Online platforms like Pinterest hosting vast content collections traditionally rely on manual curation or user-generated search logs to create keyword landing pages (KLPs) -- topic-centered collection pages that serve as entry points for content discovery. While manual curation ensures quality, it doesn't scale to millions of collections, and search log approaches result in limited topic coverage and imprecise content matching. In this paper, we present PinLanding, a novel content-first architecture that transforms the way platforms create topical collections. Instead of deriving topics from user behavior, our system employs a multi-stage pipeline combining vision-language model (VLM) for attribute extraction, large language model (LLM) for topic generation, and a CLIP-based dual-encoder architecture for precise content matching. Our model achieves 99.7% Recall@10 on Fashion200K benchmark, demonstrating strong attribute understanding capabilities. In production deployment for search engine optimization with 4.2 million shopping landing pages, the system achieves a 4X increase in topic coverage and 14.29% improvement in collection attribute precision over the traditional search log-based approach via human evaluation. The architecture can be generalized beyond search traffic to power various user experiences, including content discovery and recommendations, providing a scalable solution to transform unstructured content into curated topical collections across any content domain.

Today PCWorld is launching Smart Answers, a chatbot tool that helps you get more from our content. It's built using Generative AI and existing content written by our human editors. The way we interact with content is changing. It wasn't so long ago you would have sifted through a printed magazine for advice on the latest consumer technology, yet it felt like a revolution when those old mags switched over to digital and online editions. These days, everything you could ever want to read is on the internet--or just as likely on YouTube or TikTok.

TikTok could soon have a new way for users to discover content. The company is in the "early stages" of testing an AI-powered chatbot, called Tako, which will be able to recommend videos and respond to queries about what users are watching. The bot, which was first reported by TechCrunch, is currently being tested in the Philippines, TikTok said in a statement. "Tako is powered by a third-party chat assistant and is designed to help make it easier to discover entertaining and inspiring content on TikTok," the company said. Despite being in an early phase of testing, TikTok is apparently featuring Tako fairly prominently in the app. A shortcut to the assistant sits in the main right-hand menu alongside shortcuts for bookmarks, and likes, according to TechCrunch, which got a peek at the feature.

The main focus of information retrieval methods is to provide accurate and efficient results which are cost-effective too. LINGO (Label Induction Grouping Algorithm) is a clustering algorithm that aims to provide search results in form of quality clusters but also has a few limitations. In this paper, our focus is based on achieving results that are more meaningful and improving the overall performance of the algorithm. LINGO works on two main steps; Cluster Label Induction by using Latent Semantic Indexing technique (LSI) and Cluster content discovery by using the Vector Space Model (VSM). As LINGO uses VSM in cluster content discovery, our task is to replace VSM with LSI for cluster content discovery and to analyze the feasibility of using LSI with Okapi BM25. The next task is to compare the results of a modified method with the LINGO original method. The research is applied to five different text-based data sets to get more reliable results for every method. Research results show that LINGO produces 40-50% better results when using LSI for content Discovery. From theoretical evidence using Okapi BM25 for scoring method in LSI (LSI+Okapi BM25) for cluster content discovery instead of VSM, also results in better clusters generation in terms of scalability and performance when compares to both VSM and LSI's Results.

MarketMuse uses AI to compare search engine knowledge graphs against your site's content inventory, then recommends what content to create to rank better for specific topics. Create content that answers top customer questions. Questions matter, both to customers and to search results. In fact, Google prominently features snippets that answer common questions searchers ask. Frase uses AI to help marketers create and optimize content that answers customers' questions by automatically fielding customer questions on your website.

Automation is affecting every aspect of modern living and TV is no different. Jonathan Easton examines the role that AI is playing in the user experience and how'the algorithm' has quietly revolutionised the way we view content. Every few years, a new fad takes the industry by storm. In the early part of this decade it was 3D, and after that it was virtual reality, augmented reality and everything in between. But artificial intelligence (AI) is no such fad.

On any given day we can find a story in the media about technology and its impact on society. But of equal importance are the ways in which new technologies like artificial intelligence (AI) and data analytics are shaping the media itself. The volume of content and the speed at which it is disseminated have both increased dramatically in the past ten years because of new technology platforms like Facebook and Twitter. The types of news pushed through these platforms or what we see when visiting them is being orchestrated by algorithms underpinned by advanced data analytics. And the ways in which we process, cite, and assess stories are all influenced by the types of screens we use and company we keep online. Together, these technologies can spread information and disinformation equally and in real time.

Content discovery, writing, editing, SEO, content distribution, and data analysis are some of the operations that you can automate with AI. Find out five great tools to improve and speed-up your content marketing workflow. What can you do today? In this article, you'll find out which tools can help you create more value, speed-up your operations, promote your content. Narratives about AI go from dystopia--robots will take over human work--to the dream of maximizing outcomes.

Any organization that has a large media archive struggles with the same challenge – how can we transform our media archives into business value? Media content management is hard, and so is content discovery at scale. Content categorization by topics is an intuitive approach that makes it easier for people to search for the content they need. However, content categorization is usually deductive and doesn't necessarily appear explicitly in the video. For example, content that is focused on the topic of'healthcare' may not actually have the word'healthcare' presented in it, which makes the categorization an even harder problem to solve.

In almost every industry today, you're seeing an increase in personalized experiences for consumers. With more people wanting control over how they buy or consume content, entertainment companies are in the midst of abiding by these customer demands. The consumer need to personalize content is a psychological impulse to find more control in a world filled with information overload. Since media content choices are often overwhelming, it's all the more important for consumers to find something fitting their world views. At the center of all this is artificial intelligence. Take a look at how machine learning continues to evolve personalized experiences in entertainment and media.