A Survey on Personalized News Recommendation Technology

Li, Miaomiao; Wang, Licheng

doi:10.1109/access.2019.2944927

Cited by 49 publications

(23 citation statements)

References 63 publications

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“…In some later NRS surveys (Karimi et al 2018 ; Chakraborty et al 2019 ), the new issues addressed (in addition to those covered in previous surveys) are beyond-accuracy aspects. Recently, the NRS surveys (Li and Wang 2019 ; Feng et al 2020 ; Qin and Lu 2020 ) have covered topics such as cold start, news content and feature engineering, and changing user preferences. The challenges discussed by each of these surveys are listed in Table 1 .…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

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

2021

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“…Existing surveys on personalized news recommendation usually classify methods into three categories, i.e., collaborative filtering-based, content-based and hybrid ones [89]. However, this classification criteria cannot adapt to the recent advances in news recommendation because many methods with diverse characteristics fall in the same category without distinguishment.…”

Section: Overviewmentioning

confidence: 99%

“…However, a large number of news articles are created and published every day, and it is impossible for users to browse through all available news to seek their interest news information [153]. Thus, personalized news recommendation techniques, which aim to select news according to users' personal interest, are critical for news platforms to help users alleviate their information overload of users and improve news reading experience [89]. Researches on personalized news recommendation have also attracted increasing attention from both academia and industry in recent years [112,152].…”

Section: Introductionmentioning

confidence: 99%

“…Over the past years, there are many survey papers that review the techniques of news recommendation [6, 8, 29-31, 33, 50, 65, 86, 89, 114, 127, 140]. For example, Li et al [89] reviewed the personalized news recommendation methods based on handcrafted features to build news and user representations. They covered many traditional methods, including collaborative filtering (CF) based ones that use the IDs of users and news, contentbased ones that use features extracted from the content of news and the user behaviors on news, and hybrid ones that rely on content-based collaborative filtering.…”

Section: Introductionmentioning

confidence: 99%

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Personalized News Recommendation: Methods and Challenges

Wu¹,

Wu²,

Huang³

et al. 2021

Preprint

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Personalized news recommendation is an important technique to help users find their interested news information and alleviate their information overload. It has been extensively studied over decades and has achieved notable success in improving users' news reading experience. However, there are still many unsolved problems and challenges that need to be further studied. To help researchers master the advances in personalized news recommendation over the past years, in this paper we present a comprehensive overview of personalized news recommendation. Instead of following the conventional taxonomy of news recommendation methods, in this paper we propose a novel perspective to understand personalized news recommendation based on its core problems and the associated techniques and challenges. We first review the techniques for tackling each core problem in a personalized news recommender system and the challenges they face. Next, we introduce the public datasets and evaluation metrics used for personalized news recommendation. We then discuss the key points on improving the responsibility of personalized news recommender systems. Finally, we raise several research directions that are worth investigating in future. This paper can provide up-to-date and comprehensive views to help readers understand the personalized news recommendation field. We hope this paper can facilitate research on personalized news recommendation and as well as related fields in natural language processing and data mining.

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“…The other promising direction of knowledge graph embedding-based recommendation in practice is news recommender systems [393][394][395][396]. Because of their textual recommendation scenarios [397], there raises three more challenges for news recommender systems compared with those based on non-textual scenarios: first, the news is distinctly time-sensitive.…”

Section: Recommendation Involving Knowledgementioning

confidence: 99%

Recommender systems based on graph embedding techniques: A comprehensive review

Deng

2021

Preprint

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Recommender systems, a pivotal tool to alleviate the information overload problem, aim to predict user's preferred items from millions of candidates by analyzing observed user-item relations. As for tackling the sparsity and cold start problems encountered by recommender systems, uncovering hidden (indirect) user-item relations by employing side information and knowledge to enrich observed information for the recommendation has been proven promising recently; and its performance is largely determined by the scalability of recommendation models in the face of the high complexity and large scale of side information and knowledge. Making great strides towards efficiently utilizing complex and large-scale data, research into graph embedding techniques is a major topic. Equipping recommender systems with graph embedding techniques contributes to outperforming the conventional recommendation implementing directly based on graph topology analysis and has been widely studied these years. This article systematically retrospects graph embedding-based recommendation from embedding techniques for bipartite graphs, general graphs, and knowledge graphs, and proposes a general design pipeline of that. In addition, comparing several representative graph embedding-based recommendation models with the most common-used conventional recommendation models, on simulations, manifests that the conventional models overall outperform the graph embedding-based ones in predicting implicit user-item interactions, revealing the relative weakness of graph embedding-based recommendation in these tasks. To foster future research, this article proposes constructive suggestions on making a trade-off between graph embedding-based recommendation and the conventional recommendation in different tasks as well as some open questions.

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A Survey on Personalized News Recommendation Technology

Cited by 49 publications

References 63 publications

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

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

Personalized News Recommendation: Methods and Challenges

Recommender systems based on graph embedding techniques: A comprehensive review

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