Saturday, October 18, 2014

PAPER Implicit User Profiling in News Recommender Systems

Implicit User Profiling in News Recommender Systems

Keywords:  Recommender Systems, Personalization, User Profiling, Mobile News, Big Data, Information Retrieval.
Abstract:  User profiling is an important part of content-based and hybrid recommender systems. These profiles model users’ interests and preferences and are used to assess an item’s relevance to a particular user. In the news domain it is difficult to extract explicit signals from the users about their interests, and user profiling depends on in-depth analyses of users’reading habits. This is a challenging task, as news articles have short life spans, are unstructured, and make use of unclear and rapidly changing terminologies. This paper discusses an approach for constructing detailed user profiles on the basis of detailed observations of users’ interaction with a mobile news app. The profiles address both news categories and news entities, distinguish between long-term interests and running context, and are currently used in a real iOS mobile news recommender system that recommends news from 89 Norwegian newspapers.
Please remember:
Personality Based Recommender Systems are the next generation of recommender systems because they perform far better than Behavioural ones (past actions and pattern of personal preferences)
What comes after the Social Networking wave?
The Next Big Investment Opportunity on the Internet will be .... Personalization!
Personality Based Recommender Systems and Strict Personality Based Compatibility Matching Engines for serious Online Dating with the normative 16PF5 personality test.

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