The saying goes that'mother knows best', and now struggling singletons can enlist their mum's help finding love online. Dating app, Badoo, now lets you add video clips from your family members to your dating profile. The unusual tool is part of Badoo's new'Family-Approved' feature, which allows users to show that they've called on their family to help them land a date. Remy Le Fèvre, Global Head of Brand Engagement and Influence at Badoo, said: 'Here at Badoo, we're all about making sure singles feel confident putting their best foot forward when dating, and for many of us, that means getting a little help from the people that love them most. 'Family-Approved is here to help singles feel good right from the start of their dating journey, whilst also showing potential matches when profiles have had the trusted green light from their loved ones - which could be a fun icebreaker when starting a conversation!'

There are so many good smart displays out there that it can be hard to choose which one to buy. Right now, one of our favorites, Amazon's second-generation Echo Show 8, is running down to $75 from $130 -- a 42 percent discount and just $5 off its lowest price. There are other available options on sale, like an adjustable stand or Blink Mini, but expect to pay a little extra for those. The Echo Show 8 is part speaker, part tablet, with TV shows and movies available from streamers like Netflix, Hulu and, of course, Prime Video. These come alongside music from Spotify, Apple Music and Amazon Music.

Advances in voice technology and voice user interfaces (VUIs) -- such as Alexa, Siri, and Google Home -- have opened up the potential for many new types of interaction. However, despite the potential of these devices reflected by the growing market and body of VUI research, there is a lingering sense that the technology is still underused. In this paper, we conducted a systematic literature review of 35 papers to identify and synthesize 127 VUI design guidelines into five themes. Additionally, we conducted semi-structured interviews with 15 smart speaker users to understand their use and non-use of the technology. From the interviews, we distill four design challenges that contribute the most to non-use. Based on their (non-)use, we identify four opportunity spaces for designers to explore such as focusing on information support while multitasking (cooking, driving, childcare, etc), incorporating users' mental models for smart speakers, and integrating calm design principles.

Recommender systems have found significant commercial success but still struggle with integrating new users. Since users often interact with content in different domains, it is possible to leverage a user's interactions in previous domains to improve that user's recommendations in a new one (multi-domain recommendation). A separate research thread on knowledge graph enhancement uses external knowledge graphs to improve single domain recommendations (knowledge graph enhancement). Both research threads incorporate related information to improve predictions in a new domain. We propose in this work to unify these approaches: Using information from interactions in other domains as well as external knowledge graphs to make predictions in a new domain that would be impossible with either information source alone. We apply these ideas to a dataset derived from millions of users' requests for content across three domains (videos, music, and books) in a live virtual assistant application. We demonstrate the advantage of combining knowledge graph enhancement with previous multi-domain recommendation techniques to provide better overall recommendations as well as for better recommendations on new users of a domain.

Code-recommendation systems, such as Copilot and CodeWhisperer, have the potential to improve programmer productivity by suggesting and auto-completing code. However, to fully realize their potential, we must understand how programmers interact with these systems and identify ways to improve that interaction. To make progress, we studied GitHub Copilot, a code-recommendation system used by millions of programmers daily. We developed CUPS, a taxonomy of common programmer activities when interacting with Copilot. Our study of 21 programmers, who completed coding tasks and retrospectively labeled their sessions with CUPS, showed that CUPS can help us understand how programmers interact with code-recommendation systems, revealing inefficiencies and time costs. Our insights reveal how programmers interact with Copilot and motivate new interface designs and metrics.

Purchases you make through the links below may earn us and our publishing partners a commission. Air quality is a major concern throughout the country with the Canadian wildfires still blazing. With all the reports of smoke choking the fresh air in the Northeast, it's understandable to be concerned about how fresh the airflow in your home is. Fortunately, we found an Amazon deal on a Reviewed-approved air quality monitor to help you know exactly how safe it is to take deep breaths in your own four walls. Right now, the online shopping giant is offering its own smart air quality monitor for $54.99.

Excellent tail performance is crucial for modern machine learning tasks, such as algorithmic fairness, class imbalance, and risk-sensitive decision making, as it ensures the effective handling of challenging samples within a dataset. Tail performance is also a vital determinant of success for personalised recommender systems to reduce the risk of losing users with low satisfaction. This study introduces a "safe" collaborative filtering method that prioritises recommendation quality for less-satisfied users rather than focusing on the average performance. Our approach minimises the conditional value at risk (CVaR), which represents the average risk over the tails of users' loss. To overcome computational challenges for web-scale recommender systems, we develop a robust yet practical algorithm that extends the most scalable method, implicit alternating least squares (iALS). Empirical evaluation on real-world datasets demonstrates the excellent tail performance of our approach while maintaining competitive computational efficiency.

Ranking model plays an essential role in e-commerce search and recommendation. An effective ranking model should give a personalized ranking list for each user according to the user preference. Existing algorithms usually extract a user representation vector from the user behavior sequence, then feed the vector into a feed-forward network (FFN) together with other features for feature interactions, and finally produce a personalized ranking score. Despite tremendous progress in the past, there is still room for improvement. Firstly, the personalized patterns of feature interactions for different users are not explicitly modeled. Secondly, most of existing algorithms have poor personalized ranking results for long-tail users with few historical behaviors due to the data sparsity. To overcome the two challenges, we propose Attention Weighted Mixture of Experts (AW-MoE) with contrastive learning for personalized ranking. Firstly, AW-MoE leverages the MoE framework to capture personalized feature interactions for different users. To model the user preference, the user behavior sequence is simultaneously fed into expert networks and the gate network. Within the gate network, one gate unit and one activation unit are designed to adaptively learn the fine-grained activation vector for experts using an attention mechanism. Secondly, a random masking strategy is applied to the user behavior sequence to simulate long-tail users, and an auxiliary contrastive loss is imposed to the output of the gate network to improve the model generalization for these users. This is validated by a higher performance gain on the long-tail user test set. Experiment results on a JD real production dataset and a public dataset demonstrate the effectiveness of AW-MoE, which significantly outperforms state-of-art methods. Notably, AW-MoE has been successfully deployed in the JD e-commerce search engine, ...

With the development of the multi-media internet, visual characteristics have become an important factor affecting user interests. Thus, incorporating visual features is a promising direction for further performance improvements in click-through rate (CTR) prediction. However, we found that simply injecting the image embeddings trained with established pre-training methods only has marginal improvements. We attribute the failure to two reasons: First, The pre-training methods are designed for well-defined computer vision tasks concentrating on semantic features, and they cannot learn personalized interest in recommendations. Secondly, pre-trained image embeddings only containing semantic information have little information gain, considering we already have semantic features such as categories and item titles as inputs in the CTR prediction task. We argue that a pre-training method tailored for recommendation is necessary for further improvements. To this end, we propose a recommendation-aware image pre-training method that can learn visual features from user click histories. Specifically, we propose a user interest reconstruction module to mine visual features related to user interests from behavior histories. We further propose a contrastive training method to avoid collapsing of embedding vectors. We conduct extensive experiments to verify that our method can learn users' visual interests, and our method achieves $0.46\%$ improvement in offline AUC and $0.88\%$ improvement in Taobao online GMV with p-value$<0.01$.

Finding a life partner can be very difficult if dating is not an option. Many young Muslims want to avoid arranged marriages but cannot go on dates because they believe it is forbidden. Now, in some of the most conservative Muslim communities in Britain and the Middle East, traditional matchmaking has entered the digital age. In this film, we meet Muslims using "halal dating apps", as well as app and platform developers. In these spaces, clients select their potential partners before they meet them, without crossing religious red lines.