Regression-based latent factor models

Agarwal, Deepak; Chen, Bee-Chung

doi:10.1145/1557019.1557029

Cited by 436 publications

(393 citation statements)

References 27 publications

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“…Other similar implementations can be found in (Chen et al, 2011;Agarwal and Chen, 2009;Bayer, 2015). Let us remark that the goal of this paper is not to present another implementation.…”

Section: Related Workmentioning

confidence: 99%

Mapping preferences into Euclidean space

Luaces

Díez

Joachims

et al. 2015

Expert Systems with Applications

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Understanding and modeling human preferences is one of the key problems in applications ranging from marketing to automated recommendation. In this paper, we focus on learning and analyzing the preferences of consumers regarding food products. In particular, we explore machine learning methods that embed consumers and products in an Euclidean space such that their relationship to each other models consumer preferences. In addition to predicting preferences that were not explicitly stated, the Euclidean embedding enables visualization and clustering to understand the overall structure of a population of consumers and their preferences regarding the set of products.Notice that consumers' clusters are market segments, and products clusters can be seen as groups of similar items with respect to consumer tastes. We explore two types of Euclidean embedding of preferences, one based on inner products and one based on distances. Using a real world dataset about consumers of beef meat, we find that both embeddings produce more accurate models than a tensorial approach that uses a SVM to learn preferences. The * Corresponding author: Tel: +34 985 182 588Email addresses: oluaces@uniovi.es (Oscar Luaces), jdiez@uniovi.es (Jorge Díez), tj@cs.cornell.edu (Thorsten Joachims), abahamonde@uniovi.es (Antonio Bahamonde) Preprint submitted to Expert Systems with ApplicationsJuly 15, 2015 reason is that the number of parameters to learned in embeddings can be considerably lower than in the tensorial approach. Furthermore, we demonstrate that the visualization of the learned embeddings provides interesting insights into the structure of the consumer and product space, and that it provides a method for qualitatively explaining consumer preferences. Additionally, it is important to emphasize that the approach presented here is flexible enough to allow its use with different levels of knowledge about consumers or products; therefore the application field is very wide to grasp an accurate understanding of consumers' preferences.

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“…Other similar implementations can be found in (Chen et al, 2011;Agarwal and Chen, 2009;Bayer, 2015). Let us remark that the goal of this paper is not to present another implementation.…”

Section: Related Workmentioning

confidence: 99%

Mapping preferences into Euclidean space

Luaces

Díez

Joachims

et al. 2015

Expert Systems with Applications

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“…Note that this two-level regression scheme can be applied to matrix factorization as well as tensor factorization. The idea to use regression priors for matrix factorization has been explored by [1,30] but not yet discussed on multi-way data relations like tensors. The final graphical representation of the model is shown in Figure 3.…”

Section: Latent Factor Modelsmentioning

confidence: 99%

Learning to rank social update streams

Liu

Bekkerman

Adler

et al. 2012

Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval

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As online social media further integrates deeper into our lives, we spend more time consuming social update streams that come from our online connections. Although social update streams provide a tremendous opportunity for us to access information on-the-fly, we often complain about its relevance. Some of us are flooded with a steady stream of information and simply cannot process it in full. Ranking the incoming content becomes the only solution for the overwhelmed users. For some others, in contrast, the incoming information stream is pretty weak, and they have to actively search for relevant information which is quite tedious. For these users, augmenting their incoming content flow with relevant information from outside their first-degree network would be a viable solution. In that case, the problem of relevance becomes even more prominent.In this paper, we start an open discussion on how to build effective systems for ranking social updates from a unique perspective of LinkedIn -the largest professional network in the world. More specifically, we address this problem as an intersection of learning to rank, collaborative filtering, and clickthrough modeling, while leveraging ideas from information retrieval and recommender systems. We propose a novel probabilistic latent factor model with regressions on explicit features and compare it with a number of non-trivial baselines. In addition to demonstrating superior performance of our model, we shed some light on the nature of social updates on LinkedIn and how users interact with them, which might be applicable to social update streams in general.

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“…as the auxiliary similarity used to regularize a matrix factorization model. Incorporating temporal component and covariate into recommender system problems have been studied very recently in several papers [4,3,2]. Of these, [10] is most related to our work where the authors regularize the factors through a regression and capture temporal variation through random walk priors on each parameter.…”

Section: Data Characteristicsmentioning

confidence: 99%

A spatio-temporal approach to collaborative filtering

Agarwal

Dhillon

2009

Proceedings of the Third ACM Conference on Recommender Systems

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In this paper, we propose a novel spatio-temporal model for collaborative filtering applications. Our model is based on low-rank matrix factorization that uses a spatio-temporal filtering approach to estimate user and item factors. The spatial component regularizes the factors by exploiting correlation across users and/or items, modeled as a function of some implicit feedback (e.g., who rated what) and/or some side information (e.g., user demographics, browsing history). In particular, we incorporate correlation in factors through a Markov random field prior in a probabilistic framework, whereby the neighborhood weights are functions of user and item covariates. The temporal component ensures that the user/item factors adapt to process changes that occur through time and is implemented in a state space framework with fast estimation through Kalman filtering. Our spatio-temporal filtering (ST-KF hereafter) approach provides a single joint model to simultaneously incorporate both spatial and temporal structure in ratings and therefore provides an accurate method to predict future ratings. To ensure scalability of ST-KF, we employ a mean-field approximation for inference. Incorporating user/item covariates in estimating neighborhood weights also helps in dealing with both cold-start and warm-start problems seamlessly in a single unified modeling framework; covariates predict factors for new users and items through the neighborhood. We illustrate our method on simulated data, benchmark data and data obtained from a relatively new recommender system application arising in the context of Yahoo! Front Page.

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Regression-based latent factor models

Cited by 436 publications

References 27 publications

Mapping preferences into Euclidean space

Mapping preferences into Euclidean space

Learning to rank social update streams

A spatio-temporal approach to collaborative filtering

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