A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce

By Zan Huang, Daniel Dajun Zeng, Hsinchun Chen. IEEE Intelligent Systems, 2007

311 citations recorded by the source corpus.

Subfields: Information Systems, Marketing, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/c4387a7ac9227c5fbd73eb1aff97169cd6639419

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Weike Pan
2. Zhong Ming
3. Julian McAuley
4. Harald Steck
5. Hao Wang
6. Yehuda Koren
7. Jessica Clark
8. Xiangnan He

In Marketing

1. Weike Pan
2. Zhong Ming
3. Harald Steck
4. Foster J. Provost
5. Julian McAuley
6. Jessica Clark
7. Xiangnan He
8. Yehuda Koren

In Sociology and Political Science

1. Foster J. Provost
2. Jessica Clark
3. Jia Guo
4. Zhong Ming
5. Diego Fernández
6. Weike Pan
7. Victor Carneiro
8. Ing-Ray Chen

Citations in the corpus

• A collaborative filtering approach to mitigate the new user cold start problem (2012, background): “A series of algorithms [19] and metrics [1,5,55,7–9] of similarity between users are currently available, enabling this important function to be performed in the CF core of this type of RS.”
• Dimensionality Reduction via Matrix Factorization for Predictive Modeling from Large, Sparse Behavioral Data (2015, background): “As explained by Koren (2010), there are two main approaches to collaborative filtering problems: There are also a few papers where SVD was demonstrated to be inferior to non-DR techniques on particular data sets or settings, such as Huang et al. (2007) and Sun et al. (2005).”
• Collaborative Filtering for Implicit Feedback Datasets (2008, background): “One of the tools that address this challenge is recommender systems, which are attracting a lot of attention recently [1, 4, 12].”
• Transfer Learning for Heterogeneous One-Class Collaborative Filtering (2016, methodology): “10: Calculate ( ) ˆui r l via Eq.(4) 11: Calculate , Θ (l) via Eq.”
• Trust Management for SOA-Based IoT and Its Application to Service Composition (2016, methodology): “We utilize the design concept of distributed collaborating filtering [12, 38] to select trust feedback from nodes sharing similar social interests.”
• Recommender systems survey (2013, background): “The most common research papers are focused on movie recommendation studies [53,230]; however, a great volume of literature for RS is centered on different topics, such as music [134,162,216], television [238,18], books [164,88], documents [206,184,183,185], elearning [241,30], e-commerce [104,54], ”
• Comparison of collaborative filtering algorithms (2011, methodology): “Its use in the evaluation of recommender systems [Herlocker et al. 2004; Huang et al. 2007] allows to interpret graphically the behavior of the system when distinguishing good items (relevant) from bad (nonrelevant).”