An empirical comparison of collaborative filtering approaches on netflix data

Recommender systems are widely used in E-Commerce for making automatic suggestions of new items that could meet the interest of a given user. Collaborative Filtering approaches compute recommendations by assuming that users, who have shown similar behavior in the past, will share a common behavior in the future. According to this assumption, the most effective collaborative filtering techniques try to discover groups of similar users in order to infer the preferences of the group members. The purpose of this work is to show an empirical comparison of the main collaborative filtering approaches, namely Baseline, Nearest Neighbors, Latent Factor and Probabilistic models, focusing on their strengths and weaknesses. Data used for the analysis are a sample of the well-known Netix Prize database. Copyright owned by the authors

Temporal Recurrent Activation Networks

We tackle the problem of predicting whether a target user (or group of users) will be active within an event stream before a time horizon. Our solution, called PATH, leverages recurrent neural networks to learn an embedding of the past events. The embedding allows to capture influence and susceptibility between users and places closer (the representation of) users that frequently get active in different event streams within a small time interval. We conduct an experimental evaluation on real world data and compare our approach with related work

Deep sequential modeling for recommendation

We propose a model which extends variational autoencoders by exploiting the rich information present in the past preference history. We introduce a recurrent version of the VAE, where instead of passing a subset of the whole history regardless of temporal dependencies, we rather pass the consumption sequence subset through a recurrent neural network. At each time-step of the RNN, the sequence is fed through a series of fully-connected layers, the output of which models the probability distribution of the most likely future preferences. We show that handling temporal information is crucial for improving the accuracy of recommendation

Machine learning methods for generating high dimensional discrete datasets

The development of platforms and techniques for emerging Big Data and Machine Learning applications requires the availability of real-life datasets. A possible solution is to synthesize datasets that reflect patterns of real ones using a two-step approach: first, a real dataset X is analyzed to derive relevant patterns Z and, then, to use such patterns for reconstructing a new dataset X ' that preserves the main characteristics of X. This survey explores two possible approaches: (1) Constraint-based generation and (2) probabilistic generative modeling. The former is devised using inverse mining (IFM) techniques, and consists of generating a dataset satisfying given support constraints on the itemsets of an input set, that are typically the frequent ones. By contrast, for the latter approach, recent developments in probabilistic generative modeling (PGM) are explored that model the generation as a sampling process from a parametric distribution, typically encoded as neural network. The two approaches are compared by providing an overview of their instantiations for the case of discrete data and discussing their pros and cons. This article is categorized under: Fundamental Concepts of Data and Knowledge > Big Data Mining Technologies > Machine Learning Algorithmic Development > Structure Discover

Audio-based anomaly detection on edge devices via self-supervision and spectral analysis

In real-world applications, audio surveillance is often performed by large models that can detect many types of anomalies. However, typical approaches are based on centralized solutions characterized by significant issues related to privacy and data transport costs. In addition, the large size of these models prevented a shift to contexts with limited resources, such as edge devices computing. In this work we propose conv-SPAD, a method for convolutional SPectral audio-based Anomaly Detection that takes advantage of common tools for spectral analysis and a simple autoencoder to learn the underlying condition of normality of real scenarios. Using audio data collected from real scenarios and artificially corrupted with anomalous sound events, we test the ability of the proposed model to learn normal conditions and detect anomalous events. It shows performances in line with larger models, often outperforming them. Moreover, the model’s small size makes it usable in contexts with limited resources, such as edge devices hardware

Siamese Network for Fake Item Detection

Currently, most multimedia users choose to purchase items through e-commerce. Nevertheless, one of the main concerns of online shopping is the possibility of obtaining counterfeit products. Therefore, it is crucial to monitor the authenticity of the product, thus adopting an automatic mechanism to validate the similarity between the purchased item and the delivered one. To overcome this issue, we propose a Siamese Network model for detecting forged items. Preliminary experimentation on a publicly available dataset proves the effectiveness of our solution

A deep learning approach for detecting security attacks on blockchain

In these last years, Blockchain technologies have been widely used in several application fields to improve data privacy and trustworthiness and security of systems. Although the blockchain is a powerful tool, it is not immune to cyber attacks: for instance, recently (January 2019) a successful 51% attack on Ethereum Classic has revealed security vulnerabilities of its platform. Under a statistical perspective, attacks can be seen as an anomalous observation, with a strong deviation from the regular behavior. Machine Learning is a science whose goal is to learn insights, patterns and outliers within large data repositories; hence, it can be exploit for blockchain attack detection. In this work, we define an anomaly detection system based on a encoder-decoder deep learning model, that is trained exploiting aggregate information extracted by monitoring blockchain activities. Experiments on complete historical logs of Ethereum Classic network prove the capability of the our model to effectively detect the publicly reported attacks. To the best of our knowledge, our approach is the first one that provides a comprehensive and feasible solution to monitor the security of blockchain transactions

Neuro-Symbolic techniques for Predictive Maintenance

Predictive maintenance plays a key role in the core business of the industry due to its potential in reducing unexpected machine downtime and related cost. To avoid such issues, it is crucial to devise artificial intelligence models that can effectively predict failures. Predictive maintenance current approaches have several limitations that can be overcome by exploiting hybrid approaches such as Neuro-Symbolic techniques. Neuro-symbolic models combine neural methods with symbolic ones leading to improvements in efficiency, robustness, and explainability. In this work, we propose to exploit hybrid approaches by investigating their advantage over classic predictive maintenance approaches