Recent advances in path-based explainable recommendation systems have attracted increasing attention thanks to the rich information provided by knowledge graphs. Most existing explainable recommendations only utilize static knowledge graphs and ignore the dynamic user-item evolutions, leading to less convincing and inaccurate explanations. Although there are some works that realize that modelling user's temporal sequential behaviour could boost the performance and explainability of the recommender systems, most of them either only focus on modelling user's sequential interactions within a path or independently and separately of the recommendation mechanism. In this paper, we propose a novel Temporal Meta-path Guided Explainable Recommendation leveraging Reinforcement Learning (TMER-RL), which utilizes reinforcement item-item path modelling between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on dynamic knowledge graph for explainable recommendation. Compared with existing works that use heavy recurrent neural networks to model temporal information, we propose simple but effective neural networks to capture users' historical item features and path-based context to characterize the next purchased item. Extensive evaluations of TMER on two real-world datasets show state-of-the-art performance compared against recent strong baselines.

Collaborative filtering (CF) is an important approach for recommendation system which is widely used in a great number of aspects of our life, heavily in the online-based commercial systems. One popular algorithms in CF is the K-nearest neighbors (KNN) algorithm, in which the similarity measures are used to determine nearest neighbors of a user, and thus to quantify the dependency degree between the relative user/item pair. Consequently, CF approach is not just sensitive to the similarity measure, yet it is completely contingent on selection of that measure. While Jaccard - as one of those commonly used similarity measures for CF tasks - concerns the existence of ratings, other numerical measures such as cosine and Pearson concern the magnitude of ratings. Particularly speaking, Jaccard is not a dominant measure, but it is long proven to be an important factor to improve any measure. Therefore, in our continuous efforts to find the most effective similarity measures for CF, this research focuses on proposing new similarity measure via combining Jaccard with several numerical measures. The combined measures would take the advantages of both existence and magnitude. Experimental results on, Movie-lens dataset, showed that the combined measures are preeminent outperforming all single measures over the considered evaluation metrics.

Early Black Friday deals are already rolling out, with more new sales showing up every day. For 2021, there's concern whether shipping issues will mean less stuff on the shelves for the holidays. As we've already been hearing for months, the global supply chain is seriously backed up with bottlenecks occurring at key points. Hoping to deal with an already difficult situation, retailers stretched out the shopping season as much as they dared, what with shipping timelines stretching to frustrating levels. These supply problems are supposed to be felt acutely in technology, or so the market watchers say.

Designing recommendation systems with limited or no available training data remains a challenge. To that end, a new combinatorial optimization problem is formulated to generate optimized item selection for experimentation with the goal to shorten the time for collecting randomized training data. We first present an overview of the optimized item selection problem and a multi-level optimization framework to solve it. The approach integrates techniques from discrete optimization, unsupervised clustering, and latent text embeddings. We then discuss how to incorporate optimized item selection with active learning as part of randomized exploration in an ongoing fashion.

The core objective of modelling recommender systems from implicit feedback is to maximize the positive sample score $s_p$ and minimize the negative sample score $s_n$, which can usually be summarized into two paradigms: the pointwise and the pairwise. The pointwise approaches fit each sample with its label individually, which is flexible in weighting and sampling on instance-level but ignores the inherent ranking property. By qualitatively minimizing the relative score $s_n - s_p$, the pairwise approaches capture the ranking of samples naturally but suffer from training efficiency. Additionally, both approaches are hard to explicitly provide a personalized decision boundary to determine if users are interested in items unseen. To address those issues, we innovatively introduce an auxiliary score $b_u$ for each user to represent the User Interest Boundary(UIB) and individually penalize samples that cross the boundary with pairwise paradigms, i.e., the positive samples whose score is lower than $b_u$ and the negative samples whose score is higher than $b_u$. In this way, our approach successfully achieves a hybrid loss of the pointwise and the pairwise to combine the advantages of both. Analytically, we show that our approach can provide a personalized decision boundary and significantly improve the training efficiency without any special sampling strategy. Extensive results show that our approach achieves significant improvements on not only the classical pointwise or pairwise models but also state-of-the-art models with complex loss function and complicated feature encoding.

We've all seen how artificial intelligence has established itself in a variety of human activities. As a result, the technology has received widespread recognition from industry players, investors, and businesses that have begun to equip their machines with intelligence, deploy automation and employ robots to perform tasks. A.I. is all around us, even if you aren't aware of it. Your insurer may be calculating your insurance using AI-powered policy software. Likewise, your bank is most likely assessing the risk of you defaulting on a loan.

In last 2–3 decades lots of technologies and computer languages discovered for the purpose of research, better learning and for more opportunities in Machines and Computing fields. One of the language on those discoveries are Machine Learning. The study of machine learning (ML) is the study of algorithms that develop automatically through different experiences and by accumulating data. Machine Learning is a branch of AI(artificial Intelligence). Artificial intelligence (AI) and computer science have developed machine learning techniques to reproduce how humans learn by using data and algorithms.

If we think of the most successful and widespread applications of machine learning in business, one of the examples would be recommender systems. Each time you visit Amazon or Netflix, you see recommended items or movies that you might like -- the product of recommender systems incorporated by these companies. Though a recommender system is a rather simple algorithm that discovers patterns in a dataset, rates items, and shows the user the items that they might rate highly, they have the power to boost sales of many e-commerce and retail companies. In simple words, these systems predict users' interests and recommend relevant items. User-item interactions -- the information about ratings, number of purchases, likes, and so on.

This November, the Smithsonian's FUTURES festival, featuring innovations that are set to change the world, will include a familiar face. Or, rather, voice: Q, introduced in 2019 as the first "genderless AI voice," is a human voice for use in digital assistants specifically created to be gender-ambiguous. "Q was designed to start a conversation around why we gender technology when technology has no gender to begin with," says Ryan Sherman, one of Q's co-creators. To design the voice, a team of linguists, sound engineers, and creatives collaborated with nonbinary individuals and sampled different voices to land on a sound range they felt had the potential to disrupt the status quo and represent nonbinary people in the world of AI. When Q was announced several years ago, it was hailed as "the genderless digital voice the world needs right now," and an acknowledgment of the harm of feminizing assistants, which perpetuates misogynistic stereotypes of women as submissive and obedient.

I came into the kitchen recently to find my husband cradling our electricity smart meter with the kind of tender attention more usually directed to a new-born, his phone clutched in his free hand. "You didn't turn your office heater off last night," he said. I went in this morning to turn it on again!" "Last night we used 10…" (here he added a unit, presumably of electricity, but all that stuff is Martian to me. "It shouldn't be that high." "But I turned it off!" But our smart home had spoken and it is far more reliable than me, his life partner of 26 years. Our house now has app-enabled devices to control the heating and the boiler remotely, to check temperature, CO2 and noise levels and to see who is at the door. There are motion-detector cameras in the garden that send us videos of foxes threatening my hens, or his tortoises escaping. Since we installed a few solar panels, my husband's smart-home management has become more urgent and more granular. An app tells him how much we are consuming, but also how much we are producing, in real time. Now he bursts in when it's sunny, shouting "We're giving electricity to the grid!