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."

Boostr, the only pipeline-to-profits advertising management platform for the media industry announced the launch of Proposal-IQ, a groundbreaking new product designed to help media sales organizations improve the quality of RFP responses while simultaneously building proposals faster. By suggesting the optimal media mix and media plan for each client's objectives and tying into the media seller's real-time inventory, Proposal-IQ drives larger deal sizes, increases in average products sold per order, and higher sell-through--helping media sales organizations grow their revenue. Media companies typically send out dozens of proposals each week and have less than 48 hours to respond to RFPs, creating an enormous burden on media sales teams who need to obtain pricing approvals, conduct inventory checks and complete multiple reviews as part of a highly manual process. Time constraints and repetitiveness often lead salespeople and planners to pitch the same products repeatedly, and their choices are often based more on familiarity than on performance data. Unfortunately, only 37% of proposals contain multiple products yet the top quartile of highest growing publishers are selling multiple products on 48% of proposals.

From reminding potty-training toddlers to go to the loo to telling bedtime stories and being used as a "conversation partner", voice-activated smart devices are being used to help rear children almost from the day they are born. But the rapid rise in voice assistants, including Google Home, Amazon Alexa and Apple's Siri could, researchers suggest, have a long-term impact on children's social and cognitive development, specifically their empathy, compassion and critical thinking skills. "The multiple impacts on children include inappropriate responses, impeding social development and hindering learning opportunities," said Anmol Arora, co-author of an article published in the journal Archives of Disease in Childhood. A key concern is that children attribute human characteristics and behaviour to devices that are, said Arora, "essentially a list of trained words and sounds mashed together to make a sentence." The children anthropomorphise and then emulate the devices, copying their failure to alter their tone, volume, emphasis or intonation.

From reminding potty-training toddlers to go to the loo to telling bedtime stories and being used as a "conversation partner", voice-activated smart devices are being used to help rear children almost from the day they are born. But the rapid rise in voice assistants, including Google Home, Amazon Alexa and Apple's Siri could, new research suggests, have a long-term impact on children's social and cognitive development, specifically their empathy, compassion and critical thinking skills. "The multiple impacts on children include inappropriate responses, impeding social development and hindering learning opportunities," said Anmol Arora, co-author of research published in the journal Archives of Disease in Childhood. A key concern is that children attribute human characteristics and behaviour to devices that are, said Arora, "essentially a list of trained words and sounds mashed together to make a sentence." The children anthropomorphise and then emulate the devices, copying their failure to alter their tone, volume, emphasis or intonation.

We present Sparrow, an information-seeking dialogue agent trained to be more helpful, correct, and harmless compared to prompted language model baselines. We use reinforcement learning from human feedback to train our models with two new additions to help human raters judge agent behaviour. First, to make our agent more helpful and harmless, we break down the requirements for good dialogue into natural language rules the agent should follow, and ask raters about each rule separately. We demonstrate that this breakdown enables us to collect more targeted human judgements of agent behaviour and allows for more efficient rule-conditional reward models. Second, our agent provides evidence from sources supporting factual claims when collecting preference judgements over model statements. For factual questions, evidence provided by Sparrow supports the sampled response 78% of the time. Sparrow is preferred more often than baselines while being more resilient to adversarial probing by humans, violating our rules only 8% of the time when probed. Finally, we conduct extensive analyses showing that though our model learns to follow our rules it can exhibit distributional biases.