Exploring privacy concerns in news recommender systems

Mohallick, Itishree; Özgöbek, Özlem

doi:10.1145/3106426.3109435

Cited by 14 publications

(10 citation statements)

References 22 publications

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“…Lack of semantics Multiple jargons and slangs with missing semantics can often be found in news stories (Mohallick and Özgöbek 2017). They are hard to interpret using available NLP libraries.…”

Section: Challenge 3: Quality Control Of the News Contentmentioning

confidence: 99%

News recommender system: a review of recent progress, challenges, and opportunities

2021

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Nowadays, more and more news readers read news online where they have access to millions of news articles from multiple sources. In order to help users find the right and relevant content, news recommender systems (NRS) are developed to relieve the information overload problem and suggest news items that might be of interest for the news readers. In this paper, we highlight the major challenges faced by the NRS and identify the possible solutions from the state-of-the-art. Our discussion is divided into two parts. In the first part, we present an overview of the recommendation solutions, datasets, evaluation criteria beyond accuracy and recommendation platforms being used in the NRS. We also talk about two popular classes of models that have been successfully used in recent years. In the second part, we focus on the deep neural networks as solutions to build the NRS. Different from previous surveys, we study the effects of news recommendations on user behaviors and try to suggest possible remedies to mitigate those effects. By providing the state-ofthe-art knowledge, this survey can help researchers and professional practitioners have a better understanding of the recent developments in news recommendation algorithms. In addition, this survey sheds light on the potential new directions.

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Section: Challenge 3: Quality Control Of the News Contentmentioning

confidence: 99%

News recommender system: a review of recent progress, challenges, and opportunities

2021

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“…However, fresh items have to be considered for recommendation, even if not too many interactions are recorded for them [26]. Scalability problems may arise as well, in particular for news aggregators, due to the high volume of new articles being published [3], [31], [40]; • Users preferences shift -The preferences of individual users are often not as stable as in other domains like entertainment [26]. Moreover, short-term interests of users can also be highly determined by their contextual situation [26], [42]- [44] or by exceptional situations like breaking news [39].…”

Section: ) Challenges Of News Recommendationmentioning

confidence: 99%

“…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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“…Online news recommendations have been addressed by researchers in the last years, either using Content-Based Filtering [41] [12] [56] [32] [50], Collaborative Filtering [15] [17], and Hybrid approaches [14] [44] [41] [55] [43] [42] [63] [20].…”

Section: News Recommender Systemsmentioning

confidence: 99%

News Session-Based Recommendations using Deep Neural Networks

Moreira

Ferreira²,

Cunha

2018

Proceedings of the 3rd Workshop on Deep Learning for Recommender Systems

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News recommender systems are aimed to personalize users experiences and help them to discover relevant articles from a large and dynamic search space. Therefore, news domain is a challenging scenario for recommendations, due to its sparse user profiling, fast growing number of items, accelerated item's value decay, and users preferences dynamic shift.Some promising results have been recently achieved by the usage of Deep Learning techniques on Recommender Systems, specially for item's feature extraction and for session-based recommendations with Recurrent Neural Networks.In this paper, it is proposed an instantiation of the CHAMELEON -a Deep Learning Meta-Architecture for News Recommender Systems. This architecture is composed of two modules, the first responsible to learn news articles representations, based on their text and metadata, and the second module aimed to provide session-based recommendations using Recurrent Neural Networks.The recommendation task addressed in this work is next-item prediction for users sessions: "what is the next most likely article a user might read in a session?"Users sessions context is leveraged by the architecture to provide additional information in such extreme cold-start scenario of news recommendation. Users' behavior and item features are both merged in an hybrid recommendation approach.A temporal offline evaluation method is also proposed as a complementary contribution, for a more realistic evaluation of such task, considering dynamic factors that affect global readership interests like popularity, recency, and seasonality.Experiments with an extensive number of session-based recommendation methods were performed and the proposed instantiation of CHAMELEON meta-architecture obtained a significant relative improvement in top-n accuracy and ranking metrics (10% on Hit Rate and 13% on MRR) over the best benchmark methods. * Also with Brazilian Aeronautics Institute of Technology -ITA.

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Exploring privacy concerns in news recommender systems

Cited by 14 publications

References 22 publications

News recommender system: a review of recent progress, challenges, and opportunities

News recommender system: a review of recent progress, challenges, and opportunities

Contextual Hybrid Session-Based News Recommendation With Recurrent Neural Networks

News Session-Based Recommendations using Deep Neural Networks

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