Collaborative recommender systems are highly vulnerable to attack. Attackers can use automated means to inject a large number of biased profiles into such a system, resulting in r...
Robin D. Burke, Bamshad Mobasher, Chad Williams, R...
This paper describes an approach for incorporating externally specified aggregate ratings information into certain types of collaborative filtering (CF) methods. For a statistic...
The potential benefit of integrating contextual information for recommendation has received much research attention recently, especially with the ever-increasing interest in mobil...
Yize Li, Jiazhong Nie, Yi Zhang, Bingqing Wang, Ba...
Many collaborative music recommender systems (CMRS) have succeeded in capturing the similarity among users or items based on ratings, however they have rarely considered about the...
Qing Li, Byeong Man Kim, Donghai Guan, Duk whan Oh
Recommender systems are an important component of many websites. Two of the most popular approaches are based on matrix factorization (MF) and Markov chains (MC). MF methods learn...
Steffen Rendle, Christoph Freudenthaler, Lars Schm...