Results 31 to 40 of about 2,095 (214)
Online Collaborative-Filtering on Graphs [PDF]
SSRN Electronic Journal, 2013 Existing approaches to designing recommendation systems with user feedback focus on settings where the number of items is small and/or admit some underlying structure. It is unclear, however, if these approaches extend to applications like social network news feeds and content-curation platforms, which have large and unstructured content pools and ...
Siddhartha Banerjee +2 more
openaire +4 more sources
Multi-space Interactive Collaborative Filtering Recommendation [PDF]
Jisuanji kexue, 2021 In the era of big data,due to information overload,it is difficult for users to find interesting content from massive data.The birth of personalized recommendation system has greatly solved this problem.Collaborative filtering has been widely used in the
LI Kang-lin, GU Tian-long, BIN Chen-zhong
doaj +1 more source
Proceedings of the 12th ACM Conference on Recommender Systems, 2018 Automatic solutions which enable the selection of the best algorithms for a new problem are commonly found in the literature. One research area which has recently received considerable efforts is Collaborative Filtering. Existing work includes several approaches using Metalearning, which relate the characteristics of datasets with the performance of ...
Tiago Cunha 0001 +2 more
openaire +2 more sources
Rating Proportion-Aware Binomial Matrix Factorization for Collaborative Filtering
IEEE Access, 2023 Addressing biases in observed data is a major challenge in statistical and machine learning applications. This challenge also exists in recommendation systems, and various methods based on causal inference are being investigated.
Iwao Tanuma, Tomoko Matsui
doaj +1 more source
A novel clustered-based detection method for shilling attack in private environments [PDF]
PeerJ Computer ScienceThe topic of privacy-preserving collaborative filtering is gaining more and more attention. Nevertheless, privacy-preserving collaborative filtering techniques are vulnerable to shilling or profile injection assaults.
Ihsan Gunes
doaj +2 more sources
Collaborative Filtering with Stability
CoRR, 2018 Collaborative filtering (CF) is a popular technique in today's recommender systems, and matrix approximation-based CF methods have achieved great success in both rating prediction and top-N recommendation tasks. However, real-world user-item rating matrices are typically sparse, incomplete and noisy, which introduce challenges to the algorithm ...
Dongsheng Li 0002 +5 more
openaire +2 more sources
Deep social collaborative filtering [PDF]
Proceedings of the 13th ACM Conference on Recommender Systems, 2019 Recommender systems are crucial to alleviate the information overload problem in online worlds. Most of the modern recommender systems capture users' preference towards items via their interactions based on collaborative filtering techniques. In addition to the user-item interactions, social networks can also provide useful information to understand ...
Wenqi Fan +5 more
openaire +2 more sources
A Big Data Analysis Method Based on Modified Collaborative Filtering Recommendation Algorithms
Open Physics, 2019 With the rapid development of e-commerce, collaborative filtering recommendation system has been widely used in various network platforms. Using recommendation system to accurately predict customers’ preferences for goods can solve the problem of ...
Yin Nan
doaj +1 more source
Enforcing Differential Privacy for Shared Collaborative Filtering
IEEE Access, 2017 Collaborative filtering is now successfully applied to recommender systems. The availability of extensive personal data is necessary for generating high quality recommendations.
Jianqiang Li +6 more
doaj +1 more source
Collaborative filtering with privacy [PDF]
Proceedings 2002 IEEE Symposium on Security and Privacy, 2005 Server-based collaborative filtering systems have been very successful in e-commerce and in direct recommendation applications. In future, they have many potential applications in ubiquitous computing settings. But today's schemes have problems such as loss of privacy, favoring retail monopolies, and with hampering diffusion of innovations.
openaire +1 more source