Improving news articles recommendations via user clustering

Bouras, Christos; Tsogkas, Vassilis

doi:10.1007/s13042-014-0316-3

Cited by 25 publications

(17 citation statements)

References 26 publications

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“…The authors have employed this method for the systematic organization of the content and its retrieval. Further, RSS news feeds are represented in Extensible Mark-up Language (XML) formats [9]. This method is effective if the similar news items are merged together to gather the news from various sources.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Stream Clustering Based NEWS Summarization System

Raja¹,

Swamynathan²

2022

Computers, Materials &Amp; Continua

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News feed is one of the potential information providing sources which give updates on various topics of different domains. These updates on various topics need to be collected since the domain specific interested users are in need of important updates in their domains with organized data from various sources. In this paper, the news summarization system is proposed for the news data streams from RSS feeds and Google news. Since news stream analysis requires live content, the news data are continuously collected for our experimentation. The major contributions of this work involve domain corpus based news collection, news content extraction, hierarchical clustering of the news and summarization of news. Many of the existing news summarization systems lack in providing dynamic content with domain wise representation. This is alleviated in our proposed system by tagging the news feed with domain corpuses and organizing the news streams with the hierarchical structure with topic wise representation. Further, the news streams are summarized for the users with a novel summarization algorithm. The proposed summarization system generates topic wise summaries effectively for the user and no system in the literature has handled the news summarization by collecting the data dynamically and organizing the content hierarchically. The proposed system is compared with existing systems and achieves better results in generating news summaries. The Online news content editors are highly benefitted by this system for instantly getting the news summaries of their domain interest.

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Section: Related Workmentioning

confidence: 99%

Hierarchical Stream Clustering Based NEWS Summarization System

Raja¹,

Swamynathan²

2022

Computers, Materials &Amp; Continua

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“…Nevertheless, texts contain only few words grow at unprecedented rates from a wide range of popular resources, including Reddit, Stackoverflow, Twitter, and Instagram. Clustering those texts into groups of similar texts plays a crucial role in many real-world applications such as topic discovery (Kim et al, 2013), trend detection (Mathioudakis and Koudas, 2010), and recommendation (Bouras and Tsogkas, 2017). We evaluate on six benchmark datasets for short text clustering.…”

Section: Semantic Textual Similaritymentioning

confidence: 99%

Virtual Augmentation Supported Contrastive Learning of Sentence Representations

Zhang¹,

Xiao²,

Zhu³

et al. 2021

Preprint

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Despite profound successes, contrastive representation learning relies on carefully designed data augmentations using domain specific knowledge. This challenge is magnified in natural language processing where no general rules exist for data augmentation due to the discrete nature of natural language. We tackle this challenge by presenting a Virtual augmentation Supported Contrastive Learning of sentence representations (VaSCL). Originating from the interpretation that data augmentation essentially constructs the neighborhoods of each training instance, we in turn utilize the neighborhood to generate effective data augmentations. Leveraging the large training batch size of contrastive learning, we approximate the neighborhood of an instance via it's K-nearest in-batch neighbors in the representation space. We then define an instance discrimination task within this neighborhood, and generate the virtual augmentation in an adversarial training manner. We access the performance of VaSCL 1 on a wide range of downstream tasks, and set a new state-of-the-art for unsupervised sentence representation learning.

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“…One first case designed for news recommendation ( [20]) proposes a method for representing two faceted-profiles: a long-term subprofile comprising terms and categories from the history of relevant documents; a short-term one, with the same infor-mation but created after the first subprofile has been built. A second example is [7], where the profile comprises two subprofiles: the list of terms extracted from positively judged documents, enriched by the terms belonging to the cluster to which the user belongs (after applying K-means to every user) and enriched by Wordnet hypernyms, and the terms from negatively judged documents.…”

Section: Related Workmentioning

confidence: 99%

“…In the case where information coming from one source (typically documents) is organized into different profiles [20], in our proposal, we do not consider positive and negative documents. In the second reference, [7], the authors apply global clustering at the user level (users are the instances and the terms, the attributes) while our clustering is carried out at the document level and locally (only for the active user).…”

Section: Differences Of Our Approach With the Related Workmentioning

confidence: 99%

Automatic construction of multi-faceted user profiles using text clustering and its application to expert recommendation and filtering problems

Campos

Fernández‐Luna

Huete

et al. 2020

Knowledge-Based Systems

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In the information age we are living in today, not only are we interested in accessing multimedia objects such as documents, videos, etc. but also in searching for professional experts, people or celebrities, possibly for professional needs or just for fun. Information access systems need to be able to extract and exploit various sources of information (usually in text format) about such individuals, and to represent them in a suitable way usually in the form of a profile. In this article, we tackle the problems of profile-based expert recommendation and document filtering from a machine learning perspective by clustering expert textual sources to build profiles and capture the different hidden topics in which the experts are interested. The experts will then be represented by means of multi-faceted profiles. Our experiments show that this is a valid technique to improve the performance of expert finding and document filtering.

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Improving news articles recommendations via user clustering

Cited by 25 publications

References 26 publications

Hierarchical Stream Clustering Based NEWS Summarization System

Hierarchical Stream Clustering Based NEWS Summarization System

Virtual Augmentation Supported Contrastive Learning of Sentence Representations

Automatic construction of multi-faceted user profiles using text clustering and its application to expert recommendation and filtering problems

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