Implicit Preferences Discovery for Biography Recommender System Using Twitter
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TLDR
Implicitly infer user interest with good accuracy is proposed here and this understanding of interests can later be updated by observing user actions as they interact with the system.About:
This article is published in Procedia Computer Science.The article was published on 2020-01-01 and is currently open access. It has received 9 citations till now. The article focuses on the topics: Recommender system.read more
Citations
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Monitoring and Recognizing Enterprise Public Opinion from High-Risk Users Based on User Portrait and Random Forest Algorithm
TL;DR: This paper combines user portrait technology and a random forest algorithm to help enterprises identify high-risk users who have posted negative comments and thus may trigger negative public opinion.
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Signaling persuasion in crowdfunding entrepreneurial narratives: The subjectivity vs objectivity debate
TL;DR: Text mining and Bayesian inference is employed to identify and differentiate the subjective and objective attributes in an entrepreneurial narrative to contribute to the understanding of the subjectivity versus objectivity debate in economic exchange in online crowdfunding.
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Impact of Similarity Measures in K-means Clustering Method used in Movie Recommender Systems
TL;DR: The aim of this work is to study the effect of similar measures in movie recommender systems in terms of standard deviation (SD), mean absolute error (MAE), root mean square error (RMSE), t-value, Dunn Matrix, average similarity and computational time using publicly available MovieLens dataset.
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A Real-Time Entity Monitoring based on States and Scenarios
TL;DR: A discrete simulation is introduced for describing the times and sizes associated with the new schema when the volume of the projects to update grow-up, allowing implementing scenarios and entity states to increase the suitability between indicators and decision criteria according to the current scenario and entity state under analysis.
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Movie Recommender System using Single Value Decomposition and K-means Clustering
TL;DR: This work introduces an efficient model using Single Value Decomposition (SVD) as a method for dimensions reduction and K-means clustering as classification method and demonstrates that the proposed method is able to outperform other existing methods.
References
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Journal ArticleDOI
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
TL;DR: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches.
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Hybrid Recommender Systems: Survey and Experiments
TL;DR: This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants, and shows that semantic ratings obtained from the knowledge- based part of the system enhance the effectiveness of collaborative filtering.
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Context-Aware Recommender Systems
TL;DR: An overview of the multifaceted notion of context is provided, several approaches for incorporating contextual information in recommendation process are discussed, and the usage of such approaches in several application areas where different types of contexts are exploited are illustrated.
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Recommender system application developments
TL;DR: This paper reviews up-to-date application developments of recommender systems, clusters their applications into eight main categories, and summarizes the related recommendation techniques used in each category.
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Implicit feedback for inferring user preference: a bibliography
Diane Kelly,Jaime Teevan +1 more
TL;DR: Traditional relevance feedback methods require that users explicitly give feedback by specifying keywords, selecting and marking documents, or answering questions about their interests, which can be difficult to collect the necessary data and the effectiveness of explicit techniques can be limited.
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