Nowadays, many platforms on the Web offer organized events, allowing users to be organizers or participants. For such platforms, it is beneficial to predict potential event participants. Existing work on this problem tends to borrow recommendation techniques. However, compared to e-commerce items and purchases, events and participation are usually of a much smaller frequency, and the data may be insufficient to learn an accurate model. In this paper, we propose to utilize social media retweeting activity data to enhance the learning of event participant prediction models. We create a joint knowledge graph to bridge the social media and the target domain, assuming that event descriptions and tweets are written in the same language. Furthermore, we propose a learning model that utilizes retweeting information for the target domain prediction more effectively. We conduct comprehensive experiments in two scenarios with real-world data. In each scenario, we set up training data of different sizes, as well as warm and cold test cases. The evaluation results show that our approach consistently outperforms several baseline models, especially with the warm test cases, and when target domain data is limited.

With Prime Big Deal Days being a little more than one week away, Amazon has kicked things off early by discounting most of its Echo smart speakers. You can get up to 69 percent off Echo devices and bundles right now, and that goes for any Amazon shopping -- not just Prime members. Of note is the Echo Dot for $23, a record low that matches its July Prime Day price, and the Echo Dot bundled with a Kasa Smart Plug Mini for just about $1 more. Considering the Echo Dot is one of our favorite smart speakers and the Kasa accessory earned a spot on our list of best smart plugs, that bundle is an affordable way to get your smart home started. As part of a big Echo speaker sale, this bundle that includes the Echo Dot and a Kasa smart plug is 67 percent off. The Echo Dot was not one of the many Amazon devices to receive an upgrade last month during Amazon's hardware event, which means you're getting the latest model of the speaker in this sale.

Conversational contextual bandits elicit user preferences by occasionally querying for explicit feedback on key-terms to accelerate learning. However, there are aspects of existing approaches which limit their performance. First, information gained from key-term-level conversations and arm-level recommendations is not appropriately incorporated to speed up learning. Second, it is important to ask explorative key-terms to quickly elicit the user's potential interests in various domains to accelerate the convergence of user preference estimation, which has never been considered in existing works. To tackle these issues, we first propose ``ConLinUCB", a general framework for conversational bandits with better information incorporation, combining arm-level and key-term-level feedback to estimate user preference in one step at each time. Based on this framework, we further design two bandit algorithms with explorative key-term selection strategies, ConLinUCB-BS and ConLinUCB-MCR. We prove tighter regret upper bounds of our proposed algorithms. Particularly, ConLinUCB-BS achieves a regret bound of $O(d\sqrt{T\log T})$, better than the previous result $O(d\sqrt{T}\log T)$. Extensive experiments on synthetic and real-world data show significant advantages of our algorithms in learning accuracy (up to 54\% improvement) and computational efficiency (up to 72\% improvement), compared to the classic ConUCB algorithm, showing the potential benefit to recommender systems.

Feel like your smart home assistant needs a bit of an upgrade? Kurt "The CyberGuy" Knutsson shares steps on how you can change your Amazon Alexa's name and accent. Ever felt that your smart home assistant's voice doesn't quite match your aesthetic or mood? Well, Alexa is lending an ear to your preferences. There's a charm in personalizing every tiny detail of our gadgets, and Amazon's Alexa isn't one to be left behind.

Fox News Flash top entertainment and celebrity headlines are here. Sarah Ferguson expressed her shock and grief as she mourned the death of her former personal assistant, Jenean Chapman, who was murdered in Texas this week. The 63-year-old Duchess of York paid tribute to Chapman in an Instagram post that she shared on Thursday. "I am shocked and saddened to learn that Jenean Chapman, who worked with me as my personal assistant many years ago, has been murdered in Dallas aged just 46. A suspect is in custody," Ferguson wrote.

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When it comes to curating a dating profile, singletons may spend countless hours deciding which photographs show their best angles. But experts now suggest that attraction really is just about the animals you're shot with, as 76 per cent of daters would be tempted to swipe right if a feline featured. Dating app, FindingTheOne, polled 2,000 of its users on their preferences and pet peeves when it comes to furry friends online. While dogs are usually deemed a man's best friend, results show they're certainly not the best wingmen, as just 41 per cent of users were tempted to date a pup's parent. Meanwhile, a startling 62 per cent wouldn't mind falling for a snake or lizard owner - and 23 per cent even find them'sexy'.

knowledge graph-based recommendation methods have achieved great success in the field of recommender systems. However, over-reliance on high-quality knowledge graphs is a bottleneck for such methods. Specifically, the long-tailed distribution of entities of KG and noise issues in the real world will make item-entity dependent relations deviate from reflecting true characteristics and significantly harm the performance of modeling user preference. Contrastive learning, as a novel method that is employed for data augmentation and denoising, provides inspiration to fill this research gap. However, the mainstream work only focuses on the long-tail properties of the number of items clicked, while ignoring that the long-tail properties of total number of clicks per user may also affect the performance of the recommendation model. Therefore, to tackle these problems, motivated by the Debiased Contrastive Learning of Unsupervised Sentence Representations (DCLR), we propose Two-Level Debiased Contrastive Graph Learning (TDCGL) model. Specifically, we design the Two-Level Debiased Contrastive Learning (TDCL) and deploy it in the KG, which is conducted not only on User-Item pairs but also on User-User pairs for modeling higher-order relations. Also, to reduce the bias caused by random sampling in contrastive learning, with the exception of the negative samples obtained by random sampling, we add a noise-based generation of negation to ensure spatial uniformity. Considerable experiments on open-source datasets demonstrate that our method has excellent anti-noise capability and significantly outperforms state-of-the-art baselines. In addition, ablation studies about the necessity for each level of TDCL are conducted.

Collaborative filtering (CF) has become a popular method for developing recommender systems (RSs) where ratings of a user for new items are predicted based on her past preferences and available preference information of other users. Despite the popularity of CF-based methods, their performance is often greatly limited by the sparsity of observed entries. In this study, we explore the data augmentation and refinement aspects of Maximum Margin Matrix Factorization (MMMF), a widely accepted CF technique for rating predictions, which has not been investigated before. We exploit the inherent characteristics of CF algorithms to assess the confidence level of individual ratings and propose a semi-supervised approach for rating augmentation based on self-training. We hypothesize that any CF algorithm's predictions with low confidence are due to some deficiency in the training data and hence, the performance of the algorithm can be improved by adopting a systematic data augmentation strategy. We iteratively use some of the ratings predicted with high confidence to augment the training data and remove low-confidence entries through a refinement process. By repeating this process, the system learns to improve prediction accuracy. Our method is experimentally evaluated on several state-of-the-art CF algorithms and leads to informative rating augmentation, improving the performance of the baseline approaches.

Recommender systems have been shown to be vulnerable to poisoning attacks, where malicious data is injected into the dataset to cause the recommender system to provide biased recommendations. To defend against such attacks, various robust learning methods have been proposed. However, most methods are model-specific or attack-specific, making them lack generality, while other methods, such as adversarial training, are oriented towards evasion attacks and thus have a weak defense strength in poisoning attacks. In this paper, we propose a general method, Real-time Vicinal Defense (RVD), which leverages neighboring training data to fine-tune the model before making a recommendation for each user. RVD works in the inference phase to ensure the robustness of the specific sample in real-time, so there is no need to change the model structure and training process, making it more practical. Extensive experimental results demonstrate that RVD effectively mitigates targeted poisoning attacks across various models without sacrificing accuracy. Moreover, the defensive effect can be further amplified when our method is combined with other strategies.