Recommending Personalized News in Short User Sessions

Epure, Elena V.; Kille, Benjamin; Ingvaldsen, Jon Espen; Deneckère, Rébecca; Salinesi, Camille; Albayrak, Şahin

doi:10.1145/3109859.3109894

Cited by 36 publications

(14 citation statements)

References 32 publications

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“…A negative binomial-multinomial mixing model is proposed to simulate the user's choice of different items in each time period. Epure et al [10] propose a personalized recommendation method to provide recommendations for specific users. 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.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

SDRM‐LDP: A Recommendation Model Based on Local Differential Privacy

Yin

Yang

et al. 2021

Wireless Communications and Mobile Computing

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The development of 5G technology has driven the rise of e-commerce, social networking, and the Internet of Things. Under the high-speed transmission, the data volume increases, and the user demand also changes. Personalized customization has become the mainstream trend of network development. However, as the speed of the Internet increases, a series of problems also arise. The increase in data volume results in a reduction of bandwidth, a growth of the central processor’s pressure, and a higher risk of data leakage. A search system and a recommendation platform are the tools to improve people’s search efficiency. However, providing personalized recommendations to different users according to their needs is still an urgent problem. Simultaneously, the big data volume means that attackers can also get more information. They can use background knowledge and various reasoning methods to deduce the user’s private information using nonprivate items. In this paper, the solutions to safe and reliable recommendation services are the main problem explored. Based on this idea, this paper proposed short-term dynamic recommendation model based on local differential privacy (SDRM-LDP). This model uses a small amount of user information to construct short-term user preference behaviors and provides recommendations for users based on the similarity between items. We consider that an attacker uses nonprivate items to derive privacy items. Therefore, we randomly replace the original data in the same category. At the same time, the local differential privacy (LDP) is added to the privacy item query to make the private data available and protect the privacy information. In this paper, two real-world datasets, ML-100K and ML-10M, are used for experiments. Experimental results show that the results of SDRM-LDP are superior to other models.

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Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

SDRM‐LDP: A Recommendation Model Based on Local Differential Privacy

Yin

Yang

et al. 2021

Wireless Communications and Mobile Computing

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“…Esiyok et al (2014), for instance, demonstrate that different categories of news have a strong effect on the user clicking behaviour that suggests the presence of content-based reading habits. Similarly, Epure et al (2017) show how user preferences towards specific forms of thematic content provided by the single news outlet change over time. None of these studies, however, look at the content-based reading habits in the comparative perspective, thus raising the question of how similar/different are these habits between the users of different news outlets and leading us to the second research question:…”

Section: Content-based Consumption Habitsmentioning

confidence: 99%

“…To address this gap, we extend the few earlier studies (Epure et al, 2017; Esiyok et al, 2014) by employing more computational approaches for studying news readers’ habits. To do so, we use a large set of data on user click behaviour during their interactions with news content coming from two major legacy newspapers.…”

Section: Introductionmentioning

confidence: 99%

We are what we click: Understanding time and content-based habits of online news readers

Makhortykh

Vreese²,

Helberger

et al. 2020

New Media & Society

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The article contributes both conceptually and methodologically to the study of online news consumption by introducing new approaches to measuring user information behaviour and proposing a typology of users based on their click behaviour. Using as a case study two online outlets of large national newspapers, it employs computational approaches to detect patterns in time- and content-based user interactions with news content based on clickstream data. The analysis of interactions detects several distinct timelines of news consumption and scrutinises how users switch between news topics during reading sessions. Using clustering analysis, the article then identifies several types of news readers (e.g. samplers, gourmets) and examines their news diets. The results point out the limited variation in topical composition of the news diets between different types of readers and the tendency of these diets to align with the news supply patterns (i.e. the average distribution of topics covered by the outlet).

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“…The problem of filtering and recommending news items has been investigated for more than 20 years now, see [24] for an early work in this area. Technically, a variety of approaches have been put forward in these years, from collaborative filtering approaches [25], [26], to content-based methods [27]- [32], or hybrid systems [27], [33]- [39], see also [3] and [40] for recent surveys.…”

Section: A News Recommender Systemsmentioning

confidence: 99%

“…In a pre-processing step, like in [15], [39], [71], we organized the data into sessions using a 30 minute threshold of inactivity as an indicator of a new session. Sessions were then sorted by timestamp of their first click.…”

Section: A Datasetsmentioning

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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Recommending Personalized News in Short User Sessions

Cited by 36 publications

References 32 publications

SDRM‐LDP: A Recommendation Model Based on Local Differential Privacy

SDRM‐LDP: A Recommendation Model Based on Local Differential Privacy

We are what we click: Understanding time and content-based habits of online news readers

Contextual Hybrid Session-Based News Recommendation With Recurrent Neural Networks

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