Proposal of Recommender System Removed Popularity Bias by Using Information Gain
نویسندگان
چکیده
منابع مشابه
Recommender Response to Diversity and Popularity Bias in User Profiles
D ATA A N D M E T H O D S MovieLens 10M ratings + Tag Genome Took 5 disjoint samples of 1000 users Select 5 ratings for each as test ratings for accuracy Generate 100-item lists, prune to 10 and 25 items Measure user input profile & each recommender’s output Diversity: Intra-List Similarity with Pearson correlation over tag genome vectors Popularity: Mean Popularity Rank Accurac...
متن کاملCorrecting Popularity Bias by Enhancing Recommendation Neutrality
In this paper, we attempt to correct a popularity bias, which is the tendency for popular items to be recommended more frequently, by enhancing recommendation neutrality. Recommendation neutrality involves excluding specified information from the prediction process of recommendation. This neutrality was formalized as the statistical independence between a recommendation result and the specified...
متن کاملIncorporating popularity in a personalized news recommender system
Online news reading has become a widely popular way to read news articles from news sources around the globe. With the enormous amount of news articles available, users are easily overwhelmed by information of little interest to them. News recommender systems help users manage this flood by recommending articles based on user interests rather than presenting articles in order of their occurrenc...
متن کاملIMPROVE THE RECOMMENDER SYSTEM USING SEMANTIC WEB
To buy his/her necessities such as books, movies, CD, music, etc., one always trusts others’ oral and written consultations and offers and include them in his/her decisions. Nowadays, regarding the progress of technologies and development of e-business in websites, a new age of digital life has been commenced with the Recommender systems. The most important objectives of these systems include a...
متن کاملRecommender Popularity Controls: An Observational Study
We describe an observational study of a recommender system that provides users with direct control over their personalization. Specifically, we allow users to tune a movie recommender towards more or less popular content. We report on 14 months of usage, which includes 6,846 users who visited the interface at least once. We find, surprisingly, that the popularity of items a user has interacted ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Transactions of the Japanese Society for Artificial Intelligence
سال: 2015
ISSN: 1346-0714,1346-8030
DOI: 10.1527/tjsai.30_647