Online stores and service providers rely heavily on recommendation softwares to guide users through the vast amount of available products. Consequently, the field of recommender systems has attracted increased attention from the industry and academia alike, but despite this joint effort, the field still faces several challenges. For instance, most existing work models the recommendation problem as a matrix completion problem to predict the user preference for an item. This abstraction prevents the system from utilizing the rich information from the ordered sequence of user actions logged in online sessions. To address this limitation, researchers have recently developed a promising new breed of algorithms called sequence-aware recommender systems to predict the user's next action by utilizing the time series composed of the sequence of actions in an ongoing user session. This paper proposes a novel sequence-aware recommendation approach based on a complex network generated by the hidden metric space model, which combines node similarity and popularity to generate links. We build a network model from data and then use it to predict the user's subsequent actions. The network model provides an additional source of information that improves the accuracy of the recommendations. The proposed method is implemented and tested experimentally on a large dataset. The results prove that the proposed approach performs better than state-of-the-art recommendation methods.

Voice user interfaces and digital assistants are rapidly entering our lives and becoming singular touch points spanning our devices. These always-on services capture and transmit our audio data to powerful cloud services for further processing and subsequent actions. Our voices and raw audio signals collected through these devices contain a host of sensitive paralinguistic information that is transmitted to service providers regardless of deliberate or false triggers. As our emotional patterns and sensitive attributes like our identity, gender, well-being, are easily inferred using deep acoustic models, we encounter a new generation of privacy risks by using these services. One approach to mitigate the risk of paralinguistic-based privacy breaches is to exploit a combination of cloud-based processing with privacy-preserving, on-device paralinguistic information learning and filtering before transmitting voice data. In this paper we introduce EDGY, a configurable, lightweight, disentangled representation learning framework that transforms and filters high-dimensional voice data to identify and contain sensitive attributes at the edge prior to offloading to the cloud. We evaluate EDGY's on-device performance and explore optimization techniques, including model quantization and knowledge distillation, to enable private, accurate and efficient representation learning on resource-constrained devices. Our results show that EDGY runs in tens of milliseconds with 0.2% relative improvement in "zero-shot" ABX score or minimal performance penalties of approximately 5.95% word error rate (WER) in learning linguistic representations from raw voice signals, using a CPU and a single-core ARM processor without specialized hardware.

Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative Filtering. Following the convention of RS, existing methods exploit unique user representation in their model design. This paper focuses on a challenging scenario where a user has multiple categories of interests. Under this setting, we argue that the unique user representation might induce preference bias, especially when the item category distribution is imbalanced. To address this issue, we propose a novel method called \textit{Diversity-Promoting Collaborative Metric Learning} (DPCML), with the hope of considering the commonly ignored minority interest of the user. The key idea behind DPCML is to include a multiple set of representations for each user in the system. Based on this embedding paradigm, user preference toward an item is aggregated from different embeddings by taking the minimum item-user distance among the user embedding set. Furthermore, we observe that the diversity of the embeddings for the same user also plays an essential role in the model. To this end, we propose a \textit{diversity control regularization} term to accommodate the multi-vector representation strategy better. Theoretically, we show that DPCML could generalize well to unseen test data by tackling the challenge of the annoying operation that comes from the minimum value. Experiments over a range of benchmark datasets speak to the efficacy of DPCML.

Alexa, Siri and Google Home might be making children less intelligent and socially stunted, it was claimed today. The voice-controlled devices -- popular in homes across the world -- allow users to ask questions and receive answers. But this may impede youngster's learning skills, critical thinking and empathy, says Dr Anmol Arora, a researcher at Cambridge University. Dr Anmol Arora, a researcher at Cambridge University, says this is down to the tech only offering short and concise answers to questions, inappropriate responses and being unable to give feedback on their social skills. Alexa, Siri and Google Home might be making children less intelligent and socially stunted, according to an artificial intelligence expert.

What do I mean by "recommender systems", and why are they useful? Let's look at the top 3 websites on the Internet, according to Alexa: Google, YouTube, and Facebook. Recommender systems form the very foundation of these technologies. They are why Google is the most successful technology company today. I'm sure I'm not the only one who's accidentally spent hours on YouTube when I had more important things to do! Just how do they convince you to do that?

Recommender systems have become an essential tool for providers and users of online services and goods, especially with the increased use of the Internet to access information and purchase products and services. This work proposes a novel recommendation method based on complex networks generated by a similarity-popularity model to predict ones. We first construct a model of a network having users and items as nodes from observed ratings and then use it to predict unseen ratings. The prospect of producing accurate rating predictions using a similarity-popularity model with hidden metric spaces and dot-product similarity is explored. The proposed approach is implemented and experimentally compared against baseline and state-of-the-art recommendation methods on 21 datasets from various domains. The experimental results demonstrate that the proposed method produces accurate predictions and outperforms existing methods. We also show that the proposed approach produces superior results in low dimensions, proving its effectiveness for data visualization and exploration.

How might we design Reinforcement Learning (RL)-based recommenders that encourage aligning user trajectories with the underlying user satisfaction? Three research questions are key: (1) measuring user satisfaction, (2) combatting sparsity of satisfaction signals, and (3) adapting the training of the recommender agent to maximize satisfaction. For measurement, it has been found that surveys explicitly asking users to rate their experience with consumed items can provide valuable orthogonal information to the engagement/interaction data, acting as a proxy to the underlying user satisfaction. For sparsity, i.e, only being able to observe how satisfied users are with a tiny fraction of user-item interactions, imputation models can be useful in predicting satisfaction level for all items users have consumed. For learning satisfying recommender policies, we postulate that reward shaping in RL recommender agents is powerful for driving satisfying user experiences. Putting everything together, we propose to jointly learn a policy network and a satisfaction imputation network: The role of the imputation network is to learn which actions are satisfying to the user; while the policy network, built on top of REINFORCE, decides which items to recommend, with the reward utilizing the imputed satisfaction. We use both offline analysis and live experiments in an industrial large-scale recommendation platform to demonstrate the promise of our approach for satisfying user experiences.

Amazon isn't done updating its Eero router lineup this year, if not quite in the way you'd expect. To start, the brand's new Echo Dot speakers will now double as Eero WiFi extenders. Plug one in and you'll get as much as 1,000 square feet of additional network coverage. That speaker on your nightstand could improve the internet connection in your office, in other words. The base Echo Dot is available for pre-order today at $50, while the Echo Dot with Clock and colorful Echo Dot Kids will sell for $60. Given that an Eero 6 Extender costs $79, this is an easy choice if you use one of Amazon's routers -- you can pay less to bolster your WiFi network while adding speaker functionality.

BMW began incorporating smart voice features into its infotainment systems using Amazon's Alexa in 2018. In the intervening years, the number of models sporting the digital assistant have only increased. At Amazon's 2022 Devices & Services Event on Wednesday, the two companies announced a deepening of their partnership: BMW's next-generation of infotainment systems will feature an Alexa-based assistant specifically developed with the driver in mind. The as-of-yet unnamed BMW assistant will be constructed from an Alexa Custom Assistant, "a comprehensive solution that makes it easy for BMW and other brands and device makers to create their own custom intelligent assistant tailored to their brand personality and customer needs." Those capabilities might include a proactive notification from the vehicle's assistant alerting the driver that the battery charge is low while automatically reserving a charging slot at the next off-ramp or preemptively scheduling regular service with the local dealership, and "will enable an even more natural dialogue between driver and vehicle," per a Wednesday BMW press release.

Amazon has revealed new Echo speakers, although they don't look much different on the outside. In 2020, it unveiled a completely redesigned Echo with a spherical shape instead of its previous cylindrical construction. Instead, the company says it has improved the audio performance of both the high-end Echo Studio and the compact Echo Dot while keeping the same overall design for both. The retooled Echo Studio comes with new spatial audio processing that improves on Amazon's previous 3D sound technology. The company says we can expect better stereo sound with "greater, width, clarity and presence."