On bootstrapping recommender systems

By Nadav Golbandi, Yehuda Koren, Ronny Lempel. 2010

85 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

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

1. Weinan Zhang
2. Jun Wang
3. Adam Trischler
4. Hongyuan Zha
5. Ke Zhou
6. Alessandro Sordoni
7. Mehdi Elahi
8. Neil Rubens

In Information Systems

1. Jun Wang
2. Francesco Ricci
3. Mehdi Elahi
4. Weinan Zhang
5. Neil Rubens
6. Xiaoxue Zhao
7. Hongyuan Zha
8. Ke Zhou

In Management Science and Operations Research

1. Francesco Ricci
2. Mehdi Elahi
3. Neil Rubens
4. Weinan Zhang
5. Jun Wang
6. Xiaoxue Zhao
7. Hongyuan Zha
8. Ke Zhou

Citations in the corpus

• Learning Algorithms for Active Learning (2017, background): “…systems, for example, ratings data for existing users can inform a strategy that efficiently elicits preferences for new users who lack prior rating data, thus boot-strapping the system quickly out of the cold-start setting (Golbandi et al., 2010; 2011; Sun et al., 2013a; Kawale et al., 2015).”
• Adaptive bootstrapping of recommender systems using decision trees (2011, methodology): “We also included Var [5], which ranks items by their vari­ance ampli.ed by multiplying it with the square root of the item y popularity: |R(i)|· Var(i).”
• Artificial intelligence in recommender systems (2020, background): “There are many different active learning strategies, such as rating impact analysis [132] and bootstrapping [133], and such active learning strategies have been integrated with common recommendation models such as the aspect model [134], decision trees [135], and matrix factorization [136].”
• Functional matrix factorizations for cold-start recommendation (2011, background): “…argued that an effective model for designing the interview process should ask the interview questions adaptively, taking into account the user’s responses when asking the next question in the interview process, and decision trees have been found to work especially well for this purpose [7,19,20].”
• A survey of active learning in collaborative filtering recommender systems (2016, methodology): “Hence, while this item could be selected by the strategies in the previous group, it is not very helpful for estimating the ratings of other items [69].”
• Learning multiple-question decision trees for cold-start recommendation (2013, background): “Many users have to repeatedly select unknown multiple times before lo- cating any item they know about.”
• Interactive collaborative filtering (2013, methodology): “, to use active learning [21, 29] or an interview process (preference elicitation) [41, 16] to first learn the user profile, and then to make recommendations based on the established profile.”