Modern recommender systems lie at the heart of complex ecosystems that couple the behavior of users, content providers, advertisers, and other actors. Despite this, the focus of the majority of recommender research -- and most practical recommenders of any import -- is on the local, myopic optimization of the recommendations made to individual users. This comes at a significant cost to the long-term utility that recommenders could generate for its users. We argue that explicitly modeling the incentives and behaviors of all actors in the system -- and the interactions among them induced by the recommender's policy -- is strictly necessary if one is to maximize the value the system brings to these actors and improve overall ecosystem "health". Doing so requires: optimization over long horizons using techniques such as reinforcement learning; making inevitable tradeoffs in the utility that can be generated for different actors using the methods of social choice; reducing information asymmetry, while accounting for incentives and strategic behavior, using the tools of mechanism design; better modeling of both user and item-provider behaviors by incorporating notions from behavioral economics and psychology; and exploiting recent advances in generative and foundation models to make these mechanisms interpretable and actionable. We propose a conceptual framework that encompasses these elements, and articulate a number of research challenges that emerge at the intersection of these different disciplines.

Amazon's fall hardware event was chock full of updates. Perhaps unsurprisingly, given the generative AI boom from the last year, the company began transforming Alexa into a much more versatile and conversational personal chatbot. But it also had plenty of new hardware to introduce, with new models of the Echo Show, security cameras, Echo Frames, a 10-gigabit router and more. Here's everything Amazon unveiled on Wednesday. As generative AI has exploded in popularity during the last year, task-focused personal assistants like Siri, Google Assistant and Alexa now seem even more dated than they did before.

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Amazon's smart glasses have yet to impress us, but the company made big changes for its third-gen Echo Frames that could go along way in changing our minds. First, the company has upgraded the design, slimming down the area around your temples that houses all of the components. Amazon has also changed the look, continuing to make the glasses and sunglasses options look more like something you'd actually want to wear. What's more, it's working with the more fashion-minded Carrera Eyewear on smart glasses with a refined touch -- in addition to its own versions. First, there's the improved sound quality.

Every fall, Amazon holds a "Devices and Services" media event where it unleashes a flood of new gadgets and software into the world. At the 2022 edition, Amazon announced a Kindle with a stylus, a robot dog, and a refreshed line of Echoes and Eeros, among other smart home gadgets. This year, the company was eager to prove that it hasn't been left behind by its rivals' recent advances in artificial intelligence and conversational interfaces. Executives showed off a smarter version of Alexa that's been given an AI boost, as well as new smart home products that harness Amazon's computer vision, machine intelligence, and face recognition technologies. There were some stumbling blocks during the presentation, but here are the highlights of what Amazon announced today.

Amazon debuted an updated Echo Show 8 during its live event today, highlighting the device's new display, camera, microphones and spatial-audio capabilities. Generative AI helps the Echo Show 8 respond dynamically to the user's position in the physical world, offering different displays depending on how far away someone is from the screen. A new language model increases the device's on-board Alexa response time by 40 percent over the previous edition. The Echo Show 8 costs $149.99 and is available for pre-order today. The device will hit the market and start shipping in October.

Have you seen the meme about people who dangle too many things on their fingers for no reason whatsoever? I'm not proud to admit it, but I'm one of those. No matter how big of a bag I'm carrying, I always find my hands full, making it difficult to interact with my phone or smartwatch on the go. Which is why voice controlled assistants and hands-free gestures are so appealing. With the Apple Watch Series 9, the company is introducing two new methods of interaction: Double Tap and Raise to Speak (to Siri).

In this paper, we study the effect of popularity degradation bias in the context of local music recommendations. Specifically, we examine how accurate two top-performing recommendation algorithms, Weight Relevance Matrix Factorization (WRMF) and Multinomial Variational Autoencoder (Mult-VAE), are at recommending artists as a function of artist popularity. We find that both algorithms improve recommendation performance for more popular artists and, as such, exhibit popularity degradation bias. While both algorithms produce a similar level of performance for more popular artists, Mult-VAE shows better relative performance for less popular artists. This suggests that this algorithm should be preferred for local (long-tail) music artist recommendation.

The recommendation of appropriate development pathways, also known as ecological civilization patterns for achieving Sustainable Development Goals (namely, sustainable development patterns), are of utmost importance for promoting ecological, economic, social, and resource sustainability in a specific region. To achieve this, the recommendation process must carefully consider the region's natural, environmental, resource, and economic characteristics. However, current recommendation algorithms in the field of computer science fall short in adequately addressing the spatial heterogeneity related to environment and sparsity of regional historical interaction data, which limits their effectiveness in recommending sustainable development patterns. To overcome these challenges, this paper proposes a method called User Graph after Pruning and Intent Graph (UGPIG). Firstly, we utilize the high-density linking capability of the pruned User Graph to address the issue of spatial heterogeneity neglect in recommendation algorithms. Secondly, we construct an Intent Graph by incorporating the intent network, which captures the preferences for attributes including environmental elements of target regions. This approach effectively alleviates the problem of sparse historical interaction data in the region. Through extensive experiments, we demonstrate that UGPIG outperforms state-of-the-art recommendation algorithms like KGCN, KGAT, and KGIN in sustainable development pattern recommendations, with a maximum improvement of 9.61% in Top-3 recommendation performance.

Following complex instructions in conversational assistants can be quite daunting due to the shorter attention and memory spans when compared to reading the same instructions. Hence, when conversational assistants walk users through the steps of complex tasks, there is a need to structure the task into manageable pieces of information of the right length and complexity. In this paper, we tackle the recipes domain and convert reading structured instructions into conversational structured ones. We annotated the structure of instructions according to a conversational scenario, which provided insights into what is expected in this setting. To computationally model the conversational step's characteristics, we tested various Transformer-based architectures, showing that a token-based approach delivers the best results. A further user study showed that users tend to favor steps of manageable complexity and length, and that the proposed methodology can improve the original web-based instructional text. Specifically, 86% of the evaluated tasks were improved from a conversational suitability point of view.