In conversational recommender systems (CRSs), conversations usually involve a set of items and item-related entities or attributes, e.g., director is a related entity of a movie. These items and item-related entities are often mentioned along the development of a dialog, leading to potential sequential dependencies among them. However, most of existing CRSs neglect these potential sequential dependencies. In this article, we first propose a Transformer-based sequential conversational recommendation method, named TSCR, to model the sequential dependencies in the conversations to improve CRS. In TSCR, we represent conversations by items and the item-related entities, and construct user sequences to discover user preferences by considering both the mentioned items and item-related entities. Based on the constructed sequences, we deploy a Cloze task to predict the recommended items along a sequence. Meanwhile, in certain domains, knowledge graphs formed by the items and their related entities are readily available, which provide various different kinds of associations among them. Given that TSCR does not benefit from such knowledge graphs, we then propose a knowledge graph enhanced version of TSCR, called TSCRKG. In specific, we leverage the knowledge graph to offline initialize our model TSCRKG, and augment the user sequence of conversations (i.e., sequence of the mentioned items and item-related entities in the conversation) with multi-hop paths in the knowledge graph. Experimental results demonstrate that our TSCR model significantly outperforms state-of-the-art baselines, and the enhanced version TSCRKG further improves recommendation performance on top of TSCR.

To mitigate the rising concern about privacy leakage, the federated recommender (FR) paradigm emerges, in which decentralized clients co-train the recommendation model without exposing their raw user-item rating data. The differentially private federated recommender (DPFR) further enhances FR by injecting differentially private (DP) noises into clients. Yet, current DPFRs, suffering from noise distortion, cannot achieve satisfactory accuracy. Various efforts have been dedicated to improving DPFRs by adaptively allocating the privacy budget over the learning process. However, due to the intricate relation between privacy budget allocation and model accuracy, existing works are still far from maximizing DPFR accuracy. To address this challenge, we develop BGTplanner (Budget Planner) to strategically allocate the privacy budget for each round of DPFR training, improving overall training performance. Specifically, we leverage the Gaussian process regression and historical information to predict the change in recommendation accuracy with a certain allocated privacy budget. Additionally, Contextual Multi-Armed Bandit (CMAB) is harnessed to make privacy budget allocation decisions by reconciling the current improvement and long-term privacy constraints. Our extensive experimental results on real datasets demonstrate that \emph{BGTplanner} achieves an average improvement of 6.76\% in training performance compared to state-of-the-art baselines.

As the sun sets on the Black Friday weekend there are still bargains to be found. Whether you are gift shopping for the holidays or treating yourself, we have all the best Cyber Monday deals for you. We worked tirelessly to filter the noise and tune into the sales worth your attention. So kick back and get ready to bag a bargain. Bringing decades of product testing experience, tempered by price-tracking tools, the WIRED team has cross-referenced our buying guide recommendations with the latest discounts to find only the very best Cyber Monday deals. Someone from the WIRED Reviews team has tested every product we list in our deals coverage, and we don't recommend anything we don't like. We always strive to find deals at their best price ever, or very close to it (some match previous discounts, but we have never seen them lower unless stated). We test products year-round and handpicked these Cyber Monday deals. To find you the best deals, we use a proprietary tool that scans prices on ...

We propose to integrate long-distance LongRange (LoRa) communication solution for sending the data from IoT to the edge computing system, by taking advantage of its unlicensed nature and the potential for open source implementations that are common in edge computing. We propose a channel hoping optimization model and apply TinyML-based channel hoping model based for LoRa transmissions, as well as experimentally study a fast predictive algorithm to find free channels between edge and IoT devices. In the open source experimental setup that includes LoRa, TinyML and IoT-edge-cloud continuum, we integrate a novel application workflow and cloud-friendly protocol solutions in a case study of plant recommender application that combines concepts of microfarming and urban computing. In a LoRa-optimized edge computing setup, we engineer the application workflow, and apply collaborative filtering and various machine learning algorithms on application data collected to identify and recommend the planting schedule for a specific microfarm in an urban area. In the LoRa experiments, we measure the occurrence of packet loss, RSSI, and SNR, using a random channel hoping scheme to compare with our proposed TinyML method. The results show that it is feasible to use TinyML in microcontrollers for channel hopping, while proving the effectiveness of TinyML in learning to predict the best channel to select for LoRa transmission, and by improving the RSSI by up to 63 %, SNR by up to 44 % in comparison with a random hopping mechanism.

In this study, we introduce Convolutional Transformer Neural Collaborative Filtering (CTNCF), a novel approach aimed at enhancing recommendation systems by effectively capturing high-order structural information in user-item interactions. CTNCF represents a significant advancement over the traditional Neural Collaborative Filtering (NCF) model by seamlessly integrating Convolutional Neural Networks (CNNs) and Transformer layers. This sophisticated integration enables the model to adeptly capture and understand complex interaction patterns inherent in recommendation systems. Specifically, CNNs are employed to extract local features from user and item embeddings, allowing the model to capture intricate spatial dependencies within the data. Furthermore, the utilization of Transformer layers enables the model to capture long-range dependencies and interactions among user and item features, thereby enhancing its ability to understand the underlying relationships in the data. To validate the effectiveness of our proposed CTNCF framework, we conduct extensive experiments on two real-world datasets. The results demonstrate that CTNCF significantly outperforms state-of-the-art approaches, highlighting its efficacy in improving recommendation system performance.

Multimodal Large Language Models (MLLMs) have become increasingly important due to their state-of-the-art performance and ability to integrate multiple data modalities, such as text, images, and audio, to perform complex tasks with high accuracy. This paper presents a comprehensive survey on personalized multimodal large language models, focusing on their architecture, training methods, and applications. We propose an intuitive taxonomy for categorizing the techniques used to personalize MLLMs to individual users, and discuss the techniques accordingly. Furthermore, we discuss how such techniques can be combined or adapted when appropriate, highlighting their advantages and underlying rationale. We also provide a succinct summary of personalization tasks investigated in existing research, along with the evaluation metrics commonly used. Additionally, we summarize the datasets that are useful for benchmarking personalized MLLMs. Finally, we outline critical open challenges. This survey aims to serve as a valuable resource for researchers and practitioners seeking to understand and advance the development of personalized multimodal large language models.

We've tested hundreds of speakers over the years, and we put the best ones into our buying guides -- namely the ones for soundbars, portable Bluetooth speakers and smart speakers. Now that Cyber Monday has arrived, we're seeing notable discounts on many of our top picks. So if you need a soundbar to make the dialogue on your TV clearer or want to take your music out on the porch once the weather warms back up, this is a good time to grab a new music maker. And if you need a speaker to do your bidding (like turning on your smart lights or reminding you to take out the trash) you can save on a smart speaker, too. Here are the best Cyber Monday speaker deals we could find. Portable Bluetooth speakers make it easy for you to bring the music where plugs don't reach -- a picnic, the front stoop, an aimless wander along the Pacific Crest Trail.

Most tech costs a fair chunk of change (the PS5 Pro and iPad Pro jump to mind), but some smaller electronics are (almost) as exciting. Now that Cyber Monday is here, we've found dozens of deals on inexpensive electronics and accessories that cost less than 50. Unlike other lists you may find today, this one is made up of items we've tested, reviewed or otherwise recommend in our buying and gift guides -- so we think they're actually worth your money. Amazon Echo Pop (2023) for 18 ( 22 off): Amazon's smallest Echo will fit in any room in your home, so Alexa can add things to your shopping list, set a timer, or answer questions (like "What's a bomb cyclone?" or "Who is Penelope Cruz married to?") from anywhere. Anker Nano Charger 30W USB-C for 13 ( 7 off): This compact 30-watt wall charger is smaller than others of its wattage and can speedily juice up an iPhone or Android handset. Anker is one of Engadget's most recommended accessory brands and this is the model we picked for our fast charger guide. Get the same deal at Anker with an auto-applied code. Anker Nano power bank with built-in USB-C connector for 16 ( 4 off): It's the size of an old-timey lipstick case but packs enough juice (and its own USB-C plug) to get a dying smartphone back in service with at least a half charge.

The expensive tech gets all the attention -- thousand-dollar phones and 500 tablets. But the supporting players, the cables and batteries and chargers that make those devices work properly, are just as important. Right now for Black Friday, many of those smaller gadgets are on sale for less than 50 even after the day has passed. And there are even some standalone devices like earbuds and smart speakers that fall well below the threshold. We've tested scads of these smaller, less expensive tech for Engadget buying guides, including the best power banks, iPad accessories, smart plugs and microSD cards. Here, we've gathered up all the Black Friday tech deals under 50 that you can still get on gadgets we recommend. Amazon Echo Pop (2023) for 18 ( 22 off): Amazon's smallest Echo will fit in any room in your home, so Alexa can add things to your shopping list, set a timer, or answer questions (like "What's a bomb cyclone?" or "Who is Penelope Cruz married to?") from anywhere.

To combat the rising energy consumption of recommender systems we implement a novel alternative for k-fold cross validation. This alternative, named e-fold cross validation, aims to minimize the number of folds to achieve a reduction in power usage while keeping the reliability and robustness of the test results high. We tested our method on 5 recommender system algorithms across 6 datasets and compared it with 10-fold cross validation. On average e-fold cross validation only needed 41.5% of the energy that 10-fold cross validation would need, while it's results only differed by 1.81%. We conclude that e-fold cross validation is a promising approach that has the potential to be an energy efficient but still reliable alternative to k-fold cross validation.