User Modeling plays an essential role in industry. In this field, task-agnostic approaches, which generate general-purpose representation applicable to diverse downstream user cognition tasks, is a promising direction being more valuable and economical than task-specific representation learning. With the rapid development of Internet service platforms, user behaviors have been accumulated continuously. However, existing general-purpose user representation researches have little ability for full-life cycle modeling on extremely long behavior sequences since user registration. In this study, we propose a novel framework called full- Life cycle User Representation Model (LURM) to tackle this challenge. Specifically, LURM consists of two cascaded sub-models: (I) Bag-of-Interests (BoI) encodes user behaviors in any time period into a sparse vector with super-high dimension (e.g., 10^5); (II) Self-supervised Multi-anchor Encoder Network (SMEN) maps sequences of BoI features to multiple low-dimensional user representations. Specially, SMEN achieves almost lossless dimensionality reduction, benefiting from a novel multi-anchor module which can learn different aspects of user interests. Experiments on several benchmark datasets show that our approach outperforms state-of-the-art general-purpose representation methods.

Amazon Prime Day is back again. It wasn't that long since the last one--the retailer held its first-ever fall Prime Day sales event in 2022, and there will be another one this fall as well. But right now, the two-day event runs through July 12. We've spent hours combing through thousands of lists to find the best Prime Day deals 2023 on WIRED-tested gear, from Fire tablets to video games and Apple Watches to standing desks. Many of these deals may require a membership, including Amazon Prime, Target Circle, or Walmart Plus. Amazon Prime has a free 30-day trial if you don't already have it (set a reminder to cancel it before it automatically renews). Target Circle is free to sign up, but you'll need to save the offer to your account from the main offer page or on the actual buy page for a particular item to see the deal price at checkout. Many of Walmart's deals were restricted for a time to subscribers but are available for just about anyone right now. Updated July 11: We added the Vitamix 5200 blender, Fire Max 11 tablet, Govee Lyra smart lamp, Willow Go wearable breast pump, Google Nest Hub (2nd Gen) smart display, Lodge Dutch Oven, All-Clad stainless steel pans, and more. If you buy something using links in our stories, we may earn a commission. This helps support our journalism. We have device deals roundups for Amazon, Google, and Apple products. Many of the best Prime Day deals are on Amazon's own hardware. Our favorite speaker for the bedroom, the Echo Dot makes a great alarm clock. It'll wake you up to your favorite playlists and read you the weather while you relax in bed. The 4th gen Echo is our favorite Alexa speaker. It offers room-filling sound with good detail and a 3.5-mm output for connecting to larger speaker systems. Our only gripe is that, despite listening to you 24/7, Alexa is still not great at picking up commands when loud music is playing. This deal includes a free LED bulb.

SHOPPING – Contains affiliated content. Products featured in this Mail Best article are selected by our shopping writers. If you make a purchase using links on this page, Dailymail.co.uk will earn an affiliate commission. Amazon's popular 2nd Generation Echo Buds, equipped with Alexa integration, are now available at a jaw-dropping 67 per cent discount, selling for just £35.99 in this late-night Lightning Deal. This early Prime Day deal has garnered attention from tech enthusiasts, particularly those comparing the Echo Buds favourably to Apple's renowned AirPods.

Like the oozing black river that gave it its name, Amazon Prime Day flows through another year. It's spreading across the internet delta too--it's now two days long, and last year there were two separate Prime Day events. This year's, ahem, prime Prime Day sales event starts now and runs through the end of July 12. We've spent hours combing through thousands of lists to find the absolute best Prime Day deals on WIRED-tested gear, from Alexa-enabled speakers and robot vacuums to laptops and tablets. Many of these deals may require a membership, including Amazon Prime, Target Circle, or Walmart Plus. Amazon Prime has a free 30-day trial if you don't already have it (set a reminder to cancel it before it automatically renews). Target Circle is free to sign up, but you'll need to save the offer to your account from the main offer page or on the actual buy page for a particular item to see the deal price at checkout. Many of Walmart's deals are in early access for subscribers but will be available for everyone later in the day. Updated July 11: We added the Colgate Hum, Motorola Razr, Niu KQi3 scooter, Victrola Re-spin, Arzopa G12 portable monitor, HyperX gaming headset and Sony WH-1000XM5. If you buy something using links in our stories, we may earn a commission. This helps support our journalism. Our favorite speaker for the bedroom, the Echo Dot makes a great alarm clock. It'll wake you up to your favorite playlists and read you the weather if you like. The 4th gen Echo is our favorite Alexa speaker. It offers room-filling sound with good detail and a 3.5-mm output for connecting to larger speaker systems. Our only gripe is that, despite listening to you 24/7, Alexa is still not great at picking up commands when loud music is playing.

SHOPPING – Contains affiliated content. Products featured in this Mail Best article are selected by our shopping writers. If you make a purchase using links on this page, Dailymail.co.uk will earn an affiliate commission. In the world of smart speakers, the Echo Dot has earned its place as a fan favourite, and now you can get your hands on the latest and greatest model at an incredible 60 per cent off thanks to this unmissable Prime Day deal. The Echo Dot (5th generation, 2022 release) is now available for just £21.99, down from its original price of £54.99 - this Prime Day deal is too good to pass up.

Like the oozing black river that gave it its name, Amazon Prime Day flows through another year. It's spreading across the internet delta too--it's now two days long and last year there were two separate Prime Day events. This year's, ahem, prime Prime Day sales event starts now and runs through the end of July 12. We've spent hours combing through thousands of lists to find the absolute best Prime Day deals on WIRED-tested gear, from Alexa-enabled speakers and robot vacuums to laptops and tablets. Note: Many of these deals may require a membership, including Amazon Prime, Target Circle, or Walmart Plus. Amazon Prime has a free 30-day trial if you don't already have it (set a reminder to cancel it before it automatically renews). Target Circle is free to sign up, but you'll need to save the offer to your account from the main offer page or on the actual buy page for a particular item to see the deal price at checkout. Many of Walmart's deals are in early access for subscribers but will be available for everyone later in the day. If you buy something using links in our stories, we may earn a commission. This helps support our journalism. Our favorite speaker for the bedroom, the Echo Dot makes a great alarm clock. It'll wake you up to your favorite playlists and read you the weather if you like. The 4th gen Echo is our favorite Alexa speaker.

Machine learning is traditionally studied at the model level: researchers measure and improve the accuracy, robustness, bias, efficiency, and other dimensions of specific models. In practice, the societal impact of machine learning is determined by the surrounding context of machine learning deployments. To capture this, we introduce ecosystem-level analysis: rather than analyzing a single model, we consider the collection of models that are deployed in a given context. For example, ecosystem-level analysis in hiring recognizes that a job candidate's outcomes are not only determined by a single hiring algorithm or firm but instead by the collective decisions of all the firms they applied to. Across three modalities (text, images, speech) and 11 datasets, we establish a clear trend: deployed machine learning is prone to systemic failure, meaning some users are exclusively misclassified by all models available. Even when individual models improve at the population level over time, we find these improvements rarely reduce the prevalence of systemic failure. Instead, the benefits of these improvements predominantly accrue to individuals who are already correctly classified by other models. In light of these trends, we consider medical imaging for dermatology where the costs of systemic failure are especially high. While traditional analyses reveal racial performance disparities for both models and humans, ecosystem-level analysis reveals new forms of racial disparity in model predictions that do not present in human predictions. These examples demonstrate ecosystem-level analysis has unique strengths for characterizing the societal impact of machine learning.

We consider the problem of latent bandits with cluster structure where there are multiple users, each with an associated multi-armed bandit problem. These users are grouped into \emph{latent} clusters such that the mean reward vectors of users within the same cluster are identical. At each round, a user, selected uniformly at random, pulls an arm and observes a corresponding noisy reward. The goal of the users is to maximize their cumulative rewards. This problem is central to practical recommendation systems and has received wide attention of late \cite{gentile2014online, maillard2014latent}. Now, if each user acts independently, then they would have to explore each arm independently and a regret of $\Omega(\sqrt{\mathsf{MNT}})$ is unavoidable, where $\mathsf{M}, \mathsf{N}$ are the number of arms and users, respectively. Instead, we propose LATTICE (Latent bAndiTs via maTrIx ComplEtion) which allows exploitation of the latent cluster structure to provide the minimax optimal regret of $\widetilde{O}(\sqrt{(\mathsf{M}+\mathsf{N})\mathsf{T}})$, when the number of clusters is $\widetilde{O}(1)$. This is the first algorithm to guarantee such strong regret bound. LATTICE is based on a careful exploitation of arm information within a cluster while simultaneously clustering users. Furthermore, it is computationally efficient and requires only $O(\log{\mathsf{T}})$ calls to an offline matrix completion oracle across all $\mathsf{T}$ rounds.

Personalized recommendations have a growing importance in direct marketing, which motivates research to enhance customer experiences by knowledge graph (KG) applications. For example, in financial services, companies may benefit from providing relevant financial articles to their customers to cultivate relationships, foster client engagement and promote informed financial decisions. While several approaches center on KG-based recommender systems for improved content, in this study we focus on interpretable KG-based recommender systems for decision making.To this end, we present two knowledge graph-based approaches for personalized article recommendations for a set of customers of a large multinational financial services company. The first approach employs Reinforcement Learning and the second approach uses the XGBoost algorithm for recommending articles to the customers. Both approaches make use of a KG generated from both structured (tabular data) and unstructured data (a large body of text data).Using the Reinforcement Learning-based recommender system we could leverage the graph traversal path leading to the recommendation as a way to generate interpretations (Path Directed Reasoning (PDR)). In the XGBoost-based approach, one can also provide explainable results using post-hoc methods such as SHAP (SHapley Additive exPlanations) and ELI5 (Explain Like I am Five).Importantly, our approach offers explainable results, promoting better decision-making. This study underscores the potential of combining advanced machine learning techniques with KG-driven insights to bolster experience in customer relationship management.

In graph representation learning, it is important that the complex geometric structure of the input graph, e.g. hidden relations among nodes, is well captured in embedding space. However, standard Euclidean embedding spaces have a limited capacity in representing graphs of varying structures. A promising candidate for the faithful embedding of data with varying structure is product manifolds of component spaces of different geometries (spherical, hyperbolic, or euclidean). In this paper, we take a closer look at the structure of product manifold embedding spaces and argue that each component space in a product contributes differently to expressing structures in the input graph, hence should be weighted accordingly. This is different from previous works which consider the roles of different components equally. We then propose WEIGHTED-PM, a data-driven method for learning embedding of heterogeneous graphs in weighted product manifolds. Our method utilizes the topological information of the input graph to automatically determine the weight of each component in product spaces. Extensive experiments on synthetic and real-world graph datasets demonstrate that WEIGHTED-PM is capable of learning better graph representations with lower geometric distortion from input data, and performs better on multiple downstream tasks, such as word similarity learning, top-$k$ recommendation, and knowledge graph embedding.