This research article introduces AIOptimizer, a prototype for a software performance optimisation tool based on cost reduction. AIOptimizer uses a recommendation system driven by reinforcement learning to improve software system efficiency and affordability. The paper highlights AIOptimizer's design factors, such as accuracy, adaptability, scalability, and user-friendliness. To provide effective and user-centric performance optimisation solutions, it emphasises the use of a modular design, data gathering techniques, continuous learning, and resilient integration. The article also investigates AIOptimizer features such as fault identification, cost optimisation recommendations, efficiency prediction, and cooperation. Furthermore, it explores several software development life cycle models and introduces AIOptimizer uses a reinforcement learning-based recommendation engine for cost optimisation. The purpose of this research study is to highlight AIOptimizer as a prototype that uses advanced optimisation techniques and smart recommendation systems to continually enhance software performance and save expenses. The research focuses on various software development life cycle models, such as the Waterfall model, Iterative model, Spiral model, V-Model, Big Bang model and Agile Model. Each model has advantages and disadvantages, and their usefulness is determined by the project's specifications and characteristics. The AIOptimizer tool is a theoretical prototype for such software performance optimizers.

Much unstructured data has been produced with the growth of the Internet and social media. A significant volume of textual data includes users' opinions about products in online stores and social media. By exploring and categorizing them, helpful information can be acquired, including customer satisfaction, user feedback about a particular event, predicting the sale of a specific product, and other similar cases. In this paper, we present an approach for sentiment analysis with a deep learning model and use it to recommend products. A two-channel convolutional neural network model has been used for opinion mining, which has five layers and extracts essential features from the data. We increased the number of comments by applying the SMOTE algorithm to the initial dataset and balanced the data. Then we proceed to cluster the aspects. We also assign a weight to each cluster using tensor decomposition algorithms that improve the recommender system's performance. Our proposed method has reached 91.6% accuracy, significantly improved compared to previous aspect-based approaches.

If you've had your eye on the new Echo Show 5 but weren't able to grab one on Prime Day, the diminutive smart display is back on sale for $50. While we saw the device go for $5 less during Amazon's sales event, that deal was exclusive to Prime members. This $40 discount is open to everyone and marks the second-lowest price we've seen since the display arrived in May. The Kids version of the display is also on sale for $60, though that's $10 more it was on Prime Day. This deal is $5 more than the all-time low we saw during Prime Day, but it's the best price we've seen outside of that, and it isn't exclusive to Amazon Prime subscribers.

Online news outlets are grappling with the moderation of user-generated content within their comment section. We present a recommender system based on ranking class probabilities to support and empower the moderator in choosing featured posts, a time-consuming task. By combining user and textual content features we obtain an optimal classification F1-score of 0.44 on the test set. Furthermore, we observe an optimum mean NDCG@5 of 0.87 on a large set of validation articles. As an expert evaluation, content moderators assessed the output of a random selection of articles by choosing comments to feature based on the recommendations, which resulted in a NDCG score of 0.83. We conclude that first, adding text features yields the best score and second, while choosing featured content remains somewhat subjective, content moderators found suitable comments in all but one evaluated recommendations. We end the paper by analyzing our best-performing model, a step towards transparency and explainability in hybrid content moderation.

In modern recommendation systems, unbiased learning-to-rank (LTR) is crucial for prioritizing items from biased implicit user feedback, such as click data. Several techniques, such as Inverse Propensity Weighting (IPW), have been proposed for single-sided markets. However, less attention has been paid to two-sided markets, such as job platforms or dating services, where successful conversions require matching preferences from both users. This paper addresses the complex interaction of biases between users in two-sided markets and proposes a tailored LTR approach. We first present a formulation of feedback mechanisms in two-sided matching platforms and point out that their implicit feedback may include position bias from both user groups. On the basis of this observation, we extend the IPW estimator and propose a new estimator, named two-sided IPW, to address the position bases in two-sided markets. We prove that the proposed estimator satisfies the unbiasedness for the ground-truth ranking metric. We conducted numerical experiments on real-world two-sided platforms and demonstrated the effectiveness of our proposed method in terms of both precision and robustness. Our experiments showed that our method outperformed baselines especially when handling rare items, which are less frequently observed in the training data.

Language speakers often use what are known as repair initiators to mend fundamental disconnects that occur between them during verbal communication. Previous research in this field has mainly focused on the human-to-human use of repair initiator. We proposed an examination of dialogue repair structure wherein the dialogue initiator is human and the party that initiates or responds to the repair is a virtual assistant. This study examined the use of repair initiators in both English and Spanish with two popular assistants, Google Assistant and Apple's Siri. Our aim was to codify the differences, if any, in responses by voice assistants to dialogues in need of repair as compared to human-human dialogues also in need of repair. Ultimately the data demonstrated that not only were there differences between human-assistant and human-human dialogue repair strategies, but that there were likewise differences among the assistants and the languages studied.

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. Updated Wednesday, July 12: We added a bunch more deals we love and added prices and links. If you buy something using links in our stories, we may earn a commission. This helps support our journalism. See the rest of our Phone and Tablet Deals for Prime Day here. Even with the addition of the 10th-generation iPad, we still think the ninth-generation iPad (8/10, WIRED Recommends) from 2021 is the best iPad for most people. It has the same shape and size as its predecessors, so all current accessories will work, including the first-generation Apple Pencil and Apple's Smart Keyboard. It retains the classic Home button with Touch ID plus thick borders around the 10.2-inch screen. This is the lowest price we've tracked. The Apple Pencil is one of the most useful tools you can add to the iPad. The second-gen pencil works with nearly every iPad in Apple's current lineup (except for the 9th- and 10th-gen iPad; if you have one, the first-gen Pencil is also on sale). Like a normal pencil, your lines get thicker as you press down harder. The Pencil is also great for navigating iPadOS, which has handwriting support in various search fields so you don't need to switch to the keyboard to type. It pairs and charges automatically when you stick it to the edge of the slate. Logitech's Combo Touch case is detachable, so you can ditch the keyboard when you don't need it and still have a kickstand case. It's fairly slim, with a lovely fabric texture, and the kickstand easily passes the lap test--it didn't wobble much or make the iPad fall off while you typed with it on your lap. If you want a bigger screen for travel, the iPad Mini (8/10, WIRED Recommends) has the edge over its peers. The design mimics the iPad Pro, with slim bezels around the 8.3-inch screen. Its compact size makes it the best slate to take with you everywhere. You might even be able to fit it into your cargo pants.

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. Updated Wednesday, July 12: We added a bunch more deals we love and added prices and links. If you buy something using links in our stories, we may earn a commission. This helps support our journalism. See the rest of our Phone and Tablet Deals for Prime Day here. Even with the addition of the 10th-generation iPad, we still think the ninth-generation iPad (8/10, WIRED Recommends) from 2021 is the best iPad for most people. It has the same shape and size as its predecessors, so all current accessories will work, including the first-generation Apple Pencil and Apple's Smart Keyboard. It retains the classic Home button with Touch ID plus thick borders around the 10.2-inch screen. This is the lowest price we've tracked. The Apple Pencil is one of the most useful tools you can add to the iPad. The second-gen pencil works with nearly every iPad in Apple's current lineup (except for the 9th- and 10th-gen iPad; if you have one, the first-gen Pencil is also on sale). Like a normal pencil, your lines get thicker as you press down harder. The Pencil is also great for navigating iPadOS, which has handwriting support in various search fields so you don't need to switch to the keyboard to type. It pairs and charges automatically when you stick it to the edge of the slate. Logitech's Combo Touch case is detachable, so you can ditch the keyboard when you don't need it and still have a kickstand case. It's fairly slim, with a lovely fabric texture, and the kickstand easily passes the lap test--it didn't wobble much or make the iPad fall off while you typed with it on your lap. If you want a bigger screen for travel, the iPad Mini (8/10, WIRED Recommends) has the edge over its peers. The design mimics the iPad Pro, with slim bezels around the 8.3-inch screen. Its compact size makes it the best slate to take with you everywhere. You might even be able to fit it into your cargo pants.

Automated Machine Learning (AutoML) techniques have recently been introduced to design Collaborative Filtering (CF) models in a data-specific manner. However, existing works either search architectures or hyperparameters while ignoring the fact they are intrinsically related and should be considered together. This motivates us to consider a joint hyperparameter and architecture search method to design CF models. However, this is not easy because of the large search space and high evaluation cost. To solve these challenges, we reduce the space by screening out usefulness yperparameter choices through a comprehensive understanding of individual hyperparameters. Next, we propose a two-stage search algorithm to find proper configurations from the reduced space. In the first stage, we leverage knowledge from subsampled datasets to reduce evaluation costs; in the second stage, we efficiently fine-tune top candidate models on the whole dataset. Extensive experiments on real-world datasets show better performance can be achieved compared with both hand-designed and previous searched models. Besides, ablation and case studies demonstrate the effectiveness of our search framework.

Recent studies show that graph neural networks (GNNs) are prevalent to model high-order relationships for collaborative filtering (CF). Towards this research line, graph contrastive learning (GCL) has exhibited powerful performance in addressing the supervision label shortage issue by learning augmented user and item representations. While many of them show their effectiveness, two key questions still remain unexplored: i) Most existing GCL-based CF models are still limited by ignoring the fact that user-item interaction behaviors are often driven by diverse latent intent factors (e.g., shopping for family party, preferred color or brand of products); ii) Their introduced non-adaptive augmentation techniques are vulnerable to noisy information, which raises concerns about the model's robustness and the risk of incorporating misleading self-supervised signals. In light of these limitations, we propose a Disentangled Contrastive Collaborative Filtering framework (DCCF) to realize intent disentanglement with self-supervised augmentation in an adaptive fashion. With the learned disentangled representations with global context, our DCCF is able to not only distill finer-grained latent factors from the entangled self-supervision signals but also alleviate the augmentation-induced noise. Finally, the cross-view contrastive learning task is introduced to enable adaptive augmentation with our parameterized interaction mask generator. Experiments on various public datasets demonstrate the superiority of our method compared to existing solutions. Our model implementation is released at the link https://github.com/HKUDS/DCCF.