In recommender systems, a common problem is the presence of various biases in the collected data, which deteriorates the generalization ability of the recommendation models and leads to inaccurate predictions. Doubly robust (DR) learning has been studied in many tasks in RS, with the advantage that unbiased learning can be achieved when either a single imputation or a single propensity model is accurate. In this paper, we propose a multiple robust (MR) estimator that can take the advantage of multiple candidate imputation and propensity models to achieve unbiasedness. Specifically, the MR estimator is unbiased when any of the imputation or propensity models, or a linear combination of these models is accurate. Theoretical analysis shows that the proposed MR is an enhanced version of DR when only having a single imputation and propensity model, and has a smaller bias. Inspired by the generalization error bound of MR, we further propose a novel multiple robust learning approach with stabilization. We conduct extensive experiments on real-world and semi-synthetic datasets, which demonstrates the superiority of the proposed approach over state-of-the-art methods.

Kurt "CyberGuy" Knutsson shows you how to customize your Alexa settings to get her to sound more like you. Take a moment to learn the newest ways to get Alexa to speak the way you want. It will dramatically improve how you are currently using Alexa forever. CLICK TO GET KURT'S CYBERGUY NEWSLETTER WITH QUICK TIPS, TECH REVIEWS, SECURITY ALERTS AND EASY HOW-TO'S TO MAKE YOU SMARTER You can change Alexa's volume directly on most devices or by using your voice, but did you know you can change Alexa's speed at which the device talks to you? Say "Alexa, speak faster" or "Alexa, speak slower" either once, or a few times to get the device speaking at the rate you'd like. If you end up wanting Alexa to go back to the original speed, just say "Alexa, speak at your default rate" and the settings will reset.

If you haven't heard about ChatGPT, the new chatbot in town, there are chances you might be living under a rock. This chatbot developed by OpenAI is known to interact with users like a conversation, answers questions and even gives detailed answers on historical facts. What's more surprising that it is now used by users who are seeking matches on dating apps like Tinder. According to a Mashable report, Tinder users have created bots to swipe and message for them. The app has banned users who use this message, but it has come to the light that even this dating platform uses AI to generate conversation starters.

Rep. Darrell Issa, R-Calif., discusses how the GOP will hold Dr. Anthony Fauci accountable and what the'Twitter files' revealed about censorship on'The Next Revolution.' I use my voice to get a lot done. Siri sets meetings for me, silences my phone, and lots more. Tap or click for five simple voice commands you'll use all the time. An Amazon Echo can help you find your phone, lock the front door and drop in to chat with loved ones.

Implicit feedback is frequently used for developing personalized recommendation services due to its ubiquity and accessibility in real-world systems. In order to effectively utilize such information, most research adopts the pairwise ranking method on constructed training triplets (user, positive item, negative item) and aims to distinguish between positive items and negative items for each user. However, most of these methods treat all the training triplets equally, which ignores the subtle difference between different positive or negative items. On the other hand, even though some other works make use of the auxiliary information (e.g., dwell time) of user behaviors to capture this subtle difference, such auxiliary information is hard to obtain. To mitigate the aforementioned problems, we propose a novel training framework named Triplet Importance Learning (TIL), which adaptively learns the importance score of training triplets. We devise two strategies for the importance score generation and formulate the whole procedure as a bilevel optimization, which does not require any rule-based design. We integrate the proposed training procedure with several Matrix Factorization (MF)- and Graph Neural Network (GNN)-based recommendation models, demonstrating the compatibility of our framework. Via a comparison using three real-world datasets with many state-of-the-art methods, we show that our proposed method outperforms the best existing models by 3-21\% in terms of Recall@k for the top-k recommendation.

We propose a method for generating simulated contextual bandit environments for personalization tasks from recommendation datasets like MovieLens, Netflix, Last.fm, Million Song, etc. This allows for personalization environments to be developed based on real-life data to reflect the nuanced nature of real-world user interactions. The obtained environments can be used to develop methods for solving personalization tasks, algorithm benchmarking, model simulation, and more. We demonstrate our approach with numerical examples on MovieLens and IMDb datasets.

A central goal of recommender systems is to select items according to the "preferences" of their users. "Preferences" is a complicated word that has been used across many disciplines to mean, roughly, "what people want." This has been justified by the assumption that people always choose what they want, an idea from 20th-century economics called revealed preference. However, this approach to preferences can lead to a variety of unwanted outcomes including clickbait, addiction, or algorithmic manipulation. Doing better requires both a change in thinking and a change in approach.

Voice assistants are deployed widely and provide useful functionality. However, recent work has shown that commercial systems like Amazon Alexa and Google Home are vulnerable to voice-based confusion attacks that exploit design issues. We propose a systems-oriented defense against this class of attacks and demonstrate its functionality for Amazon Alexa. We ensure that only the skills a user intends execute in response to voice commands. Our key insight is that we can interpret a user's intentions by analyzing their activity on counterpart systems of the web and smartphones. For example, the Lyft ride-sharing Alexa skill has an Android app and a website. Our work shows how information from counterpart apps can help reduce dis-ambiguities in the skill invocation process. We build SkilIFence, a browser extension that existing voice assistant users can install to ensure that only legitimate skills run in response to their commands. Using real user data from MTurk (N = 116) and experimental trials involving synthetic and organic speech, we show that SkillFence provides a balance between usability and security by securing 90.83% of skills that a user will need with a False acceptance rate of 19.83%.

Tour itinerary planning and recommendation are challenging problems for tourists visiting unfamiliar cities. Many tour recommendation algorithms only consider factors such as the location and popularity of Points of Interest (POIs) but their solutions may not align well with the user's own preferences and other location constraints. Additionally, these solutions do not take into consideration of the users' preference based on their past POIs selection. In this paper, we propose POIBERT, an algorithm for recommending personalized itineraries using the BERT language model on POIs. POIBERT builds upon the highly successful BERT language model with the novel adaptation of a language model to our itinerary recommendation task, alongside an iterative approach to generate consecutive POIs. Our recommendation algorithm is able to generate a sequence of POIs that optimizes time and users' preference in POI categories based on past trajectories from similar tourists. Our tour recommendation algorithm is modeled by adapting the itinerary recommendation problem to the sentence completion problem in natural language processing (NLP). We also innovate an iterative algorithm to generate travel itineraries that satisfies the time constraints which is most likely from past trajectories. Using a Flickr dataset of seven cities, experimental results show that our algorithm out-performs many sequence prediction algorithms based on measures in recall, precision and F1-scores.

British people have a lot of questions, and these days all they have to do is shout at their voice assistant Alexa and they will probably get the answer. Amazon has now revealed its most asked questions for Alexa in Britain this year, ranging from the weird, wonderful and straight-up nosey. From the height of Prime Minister Rishi Sunak to Gordon Ramsay's net worth, hundreds of questions have been asked, with some being more popular than others. The net worth of the second richest man in the world, and new owner of Twitter, Elon Musk, was one of the most frequently asked question from Alexa owners. One of the most popular questions was'Alexa, how tall is Rishi Sunak'.