Learning multiple-question decision trees for cold-start recommendation
By Mingxuan Sun, Fuxin Li, Joonseok Lee, Ke Zhou, Guy Lebanon, Hongyuan Zha. 2013
94 citations recorded by the source corpus.
Subfields: 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 Information Systems
- Katja Hofmann
- Dietmar Jannach
- Li Chen
- Wanling Cai
- Ahtsham Manzoor
- Filip Radlinski
- Philip Bachman
- Nan Wang
In Management Science and Operations Research
- Katja Hofmann
- Philip Bachman
- Nan Wang
- Dietmar Jannach
- Li Chen
- Filip Radlinski
- Alessandro Sordoni
- Yiyi Tao
Citations in the corpus
- Learning Algorithms for Active Learning (2017, methodology): “The cold-start problem is ubiquitous in recommendation systems (Aggarwal, 2016; Lika et al., 2014; Harpale & Yang, 2008; Sun et al., 2013b; Elahi et al., 2016).”
- Adversarial Active Learning for Deep Networks: a Margin Based Approach (2018, background): “…(Cohn et al., 1996), but also by its proven efficiency on a wide range of machine learning procedures: from preference rating information for a new user in a movie recommendation system (Sun et al., 2013) to classifying medical data that often requires very high cost labeling (Hoi et al., 2006).”
- A Survey on Conversational Recommender Systems (2020, background): “er rather refers to a system that addresses observed user interest changes over time, but is not designed to support a dialogue with the user. Such works were not considered as well. Other works like [132] or [166] mainly focus on finding good strategies for acquiring an initial set of ratings for c”
- Towards Conversational Recommender Systems (2016, background): “…elicit the preferences of an existing or new user (cold-start), a range of methods have been proposed, varying from interview-based strategies (e.g [30]), to asking users to rate some items, to active learning [26], entropy minimization [27], picture-based [22], and explore-exploit strategies on…”
- The FacT: Taming Latent Factor Models for Explainability with Factorization Trees (2019, methodology): “Some systems [27, 40] combine decision tree learning with matrix factorization to extract a list of interview questions for solving the cold-start problem in recommendation.”