Francisco J. Martin

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The number of online services providing users with real-time recommendations has increased exponentially over the last few years. Recommender Systems that were originally only accessible to a limited number of high-tech companies are now widely available through a growing number of both technical choices and vendors. The acceptance however, of automatically(More)
Diversity in machine learning APIs (in both software toolkits and web services), works against realising machine learning's full potential, making it difficult to draw on individual algorithms from different products or to compose multiple algorithms to solve complex tasks. This paper introduces the Protocols and Structures for Inference (PSI) service(More)
recommender system is a software application capable of suggesting interesting things to its users after learning their preferences over time (Jannach et al. 2010, Ricci et al. 2011). Recommender systems were envisioned in the 1970s (Negroponte 1970), conceptualized and prototyped in the early 1990s (Goldberg et al. 1992), and implemented and first(More)
In this paper we introduce aa interagent as an autonomous software agent which manages (intermedi-ates) the communication and coordination between an agent and the agent society wherein this is situated. With this aim, we have developed JIM, a general-purpose interagent that provides agents with a highly versatile range of programmable-before and during the(More)
In this paper, we describe AzureML, a web service that provides a model authoring environment where data scientists can create machine learning models and publish them easily ( In addition, AzureML provides several distinguishing features. These include: (a) collaboration, (b) versioning, (c) visual workflows, (d) external language(More)
The majority of data science and machine learning tutorials focus on generating models: assembling a dataset; splitting the data into training, validation, and testing subsets; building the model; and demonstrating its generalizability. But when it's time to repeat the analogous steps when using the model in production, issues of high latency or low(More)
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