Bing-CF-IDF+: A Semantics-Driven News Recommender System

Brocken, Emma; Hartveld, Aron; Koning, Emma de; Noort, Thomas van; Hogenboom, Frederik; Frăsincar, Flavius; Robal, Tarmo

doi:10.1007/978-3-030-21290-2_3

Cited by 10 publications

(7 citation statements)

References 17 publications

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“…. , e u l (17) where e n and e u denote a named entity in the profile of a news article, and of a user, respectively. The total number of named entities in the article's and user's profile is indicated by k, and respectively, l. All possible pairs of named entities from the two profiles are combined into a vector V, as per Eq.…”

Section: S(s)mentioning

confidence: 99%

“…where sim Bing (u, v) and sim sf −idf + (u, v) represent the normalized Bing and SF-IDF+ similarity scores, and α is a weight optimized on the training set. This approach of enhancing semantics-driven recommender systems with named entity similarities using the Bing page counts are prototypical also for other models, such as Bing-SF-IDF [71], Bing-CF-IDF+ [17], or Bing-CSF-IDF+ [161].…”

Section: S(s)mentioning

confidence: 99%

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A survey on knowledge-aware news recommender systems

Iana

Alam

Paulheim

2024

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News consumption has shifted over time from traditional media to online platforms, which use recommendation algorithms to help users navigate through the large incoming streams of daily news by suggesting relevant articles based on their preferences and reading behavior. In comparison to domains such as movies or e-commerce, where recommender systems have proved highly successful, the characteristics of the news domain (e.g., high frequency of articles appearing and becoming outdated, greater dynamics of user interest, less explicit relations between articles, and lack of explicit user feedback) pose additional challenges for the recommendation models. While some of these can be overcome by conventional recommendation techniques, injecting external knowledge into news recommender systems has been proposed in order to enhance recommendations by capturing information and patterns not contained in the text and metadata of articles, and hence, tackle shortcomings of traditional models. This survey provides a comprehensive review of knowledge-aware news recommender systems. We propose a taxonomy that divides the models into three categories: neural methods, non-neural entity-centric methods, and non-neural path-based methods. Moreover, the underlying recommendation algorithms, as well as their evaluations are analyzed. Lastly, open issues in the domain of knowledge-aware news recommendations are identified and potential research directions are proposed.

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Section: S(s)mentioning

confidence: 99%

Section: S(s)mentioning

confidence: 99%

A survey on knowledge-aware news recommender systems

Iana

Alam

Paulheim

2024

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“…These related concepts are computed by multiplying the weights of original concepts with the weight of relationships among them. For more semantic interpretation, NER information is incorporated [35]. All the work mentioned above is a series of efforts put in the path of the news recommendation system and is implemented in the Hermes News Portal which has two extensions, Athena [36] and Ceryx [30].…”

Section: ) Wordnet -Conceptbasedmentioning

confidence: 99%

“…Recently, the researchers working on the Hermes news portal [35] achieved an F-measure of 60% by adding additional information of named-entities from conceptbased knowledge bases. This is the highest realised Fmeasure on this news portal so far.…”

Section: Findings and Insightmentioning

confidence: 99%

A Review of Text-Based Recommendation Systems

et al. 2021

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Many websites over the Internet are producing a variety of textual data; such as news, research articles, ebooks, personal blogs, and user reviews. In these websites, the textual data is so large that the process of finding pertinent information by a user often becomes cumbersome. To overcome this issue, "Text-based Recommendation Systems (RS)" are being developed. They are the systems with the capability to find the relevant information in a minimal time using text as the primary feature. There exist several techniques to build and evaluate such systems. And though a good number of surveys compile the general attributes of recommendation systems, there is still a lack of comprehensive literature review about the text-based recommendation systems. In this paper, we present a review of the latest studies on text-based RS. We have conducted this survey by collecting literature from preeminent digital repositories, that was published during the period 2010-2020. This survey mainly covers the four major aspects of the textual based recommendation systems used in the reviewed literature. The aspects are datasets, feature extraction techniques, computational approaches, and evaluation metrics. As benchmark datasets carry a vital role in any research, publicly available datasets are extensively reviewed in this paper. Moreover, for text-based RS many proprietary datasets are also used, which are not available in the public. But we have consolidated all the attributes of these publically available and proprietary datasets to familiarize these attributes to new researchers. Furthermore, the feature extraction methods from the text are briefed and their usage in the construction of text-based RS are discussed. Later, various computational approaches that use these features are also discussed. To evaluate these systems, some evaluation metrics are adopted. We have presented an overview of these evaluation metrics and diagramed them according to their popularity. The survey concludes that Word Embedding is the widely used feature selection technique in the latest research. The survey also deduces that hybridization of text features with other features enhance the recommendation accuracy. The study highlights the fact that most of the work is on English textual data, and News recommendation is the most popular domain.

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“…They extend the synonym sets of concepts in news by adding other concepts in WordNet that have relationships with the included concepts. Based on aforementioned approaches, the family of CF-IDF is expanded by a set of later works [9,15,24,51,52].…”

Section: Feature-based News Modelingmentioning

confidence: 99%

Personalized News Recommendation: Methods and Challenges

Wu¹,

Wu²,

Huang³

et al. 2021

Preprint

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Personalized news recommendation is an important technique to help users find their interested news information and alleviate their information overload. It has been extensively studied over decades and has achieved notable success in improving users' news reading experience. However, there are still many unsolved problems and challenges that need to be further studied. To help researchers master the advances in personalized news recommendation over the past years, in this paper we present a comprehensive overview of personalized news recommendation. Instead of following the conventional taxonomy of news recommendation methods, in this paper we propose a novel perspective to understand personalized news recommendation based on its core problems and the associated techniques and challenges. We first review the techniques for tackling each core problem in a personalized news recommender system and the challenges they face. Next, we introduce the public datasets and evaluation metrics used for personalized news recommendation. We then discuss the key points on improving the responsibility of personalized news recommender systems. Finally, we raise several research directions that are worth investigating in future. This paper can provide up-to-date and comprehensive views to help readers understand the personalized news recommendation field. We hope this paper can facilitate research on personalized news recommendation and as well as related fields in natural language processing and data mining.

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Bing-CF-IDF+: A Semantics-Driven News Recommender System

Cited by 10 publications

References 17 publications

A survey on knowledge-aware news recommender systems

A survey on knowledge-aware news recommender systems

A Review of Text-Based Recommendation Systems

Personalized News Recommendation: Methods and Challenges

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