A Context-aware Recommender System for Hyperlocal News: A Conceptual Framework
Anh Nguyen Duc, Hilde Gudvangen
TL;DR
This paper proposes a conceptual framework for a mobile, context-aware recommender system tailored for hyperlocal news, emphasizing spatial-temporal relevance, recency, real-time updates, and validated news sources.
Contribution
It introduces a novel framework specifically designed for hyperlocal news recommendation, addressing unique spatial-temporal and recency requirements.
Findings
Framework effectively incorporates spatial-temporal context
Supports real-time news updates
Enhances relevance and validation of recommended news
Abstract
Recommender systems (RSs) have been popular in variety of application domains due to the increased demand for filtering and sorting items and information. Today, there is a numerous approaches and algorithms of data filtering and recommendations. This works presents a conceptual framework for constructing a mobile RS in hyper-local news domain. The mobile RS is designed to deal with specific requirements of news readers, such as spatial- temporal relevance, recency, real-time update and validated news. The implementation of the RS in a distributed file system is also discussed.
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Taxonomy
TopicsRecommender Systems and Techniques · Image Retrieval and Classification Techniques · Video Analysis and Summarization