Megha JainMegha Jain*. , "ALGORITHM FOR RESEARCH PAPER RECOMMENDATION SYSTEM", International Journal of Information Technology and Knowledge Management, July- December 2012, Volume 5, No. 2, pp. 443-445.
Accuracy measures the extent to which the recommendation algorithm is able to accurately predict the user’s liking for the recommended product. At present, most of the research on the evaluation index of the recommender system is based on the recommendation accuracy. There are many kinds of ...
This is a new deep learning model for recommender system, which we called PHD deep-learningmatrix-factorizationrecommendation-algorithm UpdatedOct 24, 2018 Python 基于协同过滤算法的个性化新闻推荐系统的设计与实现(采用Java语言的SSM框架实现基于用户、物品的协同过滤推荐算法) ...
These recommendations essentially stay same for different users from a specific geographical region but acceptability of these alternate queries may vary from person to person. A genetic algorithm based personalized recommendation system has been proposed in this work. The proposed method has been ...
Recommendation systemBig dataMap-reduceClusteringWhale optimization algorithmIn the era of Web 2.0, the data are growing immensely and is assisting E-commerce websites for better decision-making. Collaborative filtering, one of the prominent recommendation approaches, performs recommendation by finding ...
nutrition’spersonalized nutrition adviceThe general recommendations for addressing non-communicable diseases, are mainly related to lifestyle changes, such as diet and physical activity. The overall aR, Durai VasanthP, GokulT, BalamuruganS, Nivedha...
In order to solve the current issues of e–commerce recommendation systems: low accuracy, inflexible, lack personalized, etc. a solution based on hybrid recommendation algorithm is proposed, aiming at building a personalized recommendation system for e–commerce. To make up for the lack of a ...
recommendations for a brand new user or selecting algorithms for a brand new problem instance[2],[37],[28]. The main contributions of the present paper are the following. Firstly, the collaborativeAlgorithmRecommender systemAlors1can exploit sparse experimental data (as opposed to the extensive ...
In 2016, YouTubereleased a paperon its algorithm for its recommendation systems. According to the paper, the user’s watch history, search history and video completion history – all contributed to producing recommendations that were more personalized than ever. Moreover, the companyused surveysto ...
The recommendation systems at websites such as Amazon and Netflix use a technique called "collaborative filtering." To determine what products a given customer might like, they look for other customers who have assigned similar ratings to a similar range of products, and extrapolate from there. ...