Incorporating popularity in a personalized news recommender system

Jonnalagedda, Nirmal; Gauch, Susan; Labille, Kevin; Alfarhood, Sultan

doi:10.7717/peerj-cs.63

Cited by 45 publications

(32 citation statements)

References 18 publications

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“…Liang et al [25] set a higher weight to the most recent articles from those discovered by using models for short and long-term interests. External knowledge from Twi er can also be used to determine popularity [7,20,30].…”

Section: News Recency Popularity and Varietymentioning

confidence: 99%

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Recommending Personalized News in Short User Sessions

Epure

Kille

Ingvaldsen

et al. 2017

Proceedings of the Eleventh ACM Conference on Recommender Systems

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News organizations employ personalized recommenders to target news articles to speci c readers and thus foster engagement. Existing approaches rely on extensive user pro les. However frequently possible, readers rarely authenticate themselves on news publishers' websites. is paper proposes an approach for such cases. It provides a basic degree of personalization while complying with the key characteristics of news recommendation including news popularity, recency, and the dynamics of reading behavior. We extend existing research on the dynamics of news reading behavior by focusing both on the progress of reading interests over time and their relations. Reading interests are considered in three levels: short-, medium-, and long-term. Combinations of these are evaluated in terms of added value to the recommendation's performance and ensured news variety. Experiments with 17-month worth of logs from a German news publisher show that most frequent relations between news reading interests are constant in time but their probabilities change. Recommendations based on combined shortterm and long-term interests result in increased accuracy while recommendations based on combined short-term and medium-term interests yield higher news variety.

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Section: News Recency Popularity and Varietymentioning

confidence: 99%

“…A collaborative recommender suggests items by comparing user preferences. Hybrid solutions are frequently reported to perform best in the news domain [2,4,6,20,[24][25][26].…”

Section: Literature Reviewmentioning

confidence: 99%

Recommending Personalized News in Short User Sessions

Epure

Kille

Ingvaldsen

et al. 2017

Proceedings of the Eleventh ACM Conference on Recommender Systems

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“…18 They are appropriate assistants when people face a great deal of information and need to select the required information for decision making. [20][21][22][23][24] Recommender systems have been defined in different ways  Computer programs that offer recommendations for special users which they may like or benefit from. 25  Strong tools that support the users and provide them with useful recommendations which are related to different decision-making processes.…”

Section: Recommender Systemsmentioning

confidence: 99%

Recommender system introduction for requests of cancer world

Aalipour

Ghazisaeedi

2017

Int J Community Med Public Health

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Awareness of cancer patients and their families, health care providers, and specialized cancer centers is achieved through access to up-to-date information about various items. Today, intelligent information technology systems have an important role in the awareness of people. Therefore, a type of technology is required that is capable of learning people’s needs, interests and suggesting appropriate information accordingly. The emergence of information technology systems, like recommender systems, is a step towards selecting appropriate information. With modeling the preferences, interests, needs, requests, and behaviors of the users, recommender systems seek to predict the future preferences, needs, and behaviors of the users to recommend appropriate and helpful services accordingly. Recommender systems can be a suitable tool for the information management of cancer-related screenings, diagnoses, treatments, operations, and rehabilitation programs. Access to treatment and health recommendations from valid sources is an important component of the natural processes of human decision making. The aim of this collection is to introduce recommender systems to use in cancer-related issues.

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“…One typical way of improving the quality of the recommendations in sparse-data situations is adopt a hybrid approach and consider additional information to assess the relevance of an item [17]- [19]. Previous approaches in the context of session-based recommendation for example used content [20] or context information [21] for improved recommendations.…”

Section: Introductionmentioning

confidence: 99%

Contextual Hybrid Session-Based News Recommendation With Recurrent Neural Networks

2019

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Recommender systems help users deal with information overload by providing tailored item suggestions to them. The recommendation of news is often considered to be challenging, since the relevance of an article for a user can depend on a variety of factors, including the user's short-term reading interests, the reader's context, or the recency or popularity of an article. Previous work has shown that the use of Recurrent Neural Networks is promising for the next-in-session prediction task, but has certain limitations when only recorded item click sequences are used as input. In this work, we present a contextual hybrid, deep learning based approach for session-based news recommendation that is able to leverage a variety of information types. We evaluated our approach on two public datasets, using a temporal evaluation protocol that simulates the dynamics of a news portal in a realistic way. Our results confirm the benefits of considering additional types of information, including article popularity and recency, in the proposed way, resulting in significantly higher recommendation accuracy and catalog coverage than other session-based algorithms. Additional experiments show that the proposed parameterizable loss function used in our method also allows us to balance two usually conflicting quality factors, accuracy and novelty.

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Incorporating popularity in a personalized news recommender system

Cited by 45 publications

References 18 publications

Recommending Personalized News in Short User Sessions

Recommending Personalized News in Short User Sessions

Recommender system introduction for requests of cancer world

Contextual Hybrid Session-Based News Recommendation With Recurrent Neural Networks

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