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Auto-Scoring of Personalised News in the Real-Time Web: Challenges, Overview and Evaluation of the State-of-the-Art Solutions
KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.ORCID-id: 0000-0002-9351-8508
KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.
2015 (Engelska)Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

The problem of automated personalised news recommendation, often referred as auto-scoring has attracted substantial research throughout the last decade in multiple domains such as data mining and machine learning, computer systems, e commerce and sociology. A typical "recommender systems" approach to solving this problem usually adopts content-based scoring, collaborative filtering or more often a hybrid approach. Due to their special nature, news articles introduce further challenges and constraints to conventional item recommendation problems, characterised by short lifetime and rapid popularity trends. In this survey, we provide an overview of the challenges and current solutions in news personalisation and ranking from both an algorithmic and system design perspective, and present our evaluation of the most representative scoring algorithms while also exploring the benefits of using a hybrid approach. Our evaluation is based on a real-life case study in news recommendations.

Ort, förlag, år, upplaga, sidor
IEEE Computer Society, 2015. s. 169-180
Nyckelord [en]
Internet, collaborative filtering, recommender systems, auto-scoring, automated personalised news recommendation, collaborative filtering, content-based scoring, hybrid approach, item recommendation problems, news personalisation, real-time Web, recommender systems approach, Algorithm design and analysis, Collaboration, Correlation, Market research, Measurement, Recommender systems, auto-scoring, data mining, machine learning, recommender systems, scoring algorithms
Nationell ämneskategori
Datorsystem
Forskningsämne
Datalogi
Identifikatorer
URN: urn:nbn:se:kth:diva-179469DOI: 10.1109/ICCAC.2015.9ISI: 000380476500016Scopus ID: 2-s2.0-84962109478OAI: oai:DiVA.org:kth-179469DiVA, id: diva2:883354
Konferens
Cloud and Autonomic Computing (ICCAC), 2015 International Conference on, Cambridge, MA, USA, September 21-25, 2015
Anmärkning

QC 20160121

Tillgänglig från: 2015-12-17 Skapad: 2015-12-17 Senast uppdaterad: 2016-09-05Bibliografiskt granskad

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