Adaptive bootstrapping of recommender systems using decision trees

By Nadav Golbandi, Yehuda Koren, Ronny Lempel. 2011

288 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. Adam Trischler
3. Jianfeng Gao
4. Alessandro Sordoni
5. Jun Wang
6. Philip Bachman
7. Mohit Bansal
8. Kyunghyun Cho

In Information Systems

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

In Management Science and Operations Research

1. Weinan Zhang
2. Mehdi Elahi
3. Jun Wang
4. Neil Rubens
5. Francesco Ricci
6. Xiaoxue Zhao
7. Philip Bachman
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).”
• Learning multiple-question decision trees for cold-start recommendation (2013, background): “Copyright 2013 ACM 978-1-4503-1869-3/13/02 ...$15.00.”
• A survey of active learning in collaborative filtering recommender systems (2016, methodology): “For each candidate item in Iu, this strategy computes the reduction of RMSE, before and after adding the rating of the candidate item to the training set.”
• 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].”
• Interactive collaborative filtering (2013, methodology): “Here we follow the work of [17] and set the interview question number as 5.”
• Facing the cold start problem in recommender systems (2014, background): “Finally, in Golbandi, Koren, and Lempel (2011), a model for the profiling of new users is discussed.”
• Comparing the Predictive Capability of Social and Interest Affinity for Recommendations (2014, background): “items) with more ratings? The belief is that CF does better when a user has rated more items [15, 8].”
• Functional matrix factorizations for cold-start recommendation (2011, methodology): “We compare the performance with two baseline methods, named as Tree and TreeU and briefly described as follows: • Tree: this is the method proposed in the very recent work of [8].”