Dynamic Poisson Factorization

Models for recommender systems use latent factors to explain the preferences and behaviors of users with respect to a set of items (e.g., movies, books, academic papers). Typically, the latent factors are assumed to be static and, given these factors, the observed preferences and behaviors of users are assumed to be generated without order. These assumptions limit the explorative and predictive capabilities of such models, since users' interests and item popularity may evolve over time. To address this, we propose dPF, a dynamic matrix factorization model based on the recent Poisson factorization model for recommendations. dPF models the time evolving latent factors with a Kalman filter and the actions with Poisson distributions. We derive a scalable variational inference algorithm to infer the latent factors. Finally, we demonstrate dPF on 10 years of user click data from arXiv.org, one of the largest repository of scientific papers and a formidable source of information about the behavior of scientists. Empirically we show performance improvement over both static and, more recently proposed, dynamic recommendation models. We also provide a thorough exploration of the inferred posteriors over the latent variables.Comment: RecSys 201

A systematic mapping study

Corte-Real, N., Ruivo, P., & Oliveira, T. (2014). The diffusion stages of business intelligence & analytics (BI&A):: A systematic mapping study. In Procedia Technology (Vol. 16, pp. 172-179). (Procedia Technology). DOI: 10.1016/j.protcy.2014.10.080Business intelligence & analytics (BI&A) has evolved to become a foundational cornerstone of enterprise decision support. Since the way BI&A is implemented and assimilated is quite different among organizations is important to approach BI&A literature by four selected diffusion stages (adoption, implementation, use and impacts of use). The diffusion stages assume a crucial importance to track the BI&A evolution in organizations and justify the investment made. The main focus of this paper is to evidence BI&A research on its several diffusion stages. It provides an updated bibliography of BI&A articles published in the IS journal and conferences during the period of 2000 and 2013. A total of 30 articles from 11 journals and 8 conferences are reviewed. This study contributes to the BI&A research in three ways. This is the first systematic mapping study focused on BI&A diffusion stages. It contributes to see how BI&A stages have been analyzed (theories used, data collection methods, analysis methods and publication source). Finally, it observes that little attention has been given to BI&A post-adoption stages and proposes future research line on this area.publishersversionpublishe

CnGAN: Generative Adversarial Networks for Cross-network user preference generation for non-overlapped users

A major drawback of cross-network recommender solutions is that they can only be applied to users that are overlapped across networks. Thus, the non-overlapped users, which form the majority of users are ignored. As a solution, we propose CnGAN, a novel multi-task learning based, encoder-GAN-recommender architecture. The proposed model synthetically generates source network user preferences for non-overlapped users by learning the mapping from target to source network preference manifolds. The resultant user preferences are used in a Siamese network based neural recommender architecture. Furthermore, we propose a novel user based pairwise loss function for recommendations using implicit interactions to better guide the generation process in the multi-task learning environment.We illustrate our solution by generating user preferences on the Twitter source network for recommendations on the YouTube target network. Extensive experiments show that the generated preferences can be used to improve recommendations for non-overlapped users. The resultant recommendations achieve superior performance compared to the state-of-the-art cross-network recommender solutions in terms of accuracy, novelty and diversity

Feedback-Based Self-Learning in Large-Scale Conversational AI Agents

Today, most large-scale conversational AI agents (e.g. Alexa, Siri, or Google Assistant) are built using manually annotated data to train the different components of the system. Typically, the accuracy of the ML models in these components are improved by manually transcribing and annotating data. As the scope of these systems increase to cover more scenarios and domains, manual annotation to improve the accuracy of these components becomes prohibitively costly and time consuming. In this paper, we propose a system that leverages user-system interaction feedback signals to automate learning without any manual annotation. Users here tend to modify a previous query in hopes of fixing an error in the previous turn to get the right results. These reformulations, which are often preceded by defective experiences caused by errors in ASR, NLU, ER or the application. In some cases, users may not properly formulate their requests (e.g. providing partial title of a song), but gleaning across a wider pool of users and sessions reveals the underlying recurrent patterns. Our proposed self-learning system automatically detects the errors, generate reformulations and deploys fixes to the runtime system to correct different types of errors occurring in different components of the system. In particular, we propose leveraging an absorbing Markov Chain model as a collaborative filtering mechanism in a novel attempt to mine these patterns. We show that our approach is highly scalable, and able to learn reformulations that reduce Alexa-user errors by pooling anonymized data across millions of customers. The proposed self-learning system achieves a win/loss ratio of 11.8 and effectively reduces the defect rate by more than 30% on utterance level reformulations in our production A/B tests. To the best of our knowledge, this is the first self-learning large-scale conversational AI system in production.Comment: 8 pages, 2 figure

Show Your Face! Investigating the Relationship Between Human Faces and Music’s Success

Streaming services are becoming the primary source for media consumption. Particularly platforms like SoundCloud, where users can disseminate user-generated content (UGC), are gaining relevance. To shed light into the drivers which positively influence the number of listeners, we draw from marketing literature related to depictions of people, which suggests that human faces can contribute to a higher degree of brand liking or brand identification. Thereupon, we propose a hypothesis which suggests that human faces on cover arts likewise generate more plays. We follow a data science approach using 1754 observations from SoundCloud and apply Google’s facial recognition API (Vision AI) to examine the impact of human faces on music’s success. We provide initial evidence that tracks with a human-face cover art yield in a higher number of plays compared to tracks with a cover art without a human face