Browsing by Author "Almutairi, Faisal"

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    Context-Aware Recommendation-Based Learning Analytics Using Tensor and Coupled Matrix Factorization
    (2016-08) Almutairi, Faisal
    In higher education, student retention and timely graduation are enduring challenges. Educational, advising, and counseling innovations and interventions are needed to address these challenges. With the rapidly expanding collection and availability of learning data and related analytics, student performance can be accurately monitored, and possibly predicted ahead of time, thus enabling early warning and degree planning `expert systems' to provide disciplined decision support to counselors, advisors, educators -- and even help guide students in semester-to-semester course selection. Previous work in educational data mining has explored matrix factorization techniques for grade prediction, albeit without taking contextual information into account. Temporal information should be informative as it distinguishes between the different class offerings and indirectly captures student experience as well. To exploit temporal and/or other kinds of context, we develop three approaches that leverage side information besides historical grades under the framework of Collaborative Filtering (CF). Two of the proposed methods build upon Coupled Matrix Factorization (CMF) with a shared latent matrix factor. The third method utilizes tensor factorization to model grades and their context. For each method, the latent factors obtained using matrix/tensor factorization lead to a compact model which we use not only to predict the unseen grades, but also the associated contextual information. We evaluate these approaches on grade datasets obtained from the University of Minnesota. Experimental results show that quite accurate prediction is possible using even simple models, while more advanced approaches outperform the prior art in predicting randomly missing entries.
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    Latent Factorization for Hierarchical and Explainable Embeddings and Data Disaggregation
    (2021-08) Almutairi, Faisal
    A tremendous growth in data collection has been an important enabler of the recent upsurge in Machine Learning (ML) models. ML techniques involve processing, analyzing, and discovering patterns from real user generated data. These data are usually high-dimensional, sparse, incomplete, and, in many applications, are only available at coarse granularity. For instance, a location mode can be at a state-level rather than county, or a time mode can be on a monthly basis instead of weekly. These (dis)aggregation challenges in real world data raise some intriguing questions and bring some challenging tasks. Given coarse-granular/aggregated data (e.g., monthly summaries), can we recover the fine-granular data (e.g., the daily counts)? Aggregated data enjoy concise representations and thus can be stored and transferred efficiently, which is critical in the era of data deluge. On the other hand, recent ML models are data hungry and benefit from detailed data for personalized analysis and prediction. Thus, data disaggregation algorithms are becoming increasingly important in various domains. In this thesis, we provide data disaggregation frameworks for one-dimensional time series data and multidimensional (tensor) data. The developed models recognize the structure of the data and exploit it to reduce the number of unknown parameters. In a related setting, multidimensional data are often partially observed, e.g., recommender systems data are usually extremely sparse as a user interacts with only a small subset of the available items. Can we reconstruct/complete the missing data? This question is central in many recommendation and more general prediction tasks in various applications such as healthcare, learning and business analytics. A major challenge stems from the fact that the number of unknown parameters is usually much larger than the number of observed samples, which has motivated using prior information. Imposing the appropriate regularization prior limits the solution search to the ‘right’ space. In addition to sparsity, high-dimensionality also creates the challenge of ‘hiding’ the underlying structures and causes that can explain the data. In order to tackle this ‘dimensionality curse’, many dimensionality reduction (DR) methods such as principal component analysis (PCA) have been proposed. The reduced dimension data usually yields better performance in downstream tasks, such as clustering. This suggests that the underlying structure (e.g., clustering) is more pronounced in some low-dimensional space compared to the original data domain. In this thesis, we present principled approaches that bridge incorporating prior information and DR techniques. We rely on low-rank (nonnegative) matrix factorization for DR and incorporate two different types of priors: i) hierarchical tree clustering, and ii) user-item embedding relationships. Imposing these regularization priors not only improves the quality of latent representations, but it also helps reveal more of the underlying structure in latent space. The tree prior provides a meaningful hierarchical clustering in an unsupervised data-driven fashion, while the user-item relationships underpin the latent factors and explain how the resulting recommendations are formed.