TRecSo: Enhancing Top-k Recommendation With Social Information ∗ Chanyoung Park, Donghyun Kim, Jinoh Oh, Hwanjo Yu† Dept. of Computer Science and Engineering

First Page		Document Content
Date: 2017-10-04 00:59:03 Information science Collective intelligence Recommender systems Information retrieval Collaboration Social information processing Information systems Collaborative filtering Matrix factorization Machine learning Personalization		TRecSo: Enhancing Top-k Recommendation With Social Information ∗ Chanyoung Park, Donghyun Kim, Jinoh Oh, Hwanjo Yu† Dept. of Computer Science and Engineering Add to Reading List Source URL: gdac.uqam.ca Download Document from Source Website File Size: 733,19 KB Share Document on Facebook

	Explainable Matrix Factorization for Collaborative Filtering Behnoush Abdollahi Olfa Nasraoui Knowledge Discovery & Web Mining Lab DocID: 1xV7l - View Document
	PIIK, A COLLABORATIVE NEWS FILTERING SYSTEM FOR AN ELEARNING COMMUNITY IN HIGHER EDUCATION Platteaux Hervé, Hoein Sergio, Monnard Jacques, Jimenez François and Collaud Gérald Centre NTE - University of Fribourg, Switz DocID: 1vs8g - View Document
	Predicting Protein-Protein Interactions with Collaborative Filtering Algorithms István Albert Collaborative Filtering (CF) has proven to be a valuable tool for predicting relevant items in many different domains from e- DocID: 1vfr6 - View Document
	Practical Linear Models for Large-Scale One-Class Collaborative Filtering Suvash Sedhain†‡ , Hung Bui∗ , Jaya Kawale ∗ , Nikos Vlassis∗ , Branislav Kveton∗ , Aditya Krishna Menon‡† , Trung Bui∗ , Scott DocID: 1v839 - View Document
	Meta-Learning as a Collaborative Filtering Problem Mustafa Misir, Mich`ele Sebag MetaSel@ECAI 2014, August 2014 DocID: 1uUlV - View Document