Neural Collaborative Filtering

In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny. In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation -- collaborative filtering -- on the basis of implicit feedback. Although some recent work has employed deep learning for recommendation, they primarily used it to model auxiliary information, such as textual descriptions of items and acoustic features of musics. When it comes to model the key factor in collaborative filtering -- the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items. By replacing the inner product with a neural architecture that can learn an arbitrary function from data, we present a general framework named NCF, short for Neural network-based Collaborative Filtering. NCF is generic and can express and generalize matrix factorization under its framework. To supercharge NCF modelling with non-linearities, we propose to leverage a multi-layer perceptron to learn the user-item interaction function. Extensive experiments on two real-world datasets show significant improvements of our proposed NCF framework over the state-of-the-art methods. Empirical evidence shows that using deeper layers of neural networks offers better recommendation performance.Comment: 10 pages, 7 figure

A Survey of e-Commerce Recommender Systems

Due to their powerful personalization and efficiency features, recommendation systems are being used extensively in many online environments. Recommender systems provide great opportunities to businesses, therefore research on developing new recommender system techniques and methods have been receiving increasing attention. This paper reviews recent developments in recommender systems in the domain of ecommerce. The main purpose of the paper is to summarize and compare the latest improvements of e-commerce recommender systems from the perspective of e-vendors. By examining the recent publications in the field, our research provides thorough analysis of current advancements and attempts to identify the existing issues in recommender systems. Final outcomes give practitioners and researchers the necessary insights and directions on recommender systems

Considering temporal aspects in recommender systems: a survey

Under embargo until: 2023-07-04The widespread use of temporal aspects in user modeling indicates their importance, and their consideration showed to be highly effective in various domains related to user modeling, especially in recommender systems. Still, past and ongoing research, spread over several decades, provided multiple ad-hoc solutions, but no common understanding of the issue. There is no standardization and there is often little commonality in considering temporal aspects in different applications. This may ultimately lead to the problem that application developers define ad-hoc solutions for their problems at hand, sometimes missing or neglecting aspects that proved to be effective in similar cases. Therefore, a comprehensive survey of the consideration of temporal aspects in recommender systems is required. In this work, we provide an overview of various time-related aspects, categorize existing research, present a temporal abstraction and point to gaps that require future research. We anticipate this survey will become a reference point for researchers and practitioners alike when considering the potential application of temporal aspects in their personalized applications.acceptedVersio

Toward Sustainable Recommendation Systems

Recommendation systems are ubiquitous, acting as an essential component in online platforms to help users discover items of interest. For example, streaming services rely on recommendation systems to serve high-quality informational and entertaining content to their users, and e-commerce platforms recommend interesting items to assist customers in making shopping decisions. Further-more, the algorithms and frameworks driving recommendation systems provide the foundation for new personalized machine learning methods that have wide-ranging impacts. While successful, many current recommendation systems are fundamentally not sustainable: they focus on short-lived engagement objectives, requiring constant fine-tuning to adapt to the dynamics of evolving systems, or are subject to performance degradation as users and items churn in the system. In this dissertation research, we seek to lay the foundations for a new class of sustainable recommendation systems. By sustainable, we mean a recommendation system should be fundamentally long-lived, while enhancing both current and future potential to connect users with interesting content. By building such sustainable recommendation systems, we can continuously improve the user experience and provide a long-lived foundation for ongoing engagement. Building on a large body of work in recommendation systems, with the advance in graph neural networks, and with recent success in meta-learning for ML-based models, this dissertation focuses on sustainability in recommendation systems from the following three perspectives with corresponding contributions: • Adaptivity: The first contribution lies in capturing the temporal effects from the instant shifting of users’ preferences to the lifelong evolution of users and items in real-world scenarios, leading to models which are highly adaptive to the temporal dynamics present in online platforms and provide improved item recommendation at different timestamps. • Resilience: Secondly, we seek to identify the elite users who act as the “backbone” recommendation systems shape the opinions of other users via their public activities. By investigating the correlation between user’s preference on item consumption and their connections to the “backbone”, we enable recommendation models to be resilient to dramatic changes including churn in new items and users, and frequently updated connections between users in online communities. • Robustness: Finally, we explore the design of a novel framework for “learning-to-adapt” to the imperfect test cases in recommendation systems ranging from cold-start users with few interactions to casual users with low activity levels. Such a model is robust to the imperfection in real-world environments, resulting in reliable recommendation to meet user needs and aspirations

Temporal Aspect Aware Graph Neural Network in Cybersecurity

Žít v dynamickém světě znamená řešit časově závislé úlohy. Avšak moderní nástroje pro strojové učení na grafech jsou především navržené pro statické sítě. Proto se v této závěrečné práci detailně zabývám problematikou strojového učení respektujícího časový aspekt pro grafové úlohy. Výsledkem tohoto teoretického výzkumu je návrh dynamické grafové neuronové sítě se spojitým časem. Zaměřuji se na problém Cisco Cognitive Intelligence maliciousness classification --- úlohu odhalení internetových domén s bezpečnostním rizikem na základě interakcí mezi uživateli a doménami. Ukazuji, že tento problém lze efektivně vyřešit použitím různých přístupů strojového učení, včetně navrženého. Navíc demonstruji, že obecné zákonitostí bezpečnostního rizika domén nevykazují dynamické vlastnosti v uvažovaných datech z reálného světa.Living in a dynamic world means dealing with time-dependent tasks. However, the modern toolbox for machine learning on graphs is mainly designed for static networks. Therefore, in this thesis, I deepen into the problematics of temporal-aware machine learning approaches for graph problems. The outcome of this study is a proposal for the new continuous-time dynamic graph neural network. I focus on the Cisco Cognitive Intelligence maliciousness classification problem --- the task of malicious Internet domain exposure based on user-domain interactions. I demonstrate that this problem can be efficiently solved employing different approaches, including the proposed one. Moreover, I show that general maliciousness patterns do not exhibit dynamic properties in the considered real-world data

Modeling Multi-aspect Preferences and Intents for Multi-behavioral Sequential Recommendation

Multi-behavioral sequential recommendation has recently attracted increasing attention. However, existing methods suffer from two major limitations. Firstly, user preferences and intents can be described in fine-grained detail from multiple perspectives; yet, these methods fail to capture their multi-aspect nature. Secondly, user behaviors may contain noises, and most existing methods could not effectively deal with noises. In this paper, we present an attentive recurrent model with multiple projections to capture Multi-Aspect preferences and INTents (MAINT in short). To extract multi-aspect preferences from target behaviors, we propose a multi-aspect projection mechanism for generating multiple preference representations from multiple aspects. To extract multi-aspect intents from multi-typed behaviors, we propose a behavior-enhanced LSTM and a multi-aspect refinement attention mechanism. The attention mechanism can filter out noises and generate multiple intent representations from different aspects. To adaptively fuse user preferences and intents, we propose a multi-aspect gated fusion mechanism. Extensive experiments conducted on real-world datasets have demonstrated the effectiveness of our model