Context-aware movie recommendation based on signal processing and machine learning

Most of the existing recommendation engines do not take into consideration contextual information for suggesting in-teresting items to users. Features such as time, location, or weather, may affect the user preferences for a particular item. In this paper, we propose two different context-aware ap-proaches for the movie recommendation task. The first is an hybrid recommender that assesses available contextual factors related to time in order to increase the performance of traditional CF approaches. The second approach aims at identifying users in a household that submitted a given rat-ing. This latter approach is based on machine learning tech-niques, namely, neural networks and majority voting classi-fiers. The effectiveness of both the approaches has been exper-imentally validated using several evaluation metrics and a large dataset

Special Issue on Human and Artificial Intelligence

Although tremendous advances have been made in recent years, many real-world problems still cannot be solved by machines alone [...

A BERT-Based Approach to Intent Recognition

Conversational agents are increasingly present in the context of Industry 4.0, in particular for customer care applications. To be really useful, these agents must correctly recognize the users' intents in order to provide them with adequate and satisfactory answers. In this paper, we introduce an intent recognition approach that leverages Google's Bidirectional Encoder Representations from Transformers (BERT), a language representation model based on deep neural networks. The results of a comparative analysis performed on three publicly available datasets, including Kaggle, Alexa, and Converse datasets, show that the proposed approach can outperform other state-of-the-art models based on different techniques

Unreliable Users Detection in Social Media: Deep Learning Techniques for Automatic Detection

Since the harmful consequences of the online publication of fake news have emerged clearly, many research groups worldwide have started to work on the design and creation of systems able to detect fake news and entities that share it consciously. Therefore, manifold automatic, manual, and hybrid solutions have been proposed by industry and academia. In this article, we describe a deep investigation of the features that both from an automatic and a human point of view, are more predictive for the identification of social network profiles accountable for spreading fake news in the online environment. To achieve this goal, the features of the monitored users were extracted from Twitter, such as social and personal information as well as interaction with content and other users. Subsequently, we performed (i) an offline analysis realized through the use of deep learning techniques and (ii) an online analysis that involved real users in the classification of reliable/unreliable user profiles. The experimental results, validated from a statistical point of view, show which information best enables machines and humans to detect malicious users. We hope that our research work will provide useful insights for realizing ever more effective tools to counter misinformation and those who spread it intentionally

Case-Based Reasoning in Robot Indoor Navigation

In this paper, we advance a novel approach to the problem of autonomous robot navigation. The environment is a complex indoor scene with very little a priori knowledge, and the navigation task is expressed in terms of natural language directives referring to natural features of the environment itself. The system is able to analyze digital images obtained by applying a sensor fusion algorithm to ultrasonic sensor readings. Such images are classified in different categories using a case-based approach. The architecture we propose relies on fuzzy theory for the construction of digital images, and wavelet functions for their representation and analysis

Current and Future of Meta-Learning

The State of the Art of the young domain of Meta-Learning [3] is held by the connectionist approach. Several techniques of such an inspiration have recently shown promising results in automatically designing neural network architectures [1]. However, apart from back-propagation, only a few applications of other learning techniques are used for these purposes. The back-propagation process takes advantage of specific optimization techniques that are best suited to some fields of applications (e.g., Computer Vision and Natural Language Processing). Hence the need for a more general learning approach, namely, a basic algorithm able to make inference in different contexts with different properties. In our research work, we deal with the problem from a scientific and epistemological point of view. We believe that this is needed to fully understand the mechanisms and dynamics underlying human learning [2]. To this aim, we define some elementary inference operations and show how modern architectures can be built by a combination of those elementary methods. We analyze each method in different settings and find the best-suited application context for each learning algorithm. Furthermore, we discuss experimental findings and compare them with human learning. The discrepancy is particularly evident between unsupervised and reinforcement learning. Then, we determine which elementary learning rules are best suited for those systems and, finally, we propose some improvements in reinforcement learning architectures