抽象的な

Survey on News Recommendation

Mansi Sood, Harmeet Kaur

Recommender Systems have evolved as an answer to information overload problem prevalent with online users, looking for relevant information out of a huge premise of content available online. Such systems are used to provide recommendations to the users guiding them towards items that match their interest areas and choice. News Recommendation is a specific research area under recommender systems where these systems are used to suggest news articles to the users that match their reading interests and personal preferences. However, news recommendation differs from traditional recommendation needs and concerns in many aspects. This paper surveys many such systems built over past few years, and tries to present the special challenges associated with news recommendation over the traditional model of recommendation. It also explores recommendation techniques that can be used to generate news recommendations for the users.