Novelty-Aware Matrix Factorization Based on Items’ Popularity

Çoba, Ludovik; Symeonidis, Panagiotis; Zanker, Markus

doi:10.1007/978-3-030-03840-3_38

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…Finally, one can try to embed the consideration of tradeoffs within the learning phase, e.g., by using a corresponding regularization term. In [87], the authors propose a method called Novelty-aware Matrix Factorization (NMF), which tries to simultaneously recommend accurate and novel items. Their proposed regularization approach is pointwise, mean-ing that the novelty of each candidate item is considered individually.…”

Section: Balancing Accuracy and Novelty In Recommender Systemsmentioning

confidence: 99%

“…In our recommendation approach, we consider trade-offs in the regularization term as well. Differently, from [87], however, our approach is not focused on matrix factorization, but rather on neural models that are derived from the DSSM. Furthermore, the objective function in our work uses a listwise ranking approach to learn how to enhance the novelty level of the top-n recommendations.…”

Section: Balancing Accuracy and Novelty In Recommender Systemsmentioning

confidence: 99%

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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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Section: Balancing Accuracy and Novelty In Recommender Systemsmentioning

confidence: 99%

Section: Balancing Accuracy and Novelty In Recommender Systemsmentioning

confidence: 99%

Contextual Hybrid Session-Based News Recommendation With Recurrent Neural Networks

2019

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An overlapping clustering approach for precision, diversity and novelty-aware recommendations

Berbague

Karabadji²,

Séridi

et al. 2021

Expert Systems with Applications

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Deep Learning Models for Serendipity Recommendations: A Survey and New Perspectives

Niu

Maher

2023

ACM Comput. Surv.

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Serendipitous recommendations have emerged as a compelling approach to deliver users with unexpected yet valuable information, contributing to heightened user satisfaction and engagement. This survey paper presents an investigation of the most recent research in serendipity recommenders, with a specific emphasis on deep learning recommendation models. We categorize these models into three types, distinguishing their integration of the serendipity objective across distinct stages: pre-processing, in-processing, and post-processing. Additionally, we provide a review and summary of the serendipity definition, available ground truth datasets, and evaluation experiments employed in the field. We propose three promising avenues for future exploration: (1) leveraging user reviews to identify and explore serendipity, (2) employing reinforcement learning to construct a model for discerning appropriate timing for serendipitous recommendations, and (3) utilizing cross-domain learning to enhance serendipitous recommendations. With this review, we aim to cultivate a deeper understanding of serendipity in recommender systems and inspire further advancements in this domain.

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Novelty-Aware Matrix Factorization Based on Items’ Popularity

Cited by 3 publications

References 17 publications

Contextual Hybrid Session-Based News Recommendation With Recurrent Neural Networks

Contextual Hybrid Session-Based News Recommendation With Recurrent Neural Networks

An overlapping clustering approach for precision, diversity and novelty-aware recommendations

Deep Learning Models for Serendipity Recommendations: A Survey and New Perspectives

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