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Abstract: As the last few years have seen an increase in online hostility and polarization both, we need to move beyond the fack-checking reflex or the praise for better moderation on social networking sites (SNS) and investigate their impact on social structures and social cohesion. In particular, the role of recommender systems deployed at large scale by digital platforms such as Facebook or Twitter has been overlooked. This paper draws on the literature on cognitive science, digital media, and opinion dynamics to propose a faithful replica of the entanglement between recommender systems, opinion dynamics and users' cognitive biais on SNSs like Twitter that is calibrated over a large scale longitudinal database of tweets from political activists. This model makes it possible to compare the consequences of various recommendation algorithms on the social fabric and to quantify their interaction with some major cognitive bias. In particular, we demonstrate that the recommender systems that seek to solely maximize users' engagement necessarily lead to an overexposure of users to negative content (up to 300\% for some of them), a phenomenon called algorithmic negativity bias, to a polarization of the opinion landscape, and to a concentration of social power in the hands of the most toxic users.

Abstract: Recommendation models that utilize unique identities (IDs) to represent distinct users and items have been state-of-the-art (SOTA) and dominated the recommender systems (RS) literature for over a decade. Meanwhile, the pre-trained modality encoders, such as BERT and ViT, have become increasingly powerful in modeling the raw modality features of an item, such as text and images. Given this, a natural question arises: can a purely modality-based recommendation model (MoRec) outperforms or matches a pure ID-based model (IDRec) by replacing the itemID embedding with a SOTA modality encoder? In fact, this question was answered ten years ago when IDRec beats MoRec by a strong margin in both recommendation accuracy and efficiency. We aim to revisit this old' question and systematically study MoRec from several aspects.

Deep recommender systems (DRS) are intensively applied in modern web services. To deal with the massive web contents, DRS employs a two-stage workflow: retrieval and ranking, to generate its recommendation results. The retriever aims to select a small set of relevant candidates from the entire items with high efficiency; while the ranker, usually more precise but time-consuming, is supposed to further refine the best items from the retrieved candidates. Traditionally, the two components are trained either independently or within a simple cascading pipeline, which is prone to poor collaboration effect. Though some latest works suggested to train retriever and ranker jointly, there still exist many severe limitations: item distribution shift between training and inference, false negative, and misalignment of ranking order. As such, it remains to explore effective collaborations between retriever and ranker.

Conversational AI has seen an exponential rise in popularity in the last decade and has become mainstream over the past two years. Enterprise adoption of Conversational AI is accelerating. Businesses are investing millions of dollars for conversational AI applications with massive expectations to improve customer experience and operational efficiencies. Chatbots and Virtual Assistants are leveraging AI technologies to provide innovative and efficient solutions benefitting customers, employees, and partners. In this scenario, it is crucial to understand the fundamental differences between a chatbot and a more sophisticated solution like a virtual assistant.

The Era 100 is the first of a brand new line of wifi speakers from multi-room audio specialists Sonos, taking what was good about its popular longstanding One series and adding more bass and stereo sound. With a similar aesthetic to the outgoing One, it is only 2cm taller and 1cm deeper, making it pretty compact and easy to place on a cabinet or bookshelf. It requires just a power cable, connecting to your router via wifi 6 for streaming music from more than 100 different services, including Spotify and BBC Sounds, controlled from the Sonos app on your phone. In a first for Sonos's non-portable speakers, it also supports Bluetooth 5 for impromptu streaming from guest's phones or other Bluetooth devices, which works great. A button on the top turns the voice assistant on or off.

AI-powered healthcare: AI can be used to analyze medical data and assist doctors in making diagnoses and treatment plans. Smart homes and cities: AI can be used to automate household tasks and manage energy usage in smart homes, and in cities, it can be used to manage traffic flow, optimize public transportation, and enhance public safety. Autonomous vehicles: AI can power self-driving cars, trucks, and other vehicles, which can reduce traffic accidents, increase efficiency, and save time. Virtual assistants: AI-powered virtual assistants like Siri, Alexa, and Google Assistant are already widely used, but they are expected to become even more sophisticated and personalized in the future. Improved education: AI can be used to provide personalized education to students, track their progress, and identify areas where they need additional help. However, along with these advancements, AI also presents some challenges that need to be addressed.

This paper explores a novel application of textual semantic similarity to user-preference representation for rating prediction. The approach represents a user's preferences as a graph of textual snippets from review text, where the edges are defined by semantic similarity. This textual, memory-based approach to rating prediction enables review-based explanations for recommendations. The method is evaluated quantitatively, highlighting that leveraging text in this way outperforms both strong memory-based and model-based collaborative filtering baselines.

Explainable AI (XAI) has established itself as an important component of AI-driven interactive systems. With Augmented Reality (AR) becoming more integrated in daily lives, the role of XAI also becomes essential in AR because end-users will frequently interact with intelligent services. However, it is unclear how to design effective XAI experiences for AR. We propose XAIR, a design framework that addresses "when", "what", and "how" to provide explanations of AI output in AR. The framework was based on a multi-disciplinary literature review of XAI and HCI research, a large-scale survey probing 500+ end-users' preferences for AR-based explanations, and three workshops with 12 experts collecting their insights about XAI design in AR. XAIR's utility and effectiveness was verified via a study with 10 designers and another study with 12 end-users. XAIR can provide guidelines for designers, inspiring them to identify new design opportunities and achieve effective XAI designs in AR.

Graph Convolutional Networks (GCN) have been recently employed as core component in the construction of recommender system algorithms, interpreting user-item interactions as the edges of a bipartite graph. However, in the absence of side information, the majority of existing models adopt an approach of randomly initialising the user embeddings and optimising them throughout the training process. This strategy makes these algorithms inherently transductive, curtailing their ability to generate predictions for users that were unseen at training time. To address this issue, we propose a convolution-based algorithm, which is inductive from the user perspective, while at the same time, depending only on implicit user-item interaction data. We propose the construction of an item-item graph through a weighted projection of the bipartite interaction network and to employ convolution to inject higher order associations into item embeddings, while constructing user representations as weighted sums of the items with which they have interacted. Despite not training individual embeddings for each user our approach achieves state-of-the-art recommendation performance with respect to transductive baselines on four real-world datasets, showing at the same time robust inductive performance.