Real-Time News Recommender System

Fortuna, Blaž; Fortuna, Carolina; Mladenić, Dunja

doi:10.1007/978-3-642-15939-8_38

Cited by 26 publications

(29 citation statements)

References 3 publications

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“…On one hand, users are usually interested in most recent news. On the other hand, users sometimes are interested to read previous relevant articles [4,9,10,12,17,18], 2) Large volume of news articles make this domain different from other types of Web objects. News articles tend to be in flood within a short period of time, requiring much more computation for recommendation [3,6,10,14,15], 3) Similarity between news articles does not necessarily reflect their relatedness.…”

Section: Overviewmentioning

confidence: 98%

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Workshop and challenge on news recommender systems

Tavakolifard

Gulla

Almeroth

et al. 2013

Proceedings of the 7th ACM Conference on Recommender Systems

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Recommending news articles entails additional requirements to recommender systems. Such requirements include special consumption patterns, fluctuating item collections, and highly sparse user profiles. This workshop (NRS'13@RecSys) brought together researchers and practitioners around the topics of designing and evaluating novel news recommender systems. Additionally, we offered a challenge allowing participants to evaluate their recommendation algorithms with actual user feedback.

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Section: Overviewmentioning

confidence: 98%

“…News portals typically refrain to require users to login prior to read news articles. Hence, there is a large fraction of users who appear to be "cold-start users" [4,6,9,10,11]. 10) The lack of sufficiently many interaction to established trustworthy user profiles entails further challenges.…”

Section: Overviewmentioning

confidence: 99%

Workshop and challenge on news recommender systems

Tavakolifard

Gulla

Almeroth

et al. 2013

Proceedings of the 7th ACM Conference on Recommender Systems

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show abstract

“…When considering user-related factors, we distinguish between the users' (short-term and long-term) interests and contextual factors. Regarding the context, their location [52]- [54], their device [55], and the current time [31], [53] can influence the users' short term interests, and thus the relevance of a news article [31], [48]. In addition, the referrer URL can contain helpful information about a user's navigation and reading context [38].…”

Section: ) Challenges Of News Recommendationmentioning

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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“…Further complications for collaborative filtering arise from the dynamic nature of the users and the news items themselves [6], [3]. In general, users will prefer fresher news articles, and building an accurate model of user preferences based on the items they have previously read can be difficult [4], [6].…”

Section: Related Workmentioning

confidence: 99%

“…In general, users will prefer fresher news articles, and building an accurate model of user preferences based on the items they have previously read can be difficult [4], [6]. While users may have preferred categories of news articles, or topics they are particularly interested in, these preferences are difficult to learn.…”

Section: Related Workmentioning

confidence: 99%