Content metadata plays a very important role in movie recommender systems as it provides valuable information about various aspects of a movie such as genre, cast, plot synopsis, box office summary, etc. Analyzing the metadata can help understand the user preferences to generate personalized recommendations and item cold starting. In this talk, we will focus on one particular type of metadata - genre labels. Genre labels associated with a movie or a TV series help categorize a collection of titles into different themes and correspondingly setting up the audience expectation. We present some of the challenges associated with using genre label information and propose a new way of examining the genre information that we call as the Genre Spectrum. The Genre Spectrum helps capture the various nuanced genres in a title and our offline and online experiments corroborate the effectiveness of the approach. Furthermore, we also talk about applications of LLMs in augmenting content metadata which could eventually be used to achieve effective organization of recommendations in user’s 2-D home-grid.

In many recommendation settings, the candidate items for recommendation are associated with a maximum capacity, i.e., number of seats in a Point-of-Interest (POI) or number of item copies in the inventory. However, despite the prevalence of the capacity constraint in the recommendation process, the existing recommendation methods are not designed to optimize for respecting such a constraint. Towards closing this gap, we propose Recommendation with Capacity Constraints -- a framework that optimizes for both recommendation accuracy and expected item usage that respects the capacity constraints. We show how to apply our method to three state-of-the-art latent factor recommendation models: probabilistic matrix factorization (PMF), bayesian personalized ranking (BPR) for item recommendation, and geographical matrix factorization (GeoMF) for POI recommendation. Our experiments indicate that our framework is effective for providing good recommendations while taking the limited resources into consideration. Interestingly, our methods are shown in some cases to further improve the top-N recommendation quality of the respective unconstrained models.

Collaborative filtering (CF) has been widely employed within recommender systems to solve many real-world problems. Learning effective latent factors plays the most important role in collaborative filtering. Traditional CF methods based upon matrix factorization techniques learn the latent factors from the user-item ratings and suffer from the cold start problem as well as the sparsity problem. Some improved CF methods enrich the priors on the latent factors by incorporating side information as regularization. However, the learned latent factors may not be very effective due to the sparse nature of the ratings and the side information. To tackle this problem, we learn effective latent representations via deep learning. Deep learning models have emerged as very appealing in learning effective representations in many applications. In particular, we propose a general deep architecture for CF by integrating matrix factorization with deep feature learning. We provide a natural instantiations of our architecture by combining probabilistic matrix factorization with marginalized denoising stacked auto-encoders. The combined framework leads to a parsimonious fit over the latent features as indicated by its improved performance in comparison to prior state-of-art models over four large datasets for the tasks of movie/book recommendation and response prediction.

Matrix factorization (MF) collaborative filtering is an effective and widely used method in recommendation systems. However, the problem of finding an optimal trade-off between exploration and exploitation (otherwise known as the bandit problem), a crucial problem in collaborative filtering from cold-start, has not been previously addressed.In this paper, we present a novel algorithm for online MF recommendation that automatically combines finding the most relevantitems with exploring new or less-recommended items.Our approach, called Particle Thompson Sampling for Matrix-Factorization, is based on the general Thompson sampling framework, but augmented with a novel efficient online Bayesian probabilistic matrix factorization method based on the Rao-Blackwellized particle filter.Extensive experiments in collaborative filtering using several real-world datasets demonstrate that our proposed algorithm significantly outperforms the current state-of-the-arts.

Conversion prediction and click prediction are two important and intertwined problems in display advertising, but existing approaches usually look at them in isolation. In this paper, we aim to predict the conversion response of users by jointly examining the past purchase behavior and the click response behavior. Additionally, we model the temporal dynamics between the click response and purchase activity into a unified framework. In particular, a novel matrix factorization approach named dynamic collective matrix factorization (DCMF) is proposed to address this problem. Our model considers temporal dynamics of post-click conversions and also takes advantages of the side information of users, advertisements, and items. Experiments on a real-world marketing dataset show that our model achieves significant improvements over several baselines.

Constrained spectral clustering is a semi-supervised learning problem that aims at incorporating user-defined constraints in spectral clustering. Typically, there are two kinds of constraints: (i) must-link, and (ii) cannot-link. These constraints represent prior knowledge indicating whether two data objects should be in the same cluster or not; thereby aiding in clustering. In this paper, we propose a novel approach that uses convex subproblems to incorporate constraints in spectral clustering and co-clustering. In comparison to the prior state-of-art approaches, our approach presents a more natural way to incorporate constraints in the spectral methods and allows us to make a trade off between the number of satisfied constraints and the quality of partitions on the original graph. We use an L1 regularizer analogous to LASSO, often used in literature to induce sparsity, in order to control the number of constraints satisfied. Our approach can handle both must-link and cannot-link constraints, unlike a large number of previous approaches that mainly work on the former. Further, our formulation is based on the reduction to a convex subproblem which is relatively easy to solve using existing solvers. We test our proposed approach on real world datasets and show its effectiveness for both spectral clustering and co-clustering over the prior state-of-art.

Dipoles represent long distance connections between the pressure anomalies of two distant regions that are negatively correlated with each other. Such dipoles have proven important for understanding and explaining the variability in climate in many regions of the world, e.g., the El Nino climate phenomenon is known to be responsible for precipitation and temperature anomalies over large parts of the world. Systematic approaches for dipole detection generate a large number of candidate dipoles, but there exists no method to evaluate the significance of the candidate teleconnections. In this paper, we present a novel method for testing the statistical significance of the class of spatio-temporal teleconnection patterns called as dipoles. One of the most important challenges in addressing significance testing in a spatio-temporal context is how to address the spatial and temporal dependencies that show up as high autocorrelation. We present a novel approach that uses the wild bootstrap to capture the spatio-temporal dependencies, in the special use case of teleconnections in climate data. Our approach to find the statistical significance takes into account the autocorrelation, the seasonality and the trend in the time series over a period of time. This framework is applicable to other problems in spatio-temporal data mining to assess the significance of the patterns.

A forest canopy forms a critical platform for complex interactions between the vegetation and the atmosphere boundary layer and is considered as a crucial piece for environmental scientists in their understanding of the ecosystem and its response to the climate change. Microfronts represent a class of these interactions characterized by a moving mass of air that introduce fluctuations in ambient temperature and humidity on small spatial and temporal scales. In this paper, we present a joint spatio-temporal hidden markov model that simultaneously incorporates neighborhood dependencies in space and time. We show that our approach can trace the diffusion of microfronts more effectively than several baseline methods over a sensor data from Brazilian rainforest and a synthetically generated dataset.

Pressure dipoles are important long distance climate phenomena (teleconnection) characterized by pressure anomalies of opposite polarity appearing at two different locations at the same time. Such dipoles have proven important for understanding and explaining the variability in climate in many regions of the world, e.g., the El Niño climate phenomenon is known to be responsible for precipitation and temperature anomalies worldwide. This paper presents a novel approach for dipole discovery that outperforms existing state of the art algorithms. Our approach is based on a climate anomaly network that is constructed using the correlation of time series of climate variables at all the locations on the Earth. One novel aspect of our approach to the analysis of such networks is a careful treatment of negative correlations, whose proper consideration is critical for finding dipoles. Another key insight provided by our work is the importance of modeling the time dependent patterns of the dipoles in order to better capture the impact of important climate phenomena on land. The results presented in this paper show that these innovations allow our approach to produce better results than previous approaches in terms of matching existing climate indices with high correlation and capturing the impact of climate indices on land.