A probabilistic framework to incorporate mixed-data type features: Matrix factorization with multimodal side information

Aktukmak, Mehmet; Yılmaz, Yasin; Uysal, Ismail

doi:10.1016/j.neucom.2019.08.019

Cited by 13 publications

(3 citation statements)

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“…Variational Bayesian Principal Component Analysis (VBPCA) is a fully bayesian treatment of the linear latent variable model defined above, specifically proposed for missing value estimation [15]. Bayesian treatment is especially beneficial for application domains containing high fraction of missing values such as Recommender systems [30], [31]. Unlike PPCA models [29], VBPCA algorithm treats all the model parameters as random variables in addition to the factors.…”

Section: ) Bayesian Methodsmentioning

confidence: 99%

Self-Supervised Feature Specific Neural Matrix Completion

2020

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Unsupervised matrix completion algorithms mostly model the data generation process by using linear latent variable models. Recently proposed algorithms introduce non-linearity via multi-layer perceptrons (MLP), and self-supervision by setting separate linear regression frameworks for each feature to estimate the missing values. In this paper, we introduce an MLP based algorithm called feature-specific neural matrix completion (FSNMC), which combines self-supervised and non-linear methods. The model parameters are estimated by a rotational scheme which separates the parameter and missing value updates sequentially with additional heuristic steps to prevent over-fitting and speed up convergence. The proposed algorithm specifically targets small to medium sized datasets. Experimental results on real-world and synthetic datasets varying in size with a range of missing value percentages demonstrate the superior accuracy for FSNMC, especially at low sparsities when compared to popular methods in the literature. The proposed method has particular potential in estimating missing data collected via real experimentation in fundamental life sciences.

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Section: ) Bayesian Methodsmentioning

confidence: 99%

Self-Supervised Feature Specific Neural Matrix Completion

2020

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“…If a consumer has financial limitations while also having cultural constraints on food consumption, a thorough recommendation system can recommend meals that fit their constraints and/or preferences. Examples of a recommendation model capable of considering such heterogeneous data are factorization models as well as deep learning-based recommendation models [6,84].…”

Section: Dataset Typesmentioning

confidence: 99%

A Survey on Healthy Food Decision Influences Through Technological Innovations

Marshall

Jiménez-Pazmino

Metoyer

et al. 2022

ACM Trans. Comput. Healthcare

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It is well known that unhealthy food consumption plays a significant role in dietary and lifestyle-related diseases. Therefore, it is important for researchers to examine methods that may encourage the consumer to consider healthier dietary and lifestyle habits as diseases such as obesity, heart disease, and high blood pressure remain a worldwide issue. One promising approach to influencing healthy dietary and lifestyle habits is food recommendation models that recommend food to users based on various factors such as health effects, nutrition, preferences, and daily habits. Unfortunately, much of this work has focused on individual factors such as taste preferences and often neglects to understand other factors that influence our choices. Additionally, the evaluation of technological approaches often lacks user studies in the context of intended use. In this systematic review of food choice technology, we focus on the factors that may influence food choices and how technology can play a role in supporting those choices. We also describe existing work, approaches, trends, and issues in current food choice technology and give advice for future work areas in this space.

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“…Our work incorporates residents' feedback whenever they override the HEM system's commands, a practical and novel way of extending the success of recommender systems (e.g., movie, book, shopping, video) to HEM. Recommender systems learn from customer usage patterns to recommend items/services [31]. A similar approach can be integrated into a HEM system by accommodating human input in a meaningful way.…”

Section: Human Feedback and Activity For Hemmentioning

confidence: 99%