Diverse personalized recommendations with uncertainty from implicit preference data with the Bayesian Mallows Model

Clicking data, which exists in abundance and contains objective user preference information, is widely used to produce personalized recommendations in web-based applications. Current popular recommendation algorithms, typically based on matrix factorizations, often have high accuracy and achieve good clickthrough rates. However, diversity of the recommended items, which can greatly enhance user experiences, is often overlooked. Moreover, most algorithms do not produce interpretable uncertainty quantifications of the recommendations. In this work, we propose the Bayesian Mallows for Clicking Data (BMCD) method, which augments clicking data into compatible full ranking vectors by enforcing all the clicked items to be top-ranked. User preferences are learned using a Mallows ranking model. Bayesian inference leads to interpretable uncertainties of each individual recommendation, and we also propose a method to make personalized recommendations based on such uncertainties. With a simulation study and a real life data example, we demonstrate that compared to state-of-the-art matrix factorization, BMCD makes personalized recommendations with similar accuracy, while achieving much higher level of diversity, and producing interpretable and actionable uncertainty estimation.Comment: 27 page

Scalable and interpretable product recommendations via overlapping co-clustering

We consider the problem of generating interpretable recommendations by identifying overlapping co-clusters of clients and products, based only on positive or implicit feedback. Our approach is applicable on very large datasets because it exhibits almost linear complexity in the input examples and the number of co-clusters. We show, both on real industrial data and on publicly available datasets, that the recommendation accuracy of our algorithm is competitive to that of state-of-art matrix factorization techniques. In addition, our technique has the advantage of offering recommendations that are textually and visually interpretable. Finally, we examine how to implement our technique efficiently on Graphical Processing Units (GPUs).Comment: In IEEE International Conference on Data Engineering (ICDE) 201

Exact and efficient top-K inference for multi-target prediction by querying separable linear relational models

Many complex multi-target prediction problems that concern large target spaces are characterised by a need for efficient prediction strategies that avoid the computation of predictions for all targets explicitly. Examples of such problems emerge in several subfields of machine learning, such as collaborative filtering, multi-label classification, dyadic prediction and biological network inference. In this article we analyse efficient and exact algorithms for computing the top-$K$ predictions in the above problem settings, using a general class of models that we refer to as separable linear relational models. We show how to use those inference algorithms, which are modifications of well-known information retrieval methods, in a variety of machine learning settings. Furthermore, we study the possibility of scoring items incompletely, while still retaining an exact top-K retrieval. Experimental results in several application domains reveal that the so-called threshold algorithm is very scalable, performing often many orders of magnitude more efficiently than the naive approach

Prescription Based Recommender System for Diabetic Patients Using Efficient Map Reduce

Healthcare sector has been deprived of leveraging knowledge gained through data insights, due to manual processes and legacy record-keeping methods. Outdated methods for maintaining healthcare records have not been proven sufficient for treating chronic diseases like diabetes. Data analysis methods such as Recommendation System (RS) can serve as a boon for treating diabetes. RS leverages predictive analysis and provides clinicians with information needed to determine the treatments to patients. Prescription-based Health Recommender System (HRS) is proposed in this paper which aids in recommending treatments by learning from the treatments prescribed to other patients diagnosed with diabetes. An Advanced Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering is also proposed to cluster the data for deriving recommendations by using winnowing algorithm as a similarity measure. A parallel processing of data is applied using map-reduce to increase the efficiency & scalability of clustering process for effective treatment of diabetes. This paper provides a good picture of how the Map Reduce can benefit in increasing the efficiency and scalability of the HRS using clustering