A model-based collaborative filtering (CF) approach utilizing fast adaptive randomized singular value decomposition (SVD) is proposed for the matrix completion problem in recommender system. Firstly, a fast adaptive PCA frameworkis presented which combines the fixed-precision randomized matrix factorization algorithm [1] and accelerating skills for handling large sparse data. Then, a novel termination mechanism for the adaptive PCA is proposed to automatically determine a number of latent factors for achieving the near optimal prediction accuracy during the subsequent model-based CF. The resulted CF approach has good accuracy while inheriting high runtime efficiency. Experiments on real data show that, the proposed adaptive PCA is up to 2.7X and 6.7X faster than the original fixed-precision SVD approach [1] and svds in Matlab repsectively, while preserving accuracy. The proposed model-based CF approach is able to efficiently process the MovieLens data with 20M ratings and exhibits more than 10X speedup over the regularized matrix factorization based approach [2] and the fast singular value thresholding approach [3] with comparable or better accuracy. It also owns the advantage of parameter free. Compared with the deep-learning-based CF approach, the proposed approach is much more computationally efficient, with just marginal performance loss.

So you're interested in AI? Then join our online event, TNW2020, where you'll hear how artificial intelligence is transforming industries and businesses. The coronavirus pandemic has clearly accelerated our dependency on technology, online activities, and artificial intelligence. AI is particularly important for businesses as it enables personalized services on a massive scale, and customers are increasingly demanding it. However, not every company has the knowledge or the tools to implement AI, nor do they know what is required from them to become AI-driven. In this post, I will discuss what options these companies have.

Amazon wants to make it easier to have a smart home without actually owning a home. Today, the company is unveiling its "Alexa for Residential" program that lets property managers add Alexa-enabled devices and experiences to rental properties. Landlords and property managers can install Alexa devices, pre-fill info like device name, address and Wi-Fi network, pre-enable Alexa skills and set interactions with other smart home devices, like lights and appliances. Residents will be able to connect their personal accounts and additional devices. Amazon says 84 percent of renters want an apartment with smart home amenities, and 61 percent said they would pay a monthly fee for a voice assistant.

TCL's upcoming true wireless earbuds will cost less than €100 (about $120), the company said today. The MOVEAUDIO S200 offer features you'd hope for in true wireless earbuds, like electronic noise reduction (ENC) to reduce background noise, touch controls, wear detection and Google Assistant and Siri compatibility. Battery life is nothing to brag about here. The MOVEAUDIO S200 only last 3.5 hours on a single charge, but you can stretch that to 23 hours if you use the charging case strategically. As a consolation, the earbuds are IP54 rated, so they'll survive water, dust and sweat.

In recent years, chat-bot has become a new type of intelligent terminal to guide users to consume services. However, it is criticized most that the services it provides are not what users expect or most expect. This defect mostly dues to two problems, one is that the incompleteness and uncertainty of user's requirement expression caused by the information asymmetry, the other is that the diversity of service resources leads to the difficulty of service selection. Conversational bot is a typical mesh device, so the guided multi-rounds Q$\&$A is the most effective way to elicit user requirements. Obviously, complex Q$\&$A with too many rounds is boring and always leads to bad user experience. Therefore, we aim to obtain user requirements as accurately as possible in as few rounds as possible. To achieve this, a user intention recognition method based on Knowledge Graph (KG) was developed for fuzzy requirement inference, and a requirement elicitation method based on Granular Computing was proposed for dialog policy generation. Experimental results show that these two methods can effectively reduce the number of conversation rounds, and can quickly and accurately identify the user intention.

Pakistan has blocked Tinder, Grindr and three other dating apps for not adhering to local laws, its latest move to curb online platforms deemed to be disseminating "immoral content". Pakistan, the second-largest Muslim-majority country in the world after Indonesia, is an Islamic nation where extra-marital relationships and homosexuality are illegal. On Tuesday, the Pakistan Telecommunications Authority said it has sent notices to the management of the five apps, "keeping in view the negative effects of immoral/indecent content streaming". Press Release: PTA has blocked access to five dating/live streaming applications i.e. PTA said the notices issued to Tinder, Grindr, Tagged, Skout and SayHi sought the removal of "dating services" and moderation of live streaming content in accordance with local laws.

A recommender system generates personalized recommendations for a user by computing the preference score of items, sorting the items according to the score, and filtering the top-Kitemswith high scores. While sorting and ranking items are integral for this recommendation procedure,it is nontrivial to incorporate them in the process of end-to-end model training since sorting is non-differentiable and hard to optimize with gradient-based updates. This incurs the inconsistency issue between the existing learning objectives and ranking-based evaluation metrics of recommendation models. In this work, we present DRM (differentiable ranking metric) that mitigates the inconsistency and improves recommendation performance, by employing the differentiable relaxation of ranking-based evaluation metrics. Via experiments with several real-world datasets, we demonstrate that the joint learning of the DRM cost function upon existing factor based recommendation models significantly improves the quality of recommendations, in comparison with other state-of-the-art recommendation methods.

A growing proportion of human interactions are digitized on social media platforms and subjected to algorithmic decision-making, and it has become increasingly important to ensure fair treatment from these algorithms. In this work, we investigate gender bias in collaborative-filtering recommender systems trained on social media data. We develop neural fair collaborative filtering (NFCF), a practical framework for mitigating gender bias in recommending sensitive items (e.g. jobs, academic concentrations, or courses of study) using a pre-training and fine-tuning approach to neural collaborative filtering, augmented with bias correction techniques. We show the utility of our methods for gender de-biased career and college major recommendations on the MovieLens dataset and a Facebook dataset, respectively, and achieve better performance and fairer behavior than several state-of-the-art models.

We introduce a new non-negative matrix factorization (NMF) method for ordinal data, called OrdNMF. Ordinal data are categorical data which exhibit a natural ordering between the categories. In particular, they can be found in recommender systems, either with explicit data (such as ratings) or implicit data (such as quantized play counts). OrdNMF is a probabilistic latent factor model that generalizes Bernoulli-Poisson factorization (BePoF) and Poisson factorization (PF) applied to binarized data. Contrary to these methods, OrdNMF circumvents binarization and can exploit a more informative representation of the data. We design an efficient variational algorithm based on a suitable model augmentation and related to variational PF. In particular, our algorithm preserves the scalability of PF and can be applied to huge sparse datasets. We report recommendation experiments on explicit and implicit datasets, and show that OrdNMF outperforms BePoF and PF applied to binarized data.

Television game shows have made dating a form of entertainment for the masses for decades. More recently reality TV has adopted the genre, with hits like Love Island and First Dates. Now the phenomenon has moved online - and anyone can play. "I remember it as if it were yesterday. I was so nervous to even say, 'Hi'. The first time I saw him smiling, my heart told me. 'That's the one, I'm going to make that man happy whatever it takes."