--Phishing is the most prevalent type of cyber-attack today and is recognized as the leading source of data breaches with significant consequences for both individuals and corporations. Web-based phishing attacks are the most frequent with vectors such as social media posts and emails containing links to phishing URLs that once clicked on render host systems vulnerable to more sinister attacks. Research efforts to detect phishing URLs have involved the use of supervised learning techniques that use large amounts of data to train models and have high computational requirements. They also involve analysis of features derived from vectors including email contents thus affecting user privacy. Additionally, they suffer from a lack of resilience against evolution of threats especially with the advent of generative AI techniques to bypass these systems as with AI-generated phishing URLs. Unsupervised methods such as clustering techniques have also been used in phishing detection in the past, however, they are at times unscalable due to the use of pair-wise comparisons. They also lack high detection rates while detecting phishing campaigns. In this paper, we propose an unsupervised learning approach that is not only fast but scalable, as it does not involve pair-wise comparisons. It is able to detect entire campaigns at a time with a high detection rate while preserving user privacy; this includes the recent surge of campaigns with targeted phishing URLs generated by malicious entities using generative AI techniques.

Collaborative filtering based recommendation learns users' preferences from all users' historical behavior data, and has been popular to facilitate decision making. R Recently, the fairness issue of recommendation has become more and more essential. A recommender system is considered unfair when it does not perform equally well for different user groups according to users' sensitive attributes~(e.g., gender, race). Plenty of methods have been proposed to alleviate unfairness by optimizing a predefined fairness goal or changing the distribution of unbalanced training data. However, they either suffered from the specific fairness optimization metrics or relied on redesigning the current recommendation architecture. In this paper, we study how to improve recommendation fairness from the data augmentation perspective. The recommendation model amplifies the inherent unfairness of imbalanced training data. We augment imbalanced training data towards balanced data distribution to improve fairness. The proposed framework is generally applicable to any embedding-based recommendation, and does not need to pre-define a fairness metric. Extensive experiments on two real-world datasets clearly demonstrate the superiority of our proposed framework. We publish the source code at https://github.com/newlei/FDA.

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.

Today, we're excited to announce a new similar items recommendation recipe (aws-similar-items) in Amazon Personalize that helps you leverage your users' interaction histories and what you know about the items in your catalog to deliver relevant recommendations. Across Amazon, we provide personalized experiences for each of our users, and based on a user's interests, we change their experiences and the items they see. Visitors are often recommended items that users with similar histories have interacted with. These recommendations are called similar items, and they help users discover items relevant to what they're watching or purchasing. By taking into account the item a user is engaged with, we can improve engagement and conversion.

I'm talking about the demise of the popular KNN algorithm that is taught in pretty much every Data Science course! Read on to find out what's replacing this staple in every Data Scientists' toolkit. Finding "K" similar items to any given item is widely known in the machine learning community as a "similarity" search or "nearest neighbor" (NN) search. The most widely known NN search algorithm is the K-Nearest Neighbours (KNN) algorithm. In KNN, given a collection of objects like an e-commerce catalog of handphones, we can find a small number (K) nearest neighbors from this entire catalog for any new search query.

He credits the gains to advances in smart software. Rather than asking customers to browse through the entire catalog of mugs, he says, algorithms, artificial intelligence and troves of data "are doing the work behind the scenes." Since the coronavirus outbreak, online retailers like Wayfair, Etsy Inc. ETSY 3.99% and Pinterest Inc. PINS -0.97% are ratcheting up efforts to leverage data from a surge in e-commerce to get better at helping customers find what they are looking for--even when they don't know what that is. To do that, these Web-only stores are supercharging search-and-recommendation engines by feeding data into sophisticated algorithms, building predictive models with a level of accuracy unimaginable just a few years ago. Not all of the capabilities are new--algorithms have been around for decades.

More online businesses are integrating machine learning into their operations, with the bigger and established ones trailblazing the revolution. Machine learning has brought myriad opportunities and improved strategies to help business owners foster customer relationships and get more profit and conversions. If you haven't fully leveraged the power of machine learning in your business, let me give you five reasons why you should do so now. Can you imagine buying from the grocery store without having to wait in line to pay for your goods? If you can't, then you'd better prepared because that is now a reality.

Artificial Intelligence (AI) is no longer a science fiction whimsy, but an everyday reality. AI is now embedded in many aspects of our lives, from supermarket self-checkout cash registers to face-recognition security checks at the airport. Tech giants such as Microsoft and Google are investing millions of dollars in new AI projects. Initiatives such as a Teachable Machine, which trains computer neural network, are making Machine Learning (ML) user-friendly. Technologies such as neural networks and deep learning are being applied in many sectors by companies like Facebook and IBM.

Google could soon make searching the web even simpler. Rumors have suggested that the tech giant is set to add a screenshot search filter to its 10.61 app. Called'Smart Screenshots', this feature works with an Lens to find similar items online just by scanning your screenshot. Google could add a screenshot search filter to its 10.61 app. Called'Smart Screenshots', this feature works with Lens to find similar items on the web just by scanning your screenshot When activated, Smart Screenshots will open an updated version of the toolbar, Engadget reported.

We propose a novel learning framework to answer questions such as "if a user is purchasing a shirt, what other items will (s)he need with the shirt?" Our framework learns distributed representations for items from available textual data, with the learned representations representing items in a latent space expressing functional complementarity as well similarity. In particular, our framework places functionally similar items close together in the latent space, while also placing complementary items closer than non-complementary items, but farther away than similar items. In this study, we introduce a new dataset of similar, complementary, and negative items derived from the Amazon co-purchase dataset. For evaluation purposes, we focus our approach on clothing and fashion verticals. As per our knowledge, this is the first attempt to learn similar and complementary relationships simultaneously through just textual title metadata. Our framework is applicable across a broad set of items in the product catalog and can generate quality complementary item recommendations at scale.