Multi-Directional Detection of Scratches in Digitized Images

Publication in the conference proceedings of EUSIPCO, Glasgow, Scotland, 200

Idomaar : a framework for multi-dimensional benchmarking of recommender algorithms

In real-world scenarios, recommenders face non-functional requirements of technical nature and must handle dynamic data in the form of sequential streams. Evaluation of recommender systems must take these issues into account in order to be maximally informative. In this paper, we present Idomaar—a framework that enables the efficient multi-dimensional benchmarking of recommender algorithms. Idomaar goes beyond current academic research practices by creating a realistic evaluation environment and computing both effectiveness and technical metrics for stream-based as well as set-based evaluation. A scenario focussing on “research to prototyping to productization” cycle at a company illustrates Idomaar’s potential. We show that Idomaar simplifies testing with varying configurations and supports flexible integration of different data

A Modular Framework for Versatile Conversational Agent Building

This paper illustrates a web-based infrastructure of an architecture for conversational agents equipped with a modular knowledge base. This solution has the advantage to allow the building of specific modules that deal with particular features of a conversation (ranging from its topic to the manner of reasoning of the chatbot). This enhances the agent interaction capabilities. The approach simplifies the chatbot knowledge base design process: extending, generalizing or even restricting the chatbot knowledge base in order to suit it to manage specific dialoguing tasks as much as possible

Sub-Symbolic Mapping of Cyc Microtheories in Data-Driven 'Conceptual' Spaces

The presented work aims to combine statistical and cognitive-oriented approaches with symbolic ones so that a conceptual similarity relationship layer can be added to a Cyc KB microtheory. Given a specific microtheory, a LSA-inspired conceptual space is inferred from a corpus of texts created using both ad hoc extracted pages from the Wikipedia repository and the built-in comments about the concepts of the specific Cyc microtheory. Each concept is projected in the conceptual space and the desired layer of sub-symbolic relationships between concepts is created. This procedure can help a user in finding the concepts that are “sub-symbolically conceptually related” to a new concept that he wants to insert in the microtheory. Experimental results involving two Cyc microtheories are also reported

ContentWise Impressions: An Industrial Dataset with Impressions Included

In this article, we introduce the dataset dataset, a collection of implicit interactions and impressions of movies and TV series from an Over-The-Top media service, which delivers its media contents over the Internet. The dataset is distinguished from other already available multimedia recommendation datasets by the availability of impressions, idest the recommendations shown to the user, its size, and by being open-source. We describe the data collection process, the preprocessing applied, its characteristics, and statistics when compared to other commonly used datasets. We also highlight several possible use cases and research questions that can benefit from the availability of user impressions in an open-source dataset. Furthermore, we release software tools to load and split the data, as well as examples of how to use both user interactions and impressions in several common recommendation algorithms

Outfit completion and clothes recommendation

Recommending fashion outfits requires learning a concept of style and fashionability that is typically human. There has been an increasing research effort into creating Machine Learning models able to learn such concepts, in order to distinguish between compatible and incompatible clothes and to select an item that would complete an outfit. However, most of the work done in literature tackles this problem from a pure Machine Learning point of view, disregarding real-case scenarios and the human interaction with systems able to generate outfits. This work tries to move the problem of generating outfits to the Recommender Systems domain by presenting as its main contribution a novel algorithm for a fashion-specific Recommender System that generates fashionable outfits, able to scale its inference time to be useful in real use case scenarios, and applies such algorithm on public and industrial datasets. In addition to this, this work shows preliminary results on how this algorithm can be employed in a real scenario and reports as preliminary results the evaluations provided by three professional stylists on the outfits generated by such algorithms