Matrix completion is a class of machine learning methods that concerns the prediction of missing entries in a partially observed matrix. This paper studies matrix completion for mixed data, i.e., data involving mixed types of variables (e.g., continuous, binary, ordinal). We formulate it as a low-rank matrix estimation problem under a general family of non-linear factor models and then propose entrywise consistent estimators for estimating the low-rank matrix. Tight probabilistic error bounds are derived for the proposed estimators. The proposed methods are evaluated by simulation studies and real-data applications for collaborative filtering and large-scale educational assessment.

Recent studies on Next-basket Recommendation (NBR) have achieved much progress by leveraging Personalized Item Frequency (PIF) as one of the main features, which measures the frequency of the user's interactions with the item. However, taking the PIF as an explicit feature incurs bias towards frequent items. Items that a user purchases frequently are assigned higher weights in the PIF-based recommender system and appear more frequently in the personalized recommendation list. As a result, the system will lose the fairness and balance between items that the user frequently purchases and items that the user never purchases. We refer to this systematic bias on personalized recommendation lists as frequency bias, which narrows users' browsing scope and reduces the system utility. We adopt causal inference theory to address this issue. Considering the influence of historical purchases on users' future interests, the user and item representations can be viewed as unobserved confounders in the causal diagram. In this paper, we propose a deconfounder model named FENDER (Frequency-aware Deconfounder for Next-basket Recommendation) to mitigate the frequency bias. With the deconfounder theory and the causal diagram we propose, FENDER decomposes PIF with a neural tensor layer to obtain substitute confounders for users and items. Then, FENDER performs unbiased recommendations considering the effect of these substitute confounders. Experimental results demonstrate that FENDER has derived diverse and fair results compared to ten baseline models on three datasets while achieving competitive performance. Further experiments illustrate how FENDER balances users' historical purchases and potential interests.

Recommender systems are considered one of the most rapidly growing branches of Artificial Intelligence. The demand for finding more efficient techniques to generate recommendations becomes urgent. However, many recommendations become useless if there is a delay in generating and showing them to the user. Therefore, we focus on improving the speed of recommendation systems without impacting the accuracy. In this paper, we suggest a novel recommender system based on Factorization Machines and Association Rules (FMAR). We introduce an approach to generate association rules using two algorithms: (i) apriori and (ii) frequent pattern (FP) growth. These association rules will be utilized to reduce the number of items passed to the factorization machines recommendation model. We show that FMAR has significantly decreased the number of new items that the recommender system has to predict and hence, decreased the required time for generating the recommendations. On the other hand, while building the FMAR tool, we concentrate on making a balance between prediction time and accuracy of generated recommendations to ensure that the accuracy is not significantly impacted compared to the accuracy of using factorization machines without association rules.

As an attempt to cope with massive range of options, there has been large academic and industry interest in automatically recommending items to individuals since last century. Spotify, Amazon, Netflix, and Facebook are some popular platforms that actively use recommender systems [13]. From e-commerce to online advertisement, these systems are unavoidable in our daily online journeys to suggest items in a personalized way. Collaborative Filtering (CF) approaches, firstly proposed by [19], are currently seen as the widest implemented and most mature of the technologies to build recommender systems. Given a set of observed item ratings, CF aims at estimating unknown preferences based on the assumption that users with similar preferences in the past will yield similar preferences in the future. Despite the role of Collaborative Filtering, significant challenges limit its effectiveness, including the diversity and locality of user preferences, the structural sparsity of user-item ratings, the subjectivity of rating scales, and the increasingly large user and item bases [13, 49]. To address the diversity of user profiles, reduce the dimensionality and minimize rating sparsity, matrix factorization and clustering approaches have been combined within CF approaches for two decades [13]. However, traditional clustering techniques are typically applied to either group users or items separately. In real-world CF scenarios, the preferences of a subset of users is frequently only significantly correlated on a subset of the overall items, and vice versa [47].

When it comes to online dating, most people think of Tinder. It's one of the most popular dating apps out there and for good reason – it works! But if you're not careful, you can end up wasting a lot of time and money on Tinder. That's why I recommend buying Tinder accounts from a reputable source. By doing this, you'll be able to get started on Tinder right away without having to worry about all the hassle that comes with setting up a new account.

In this blog I will help you build a Recommender system. This system will try to learn the behavior of user and try to make recommendations accordingly. As the first step, we will make a system which gives 5 options to the user to choose from and based on the past user choice, the system would recommend most probable option the user would choose. This solution can be extended to many applications. This solution is based on Beta Distribution.

Amazon's Alexa Together caregiver service is now more useful if you want to know a loved one's specific activities. The company has added custom alerts that ping up to 10 caregivers when there's particular smart home activity. You'll know if the care receiver opened a sensor-equipped medicine cabinet, for instance, or whether the bedroom light turned on at the right time in the morning. You can enable custom alerts through the "More" section in the Alexa App. Alexa Together costs $20 per month or $199 per year, and requires at least one Echo device for the person receiving support. It's currently available only in the US and requires Amazon accounts for both the caregiver and the care recipient.

'AI' and/or'Machine Learning' as it's known is becoming more prevalent in the working environment. From'rogue algorithms' upsetting GCSE gradings through to Microsoft 365 judging you for only working on one document all day, we cannot escape the fact that there are more'automated' services than ever before. For DPOs, records managers and IG officers, this poses some interesting challenges to the future of records, information and personal data. I was asked to talk about the challenges of AI and machine learning at a recent IRMS Public Sector Group webinar. In the session titled'IRM challenges of AI & something called the'metaverse' we looked at a range of issues, some of which I'd like to touch on a little bit below.

Did you miss a session from GamesBeat Summit Next 2022? All sessions are now available for viewing in our on-demand library. Click here to start watching. With Google Assistant inside and conversational AI, these speakers can do a great range of things. Here's 10 worth trying, drawn from VentureBeat coverage over the course of the past year. Before getting into the more dynamic features Google Assistant provides through Home smart speakers, start with the most popular ways people use speakers with intelligent assistants.

Question-answering (QA) that comes naturally to humans is a critical component in seamless human-computer interaction. It has emerged as one of the most convenient and natural methods to interact with the web and is especially desirable in voice-controlled environments. Despite being one of the oldest research areas, the current QA system faces the critical challenge of handling multilingual queries. To build an Artificial Intelligent (AI) agent that can serve multilingual end users, a QA system is required to be language versatile and tailored to suit the multilingual environment. Recent advances in QA models have enabled surpassing human performance primarily due to the availability of a sizable amount of high-quality datasets. However, the majority of such annotated datasets are expensive to create and are only confined to the English language, making it challenging to acknowledge progress in foreign languages. Therefore, to measure a similar improvement in the multilingual QA system, it is necessary to invest in high-quality multilingual evaluation benchmarks. In this dissertation, we focus on advancing QA techniques for handling end-user queries in multilingual environments. This dissertation consists of two parts. In the first part, we explore multilingualism and a new dimension of multilingualism referred to as code-mixing. Second, we propose a technique to solve the task of multi-hop question generation by exploiting multiple documents. Experiments show our models achieve state-of-the-art performance on answer extraction, ranking, and generation tasks on multiple domains of MQA, VQA, and language generation. The proposed techniques are generic and can be widely used in various domains and languages to advance QA systems.