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Computer Science

Survey of User Profiling in News Recommender Systems

Profile image of Mahboobeh HarandiMahboobeh Harandi

2015

Abstract
sparkles

AI

This paper surveys the landscape of user profiling in news recommender systems, outlining key challenges and dimensions of user profiles, as well as various machine learning techniques utilized in these systems. It discusses the importance of flexible, evolving user profiles, the role of content-based and collaborative filtering techniques, and the need for systems to appropriately match user preferences while managing vast amounts of transient news data. The survey emphasizes the integration of text mining and machine learning to enhance the recommendation process.

FAQs

sparkles

AI

What challenges does news article recommendation face compared to other types of recommenders?add

News recommender systems deal with short-lived articles and unstructured data, requiring dynamic user analysis. The need for systems to scale effectively amidst high volumes of data further complicates the recommendation process.

How does user feedback influence the modeling of user profiles in news recommenders?add

User feedback, both explicit and implicit, is crucial for refining user profiles, impacting system accuracy. Research indicates explicit feedback yields more precise profiles, yet implicit signals often serve as primary input due to user reluctance.

What role does temporal context play in user profile modeling for news recommendations?add

Temporal parameters like time and location significantly enhance user profiles by capturing shifting interests. The study shows that preferences may vary depending on day parts, affecting news engagement across weekdays and weekends.

What machine learning techniques are most effective for building news user profiles?add

Supervised techniques like Decision Trees and Support Vector Machines significantly outperform others, particularly for sparse data conditions. Probabilistic methods like Naive Bayes are effective in managing long-term user interests amid missing data.

How can hybrid filtering approaches improve news recommendation accuracy?add

Hybrid filtering combines content-based and collaborative methods, mitigating individual weaknesses in isolation. The switching approach, prevalent in systems like Daily Learner, enhances prediction quality by dynamically adapting to user context and preferences.

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