PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity

Qi, Tao; Wu, Fangzhao; Wu, Chuhan; Huang, Yongfeng

doi:10.48550/arxiv.2106.01300

Cited by 2 publications

(2 citation statements)

References 36 publications

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“…MIND [49] is a public news recommendation dataset provided by Microsoft News, comprising behavioral data from over 1,000,000 users and in excess of 160,000 English news articles, which has been widely used in many news recommendation studies [13,[50][51][52][53]. Given the immense scale of MIND, its processing presents not only profound complexity and challenges but also demands an exorbitant amount of time.…”

Section: Dataset and Experimental Settingsmentioning

confidence: 99%

A Contrastive News Recommendation Framework based on Curriculum Learning

Zhou,

Lin,

Zheng

et al. 2024

Preprint

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News recommendation is a form of smart technology designed to offer users news content that matches their interests according to their preferences and interests. Nevertheless, current methodologies exhibit significant limitations. Traditional models often rely on simple random negative sampling for training, an approach that insufficiently captures the patterns and preferences of users' clicking behavior, thereby undermining the model's effectiveness. Furthermore, these systems often face challenges in insufficient modeling due to the limited nature of user interactions. Considering these challenges, this paper presents a contrastive news recommendation framework based on curriculum learning(CNRCL). Specifically, we relate the negative sampling process to users’ interests, and employ curriculum learning to guide the negative sampling procedure. To address the issue of insufficient user interest modeling, we propose to use contrastive learning to bring the user closer to news that is similar to the candidate news, thus enhancing the model's accuracy in predicting user interests, compensating for limited click behaviour. Evaluation of our proposed CNRCL model on the MIND benchmark dataset reveals its superiority over existing news recommendation methods.

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Section: Dataset and Experimental Settingsmentioning

confidence: 99%

A Contrastive News Recommendation Framework based on Curriculum Learning

Zhou,

Lin,

Zheng

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The authors [23] suggest a novel technique known as PP-Rec: News Recommendation with Personalized User Interest and time-aware news popularity. The ranking score in this method for recommending a candidate news to a target user is a combination of a personalized matching score and a news popularity score.…”

Section: Literature Surveymentioning

confidence: 99%

An Adaptive Classification Framework for Handling the Cold Start Problem in Case of News Items

Mir¹,

Zaman²,

Quadri³

et al. 2022

RIA

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Many recommendation systems make product or service suggestions based on existing knowledge of the user or the item. We must deal with two types of cold start problems: item-based and user-based cold start problems. In a user-based cold start dilemma, it is difficult for the system to suggest news to a new user whose information is not saved in the system. In this article, we attempt to address user based cold start problem by assuming that in the case of a user, we only know one type of information about the user, and that information is the user's location. Using BBC news data, an ID3 classification approach was developed, which incorporates eight explanatory factors such as News ID, News text, Keywords, date, Location, Shares count, followers, and so on. The classification accuracy of one of the best fit models constructed using (80-20)% training and test ratios is around 78%. Our technique is an effective tool for the cold-start problem because it outperforms the advice by a significant margin depending on the location. According to the results, our approach is competitive in terms of both accuracy and precision.

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PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity

Cited by 2 publications

References 36 publications

A Contrastive News Recommendation Framework based on Curriculum Learning

A Contrastive News Recommendation Framework based on Curriculum Learning

An Adaptive Classification Framework for Handling the Cold Start Problem in Case of News Items

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