Learning multiple-question decision trees for cold-start recommendation

Sun, Mingxuan; Li, Fuxin; Lee, Joon Seok; Zhou, Ke; Lebanon, Guy; Zha, Hongyuan

doi:10.1145/2433396.2433451

Cited by 70 publications

(48 citation statements)

References 22 publications

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“…User features may include demographic information, location, activity context, and device capability. Collaborative filtering goes beyond content-based methods to correlate users and items based on the assumption that users prefer items favored by the like-minded [1,5,[13][14][15][16][17][18][19].…”

Section: Related Workmentioning

confidence: 99%

A Multi-Modality Deep Network for Cold-Start Recommendation

Sun

Zhang

2018

BDCC

Self Cite

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Collaborative filtering (CF) approaches, which provide recommendations based on ratings or purchase history, perform well for users and items with sufficient interactions. However, CF approaches suffer from the cold-start problem for users and items with few ratings. Hybrid recommender systems that combine collaborative filtering and content-based approaches have been proved as an effective way to alleviate the cold-start issue. Integrating contents from multiple heterogeneous data sources such as reviews and product images is challenging for two reasons. Firstly, mapping contents in different modalities from the original feature space to a joint lower-dimensional space is difficult since they have intrinsically different characteristics and statistical properties, such as sparse texts and dense images. Secondly, most algorithms only use content features as the prior knowledge to improve the estimation of user and item profiles but the ratings do not directly provide feedback to guide feature extraction. To tackle these challenges, we propose a tightly-coupled deep network model for fusing heterogeneous modalities, to avoid tedious feature extraction in specific domains, and to enable two-way information propagation from both content and rating information. Experiments on large-scale Amazon product data in book and movie domains demonstrate the effectiveness of the proposed model for cold-start recommendation.

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Section: Related Workmentioning

confidence: 99%

A Multi-Modality Deep Network for Cold-Start Recommendation

Sun

Zhang

2018

BDCC

Self Cite

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“…However, the cold-start problem makes it hard to give recommendations at first, when the system knows nothing about the user, including which questions to ask. Sun et al (2013) present a multiple-question decision tree for recommendation, where each node asks users for opinions on several movies, rather than just one. This model lets users sooner provide information about movies they have seen, but it is designed to minimize the number of questions and not the amount of effort required to answer questions of varying difficulty.…”

Section: Test-time Feature Selectionmentioning

confidence: 99%

Dynamic Question Ordering in Online Surveys

Early

Mankoff

Fienberg

2017

Journal of Official Statistics

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Online surveys have the potential to support adaptive questions, where later questions depend on earlier responses. Past work has taken a rule-based approach, uniformly across all respondents. We envision a richer interpretation of adaptive questions, which we call Dynamic Question Ordering (DQO), where question order is personalized. Such an approach could increase engagement, and therefore response rate, as well as imputation quality. We present a DQO framework to improve survey completion and imputation. In the general survey-taking setting, we want to maximize survey completion, and so we focus on ordering questions to engage the respondent and collect hopefully all information, or at least the information that most characterizes the respondent, for accurate imputations. In another scenario, our goal is to provide a personalized prediction. Since it is possible to give reasonable predictions with only a subset of questions, we are not concerned with motivating users to answer all questions. Instead, we want to order questions to get information that reduces prediction uncertainty, while not being too burdensome. We illustrate this framework with two case studies, for the prediction and survey-taking settings. We also discuss DQO for national surveys and consider connections between our statistics-based question-ordering approach and cognitive survey methodology.

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“…In the interview-based methods, an additional set of items is usually provided in the sign-up phase to collect the preferences of the cold-start users [11,39,32]. For example, Zhou et al…”

Section: Related Workmentioning

confidence: 99%

Dual-Regularized One-Class Collaborative Filtering

Yao

Tong

Yan

et al. 2014

Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

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Collaborative filtering is a fundamental building block in many recommender systems. While most of the existing collaborative filtering methods focus on explicit, multi-class settings (e.g., 1-5 stars in movie recommendation), many real-world applications actually belong to the one-class setting where user feedback is implicitly expressed (e.g., views in news recommendation and video recommendation). The main challenges in such one-class setting include the ambiguity of the unobserved examples and the sparseness of existing positive examples.In this paper, we propose a dual-regularized model for one-class collaborative filtering. In particular, we address the ambiguity challenge by integrating two state-of-the-art one-class collaborative filtering methods to enjoy the best of both worlds. We tackle the sparseness challenge by exploiting the side information from both users and items. Moreover, we propose efficient algorithms to solve the proposed model. Extensive experimental evaluations on two real data sets demonstrate that our method achieves significant improvement over the state-of-the-art methods. Overall, the proposed method leads to 7.9% -21.1% improvement over its best known competitors in terms of prediction accuracy, while enjoying the linear scalability.

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Learning multiple-question decision trees for cold-start recommendation

Cited by 70 publications

References 22 publications

A Multi-Modality Deep Network for Cold-Start Recommendation

A Multi-Modality Deep Network for Cold-Start Recommendation

Dynamic Question Ordering in Online Surveys

Dual-Regularized One-Class Collaborative Filtering

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