There is a knowledge gap regarding which types of failures robots undergo in domestic settings and how these failures influence customer experience. We classified 10,072 customer reviews of small utilitarian domestic robots on Amazon by the robotic failures described in them, grouping failures into twelve types and three categories (Technical, Interaction, and Service). We identified sources and types of failures previously overlooked in the literature, combining them into an updated failure taxonomy. We analyzed their frequencies and relations to customer star ratings. Results indicate that for utilitarian domestic robots, Technical failures were more detrimental to customer experience than Interaction or Service failures. Issues with Task Completion and Robustness & Resilience were commonly reported and had the most significant negative impact. Future failure-prevention and response strategies should address the technical ability of the robot to meet functional goals, operate and maintain structural integrity over time. Usability and interaction design were less detrimental to customer experience, indicating that customers may be more forgiving of failures that impact these aspects for the robots and practical uses examined. Further, we developed a Natural Language Processing model capable of predicting whether a customer review contains content that describes a failure and the type of failure it describes. With this knowledge, designers and researchers of robotic systems can prioritize design and development efforts towards essential issues.

Many researchers have used tag information to improve the performance of recommendation techniques in recommender systems. Examining the tags of users will help to get their interests and leads to more accuracy in the recommendations. Since user-defined tags are chosen freely and without any restrictions, problems arise in determining their exact meaning and the similarity of tags. On the other hand, using thesauruses and ontologies to find the meaning of tags is not very efficient due to their free definition by users and the use of different languages in many data sets. Therefore, this article uses the mathematical and statistical methods to determine lexical similarity and co-occurrence tags solution to assign semantic similarity. On the other hand, due to the change of users' interests over time this article have considered the time of tag assignments in co-occurrence tags for determined similarity of tags. Then the graph is created based on these similarities. For modeling the interests of the users, the communities of tags are determined by using community detection methods. So recommendations based on the communities of tags and similarity between resources are done. The performance of the proposed method has been done using two criteria of precision and recall based on evaluations with "Delicious" dataset. The evaluation results show that, the precision and recall of the proposed method have significantly improved, compared to the other methods.

Recommender Systems (RSs) aim to model and predict the user preference while interacting with items, such as Points of Interest (POIs). These systems face several challenges, such as data sparsity, limiting their effectiveness. In this paper, we address this problem by incorporating social, geographical, and temporal information into the Matrix Factorization (MF) technique. To this end, we model social influence based on two factors: similarities between users in terms of common check-ins and the friendships between them. We introduce two levels of friendship based on explicit friendship networks and high check-in overlap between users. We base our friendship algorithm on users' geographical activity centers. The results show that our proposed model outperforms the state-of-the-art on two real-world datasets. More specifically, our ablation study shows that the social model improves the performance of our proposed POI recommendation system by 31% and 14% on the Gowalla and Yelp datasets in terms of Precision@10, respectively.

Crime Stoppers of Houston Andy Kahan and FOP national vice president Joe Gamaldi react to the nation's growing crime crisis on'Justice w/ Judge Jeanine.' A potential fifth victim has been identified in the "shopping cart killer" case, involving an alleged serial killer in Northern Virginia, that has crime experts warning of the dangers of online dating. Officers believe suspect Anthony Robinson made contact with the victims via dating websites which Crime Stoppers of Houston's Andy Kahan described on "Justice w/ Judge Jeanine" as "toy stores" for murderers. "The dark side of online dating apps are luring in millions of women to, perhaps… mortal danger," he said. "There are no background checks; we all know sex offenders troll it. You're essentially playing Russian roulette with your life when you divulge personal information and continue to go out and meet people that you do not know."

As a marketer or a business owner in today's digital world, nothing is more important than forecasting. Knowing the marketing industry, the trends, and the issues that you will face gives you a great competitive advantage. Marketers should know how to stay up to date on new trends and the constantly changing marketing landscape. Forecasting the future of marketing and being the first to give the people what they really want will take you nowhere but the top -- Exactly where you belong. Is there anything better than that?

As the deep learning techniques have expanded to real-world recommendation tasks, many deep neural network based Collaborative Filtering (CF) models have been developed to project user-item interactions into latent feature space, based on various neural architectures, such as multi-layer perceptron, auto-encoder and graph neural networks. However, the majority of existing collaborative filtering systems are not well designed to handle missing data. Particularly, in order to inject the negative signals in the training phase, these solutions largely rely on negative sampling from unobserved user-item interactions and simply treating them as negative instances, which brings the recommendation performance degradation. To address the issues, we develop a Collaborative Reflection-Augmented Autoencoder Network (CRANet), that is capable of exploring transferable knowledge from observed and unobserved user-item interactions. The network architecture of CRANet is formed of an integrative structure with a reflective receptor network and an information fusion autoencoder module, which endows our recommendation framework with the ability of encoding implicit user's pairwise preference on both interacted and non-interacted items. Additionally, a parametric regularization-based tied-weight scheme is designed to perform robust joint training of the two-stage CRANet model. We finally experimentally validate CRANet on four diverse benchmark datasets corresponding to two recommendation tasks, to show that debiasing the negative signals of user-item interactions improves the performance as compared to various state-of-the-art recommendation techniques. Our source code is available at https://github.com/akaxlh/CRANet.

Compared with the traditional collaborative filtering methods, the graph convolution network can explicitly model the interaction between the nodes of the user-item bipartite graph and effectively use higher-order neighbors, which enables the graph neural network to obtain more effective embeddings for recommendation, such as NGCF And LightGCN. However, its representations is very susceptible to the noise of interaction. In response to this problem, SGL explored the self-supervised learning on the user-item graph to improve the robustness of GCN. Although effective, we found that SGL directly applies SimCLR's comparative learning framework. This framework may not be directly applicable to the scenario of the recommendation system, and does not fully consider the uncertainty of user-item interaction.In this work, we aim to consider the application of contrastive learning in the scenario of the recommendation system adequately, making it more suitable for recommendation task. We propose a supervised contrastive learning framework to pre-train the user-item bipartite graph, and then fine-tune the graph convolutional neural network. Specifically, we will compare the similarity between users and items during data preprocessing, and then when applying contrastive learning, not only will the augmented views be regarded as the positive samples, but also a certain number of similar samples will be regarded as the positive samples, which is different from SimCLR who treats other samples in a batch as negative samples. We term this learning method as Supervised Contrastive Learning(SCL) and apply it on the most advanced LightGCN. In addition, in order to consider the uncertainty of node interaction, we also propose a new data augment method called node replication.

This paper explores the environmental impact of the super-linear growth trends for AI from a holistic perspective, spanning Data, Algorithms, and System Hardware. We characterize the carbon footprint of AI computing by examining the model development cycle across industry-scale machine learning use cases and, at the same time, considering the life cycle of system hardware. Taking a step further, we capture the operational and manufacturing carbon footprint of AI computing and present an end-to-end analysis for what and how hardware-software design and at-scale optimization can help reduce the overall carbon footprint of AI. Based on the industry experience and lessons learned, we share the key challenges and chart out important development directions across the many dimensions of AI. We hope the key messages and insights presented in this paper can inspire the community to advance the field of AI in an environmentally-responsible manner.

Early in my career, working as a data analyst, I, like many people, dreamed of doing something significant and valuable for people. I wanted to be creative. I wanted to feel the results of my work, not just study the data. I worked at several startup companies for ten years before co-founding an e-commerce recommendation services company in 2012. In 2020, during the COVID pandemic, I went on sabbatical for a year -- wrote a book, released it on Amazon, and left the successful company (positive cash flow, 150 employees in Russia, Europe, and South America) entirely in the summer of 2021.

Google unveiled Wednesday at CES 2022 a range of new ways to keep its Android devices connected -- and that includes cars. As more vehicles go electric and automakers evolve into software developers, expect to see more plays directed at turning cars into connected devices. Take Volvo Cars, for instance. The automaker and Google announced at CES 2022 new content and services that will be coming to future Volvo vehicles, including the ability to download and use the YouTube app via Google Play Store and the ability to communicate with the Google Home ecosystem. New Volvo car models are equipped with an Android Automotive operating system and have embedded voice-controlled Google Assistant, Google Play Store, Google Maps and other Google services into its infotainment system.