sets containing temporal dependencies betweena group of user choices for a given level of time detail. The temporal dataset is used to construct a temporal graph of therecommender system user selection process. The latter includes a set of temporal patterns with an indication of the timeof their beginning and end, which makes it possible to determine the duration of the implementation of these patterns.On the basis of the patterns, subsets of temporal relationships are formed to build explanations for the recommendedlist of goods and services. Experimental verification of the developed approach using the “Online Retail” sales data sethas shown the possibility of identifying temporal patterns even on short initial samples.

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News Recommender System Considering Temporal Dynamics and News Taxonomy

Cited by 27 publications

References 36 publications

News recommender system: a review of recent progress, challenges, and opportunities

News recommender system: a review of recent progress, challenges, and opportunities

Nudging towards diversity: A theoretical framework for facilitating diverse news consumption through recommender design

Temporal Patterns Of User Preferences In The Tasks Of Forming Explanations In The Recommender System

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