A collaborative filtering approach to mitigate the new user cold start problem
By Jesús Bobadilla, Fernando Ortega, Antonio Hernando, Jesus Bernal. Knowledge-Based Systems, 2012
521 citations recorded by the source corpus.
The new user cold start issue represents a serious problem in recommender systems as it can lead to the loss of new users who decide to stop using the system due to the lack of accuracy in the recommendations received in that first stage in which they have not yet cast a significant number of votes with which to feed the recommender system's collaborative filtering core. For this reason it is particularly important to design new similarity metrics which provide greater precision in the results offered to users who have cast few votes. This paper presents a new similarity measure perfected using optimization based on neural learning, which exceeds the best results obtained with current metrics. The metric has been tested on the Netflix and Movielens databases, obtaining important improvements in the measures of accuracy, precision and recall when applied to new user cold start situations. The paper includes the mathematical formalization describing how to obtain the main quality measures of a recommender system using leave-one-out cross validation.
Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/1ae77c1cd60b7c2a2353cc73e047bd27bf6f3070
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Jesús Bobadilla
- Bin Guo
- Fernando Ortega
- Antonio Hernando
- Abraham Gutiérrez
- Zhu Wang
- Jing Li
- Zhiwen Yu
In Information Systems
- Fernando Ortega
- Antonio Hernando
- Abraham Gutiérrez
- Jesús Bobadilla
- Bin Guo
- Zhu Wang
- Jing Li
- Zhiwen Yu
In Management Science and Operations Research
- Fernando Ortega
- Antonio Hernando
- Abraham Gutiérrez
- Jesús Bobadilla
- Bin Guo
- Zhu Wang
- Jing Li
- Zhiwen Yu
Citations in the corpus
- Social Network and Tag Sources Based Augmenting Collaborative Recommender System (2015, background): “(3) F-measure F-measure[22] is the weighted harmonic mean of precision and recall.”
- CityTransfer (2018, background): “Among the numerous recommendation methods, collaborating filtering (CF) [6,13,22,35] has shown high performance in many recommendation systems.”
- Recommender systems survey (2013, methodology): “[36] presents a metric based on neural learning (model-based CF) and adapted for new user cold-start situations, called NCS.”