Modeling the Dynamics of Online News Reading Interests

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

doi:10.1145/3079628.3079636

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…Qin et al (2016) proposed to use contextual bandits for periodical recommendations, based on features hashing. Epure et al (2017) modeled the dynamics of user interests in news consumption by a discrete First-order Markov Chain over news categories. They in particular discovered breaks, which are stable periods in the user's behavior, e.g., January-March, April-July and August.…”

Section: News Recommendationmentioning

confidence: 99%

Considering temporal aspects in recommender systems: a survey

Bogina

Kuflik

Jannach

et al. 2022

User Model User-Adap Inter

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The widespread use of temporal aspects in user modeling indicates their importance, and their consideration showed to be highly effective in various domains related to user modeling, especially in recommender systems. Still, past and ongoing research, spread over several decades, provided multiple ad-hoc solutions, but no common understanding of the issue. There is no standardization and there is often little commonality in considering temporal aspects in different applications. This may ultimately lead to the problem that application developers define ad-hoc solutions for their problems at hand, sometimes missing or neglecting aspects that proved to be effective in similar cases. Therefore, a comprehensive survey of the consideration of temporal aspects in recommender systems is required. In this work, we provide an overview of various time-related aspects, categorize existing research, present a temporal abstraction and point to gaps that require future research. We anticipate this survey will become a reference point for researchers and practitioners alike when considering the potential application of temporal aspects in their personalized applications.

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Section: News Recommendationmentioning

confidence: 99%

Considering temporal aspects in recommender systems: a survey

Bogina

Kuflik

Jannach

et al. 2022

User Model User-Adap Inter

View full text Add to dashboard Cite

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In Search of a Stochastic Model for the E-News Reader

Veloso

Assunção

Ferreira

et al. 2019

ACM Trans. Knowl. Discov. Data

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E-news readers have increasingly at their disposal a broad set of news articles to read. Online newspaper sites use recommender systems to predict and to offer relevant articles to their users. Typically, these recommender systems do not leverage users’ reading behavior. If we know how the topics-reads change in a reading session, we may lead to fine-tuned recommendations, for example, after reading a certain number of sports items, it may be counter-productive to keep recommending other sports news. The motivation for this article is the assumption that understanding user behavior when reading successive online news articles can help in developing better recommender systems. We propose five categories of stochastic models to describe this behavior depending on how the previous reading history affects the future choices of topics. We instantiated these five classes with many different stochastic processes covering short-term memory, revealed-preference, cumulative advantage, and geometric sojourn models. Our empirical study is based on large datasets of E-news from two online newspapers. We collected data from more than 13 million users who generated more than 23 million reading sessions, each one composed by the successive clicks of the users on the posted news. We reduce each user session to the sequence of reading news topics. The models were fitted and compared using the Akaike Information Criterion and the Brier Score. We found that the best models are those in which the user moves through topics influenced only by their most recent readings. Our models were also better to predict the next reading than the recommender systems currently used in these journals showing that our models can improve user satisfaction.

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Modeling the Dynamics of Online News Reading Interests

Cited by 2 publications

References 7 publications

Considering temporal aspects in recommender systems: a survey

Considering temporal aspects in recommender systems: a survey

In Search of a Stochastic Model for the E-News Reader

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