Content Interaction Models

How Machines Understand What You’ll Love Next – The Magic of Recommendation Systems! ✨️ Every time you discover a new favorite must-read book, or the perfect playlist—there’s a recommendation system working behind the scenes. But what really powers these systems? Let’s break down the 3 main approaches: 1. Collaborative Filtering: Learning from User Behavior Imagine you and a friend have similar movie tastes. If they loved a film you haven’t seen, chances are you’ll like it too! That’s the essence of Collaborative Filtering (CF)—it relies on user-item interactions rather than specific item features. User-Based CF – Finds users with similar tastes and recommends what they liked. Item-Based CF – Recommends items similar to what a user has already liked. Matrix Factorization (SVD, ALS) – Decomposes user-item interaction data into hidden patterns. Neural CF (Deep Learning) – Uses neural networks for more complex recommendations. Strengths: Learns from actual behavior, great for discovering new content. Challenges: Struggles with cold start (new users/items) and data sparsity. 2. Content-Based Filtering: Learning from Item Features Instead of relying on what others like, Content-Based Filtering recommends items based on features. For example, if you love sci-fi movies, the system will suggest more sci-fi films based on genre, director, and plot. Uses TF-IDF, Word2Vec, BERT for text-based recommendations. Employs image/audio embeddings for visual/music recommendations. Requires feature engineering—understanding what makes an item unique. Strengths: Works well for niche preferences, doesn’t need a large user base. Challenges: Limited diversity—if you watch action movies, it keeps recommending action movies! 3. Hybrid Models: The Best of Both Worlds To overcome the limitations of both approaches, Hybrid Models combine Collaborative Filtering + Content-Based Filtering for stronger recommendations. Netflix uses a hybrid model—combining user interactions, movie genres, and user reviews. Spotify blends CF (listening history) with content-based (audio features) to create Discover Weekly. E-commerce platforms mix CF (purchase history) with product metadata. Strengths: Reduces cold start issues, improves diversity, and creates personalized experiences. Challenges: Computationally expensive and requires more engineering effort. Choosing the Right Model: Cold start problem? → Use Content-Based Filtering. Large user interaction dataset? → Leverage Collaborative Filtering. Want the best recommendations? → Go for a Hybrid Model! Building a great recommendation system is not just about algorithms—it’s about understanding users, balancing exploration vs. exploitation, and ensuring fairness & personalization. Which recommendation approach do you find most effective? Let me know in the comments below! :) #RecommendationSystems #Content #AI #DataScience #Marketing #Reach #ML #MachineLearning #Algorithms #LinkedIn

✨ If you’re starting your UX career, master interaction models. I’m talking about the UI patterns that control how users move through a system: Popups, drawers, modals, popovers, bottom sheets, tooltips, accordions, tabs, overlays. These aren’t just “components.” They’re interaction models each with a purpose, strengths, and risks. • A modal demands full attention, but overuse creates friction. • A drawer works for secondary navigation, but bury actions there and they’ll be forgotten. • Popovers are lightweight, but they disappear too quickly if misused. • Snackbars are subtle, yet critical when you need to confirm system feedback. And it doesn’t stop there. As you grow, you’ll work with navigation models (breadcrumbs, steppers, carousels, command palettes), feedback models (loaders, skeleton screens, banners, empty states), and content models (cards, chips, dropdowns, context menus). Each one is a design decision that changes how users feel and act inside your product. On large-scale systems, choosing the wrong model multiplies confusion. Good UX is not about adding more pages it’s about designing flows that reduce cognitive load and keep users oriented. 🔑 Why this matters: If you understand how models work, you’ll design experiences that scale, prevent users from getting lost, and keep interfaces predictable. That’s the difference between a project that feels seamless and one that feels overwhelming. The best UX designers aren’t just creative they’re systematic. They know why a model exists, and when to use it.

LLMs are optimized for next turn response. This results in poor Human-AI collaboration, as it doesn't help users achieve their goals or clarify intent. A new model CollabLLM is optimized for long-term collaboration. The paper "CollabLLM: From Passive Responders to Active Collaborators" by Stanford University and Microsoft researchers tests this approach to improving outcomes from LLM interaction. (link in comments) 💡 CollabLLM transforms AI from passive responders to active collaborators. Traditional LLMs focus on single-turn responses, often missing user intent and leading to inefficient conversations. CollabLLM introduces a :"Multiturn-aware reward" system, apply reinforcement fine-tuning on these rewards. This enables AI to engage in deeper, more interactive exchanges by actively uncovering user intent and guiding users toward their goals. 🔄 Multiturn-aware rewards optimize long-term collaboration. Unlike standard reinforcement learning that prioritizes immediate responses, CollabLLM uses forward sampling - simulating potential conversations - to estimate the long-term value of interactions. This approach improves interactivity by 46.3% and enhances task performance by 18.5%, making conversations more productive and user-centered. 📊 CollabLLM outperforms traditional models in complex tasks. In document editing, coding assistance, and math problem-solving, CollabLLM increases user satisfaction by 17.6% and reduces time spent by 10.4%. It ensures that AI-generated content aligns with user expectations through dynamic feedback loops. 🤝 Proactive intent discovery leads to better responses. Unlike standard LLMs that assume user needs, CollabLLM asks clarifying questions before responding, leading to more accurate and relevant answers. This results in higher-quality output and a smoother user experience. 🚀 CollabLLM generalizes well across different domains. Tested on the Abg-CoQA conversational QA benchmark, CollabLLM proactively asked clarifying questions 52.8% of the time, compared to just 15.4% for GPT-4o. This demonstrates its ability to handle ambiguous queries effectively, making it more adaptable to real-world scenarios. 🔬 Real-world studies confirm efficiency and engagement gains. A 201-person user study showed that CollabLLM-generated documents received higher quality ratings (8.50/10) and sustained higher engagement over multiple turns, unlike baseline models, which saw declining satisfaction in longer conversations. It is time to move beyond the single-step LLM responses that we have been used to, to interactions that lead to where we want to go. This is a useful advance to better human-AI collaboration. It's a critical topic, I'll be sharing a lot more on how we can get there.

Exciting research from Snap Inc.'s engineering team! Just came across their paper on Universal User Modeling (UUM) that's revolutionizing how they handle cross-domain user representations. The team at Snap has developed a framework that learns general-purpose user representations by leveraging behaviors across multiple in-app surfaces simultaneously. Rather than building separate user models for each surface (Content, Ads, Lens, etc.) and combining them post-hoc, UUM directly captures collaborative filtering signals across domains. Their approach formulates this as a cross-domain sequential recommendation problem, processing user interaction sequences of up to 5,000 events and using sliding windows of 800-length subsequences to balance computational efficiency with capturing long-range dependencies. The architecture leverages transformer-based self-attention mechanisms to model these sequences, with a clever design that projects feature vectors from different domains into a shared latent space before applying multi-head attention layers. The results are impressive! After successful A/B testing, UUM has been deployed in production with significant gains: - 2.78% increase in Long-form Video Open Rate - 19.2% increase in Long-form Video View Time - 1.76% increase in Lens play time - 0.87% increase in Notification Open Rate They're also exploring advanced modeling techniques like domain-specific encoders and self-attention with information bottlenecks to address the challenges of imbalanced cross-domain data. This work demonstrates how sophisticated user modeling can drive substantial engagement improvements across multiple recommendation surfaces within a large-scale social platform.

🚀 Google 𝗝𝘂𝘀𝘁 𝗥𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝗮 𝗡𝗲𝘄 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻𝘀 𝗔𝗣𝗜 𝗳𝗼𝗿 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗜 𝗔𝗽𝗽𝘀 For years, most LLM integrations were essentially stateless completions: you send a prompt, get a response, then resend the entire conversation history on the next call. Any memory, tool state, retries, or orchestration logic lived entirely in your application code. The 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻𝘀 𝗔𝗣𝗜 flips that model by making the interaction itself a first-class primitive - with optional server-side context, tool state, and long-running execution managed for you. 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁’𝘀 𝗻𝗲𝘄 — 𝗮𝗻𝗱 𝘄𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀:  • 🧠 One endpoint for models and agents A single REST endpoint (/interactions) lets you invoke either a model (Gemini 2.5 / Gemini 3 previews) or a specialized agent, starting with Gemini Deep Research Preview. Same interface.  • 🗂️ Optional server-side state Instead of resending megabytes of prior messages, you can pass a previous_interaction_id and let Google retain full conversational context, tool calls, and results on the server.  • ⚙️ A composable, inspectable interaction data model This isn’t just a list of messages. Interactions are structured as interleaved thoughts, tool calls, tool results, streaming outputs, and execution states like requires_action and in_progress. This is exactly what you want when debugging real agent workflows.  • ⏳ Background execution for long-horizon tasks With background=true, you can start an interaction, disconnect, and poll later. This unlocks workflows that normally break traditional HTTP patterns - research loops, multi-step planning, batch tool execution, and long-running analysis.  • 🔌 Native Remote MCP tool support Models can call Model Context Protocol (MCP) servers as tools. This is a big step toward interoperable, plug-and-play tool ecosystems across AI platforms.  • 💸 Cost and latency wins via implicit caching Because interaction history lives server-side, Google can improve cache hit rates and reduce repeated context uploads - which can translate into meaningful cost savings at scale.  • 🔐 Storage controls and retention options By default, store=true enables state, background execution, and observability. You can opt out with store=false if you want ephemeral usage — with the trade-off that background runs and interaction continuity are disabled.    • 👩💻 Practical note: SDK support is already here The Interactions API is available today in Python (google-genai) and JavaScript (@google/genai), so you can adopt it without changing your existing stack. If you’re building agentic AI applications, this feels like the natural evolution beyond chat completions - closer to a managed, stateful runtime for AI. 🔗 https://lnkd.in/gmJ44XcU #GoogleAI #Gemini #AIEngineering #AgenticAI

Ever wonder why some search systems understand your queries better than others? The key is ✨ interaction. ✨ Late interaction retrieval models are changing how we find and retrieve information from large document collections. So what is it and why does it matter? 🤨 What is late interaction? Late interaction is a clever approach that preserves fine-grained meaning by comparing individual parts of text (like words or phrases) rather than entire documents at once. Think of it this way: • Traditional models create ONE vector for your entire document • Late interaction keeps vectors for EACH token (word part) • When searching, it compares each query token with all document tokens • This preserves contextual relationships that would otherwise be lost ❗️ Why is late interaction important? 1. It enables semantically rich interactions across different data types  2. It improves both accuracy AND scalability compared to other retrieval methods  3. It's particularly valuable for RAG pipelines where precision matters  4. It offers better explainability - you can see which tokens matched 👨👩👧 The ColBERT family: Three models to know 1. ColBERT: The original text-only model based on BERT   • Uses late interaction instead of BERT's all-to-all interaction    • Great for text-based retrieval requiring high accuracy 2. ColPali: Multimodal model using PaliGemma Vision LLM   • Processes documents as images (perfect for PDFs)    • ~3B parameters with 128-dimensional embeddings 3. ColQwen: Similar to ColPali but uses Qwen2 Vision LLM   • Smaller model (~2B parameters)    • Licensed under apache2.0 (more permissive) The best part? While traditional models might miss contextual nuances, these late interaction models preserve the relationships between words and concepts, giving you more accurate results. For more on how this all fits together, check out the latest blog post by Leonie Monigatti, Danny Williams, and Victoria Slocum: https://lnkd.in/gvKtKxK2

For the last month, we've been running a fun experiment on the Buffer blog. Alongside the usual social share buttons (Facebook, LinkedIn, X, Threads, etc.), we added two new options: Share to ChatGPT and Share to Claude. Tap them, and the article opens in your LLM with a prefilled prompt to summarize the piece for you. After one month, the result surprised us: LLMs combined are already driving more share-button interactions than LinkedIn, X, reddit, and Threads. A tiny UI tweak, but a big signal. People are shifting from sharing content (and dare I say even reading it) to processing it. They want quick summaries, personalized explanations, and a smoother way to consume information. For marketers and content teams, a few principles matter: 1. Structure your content for LLM comprehension. Clear headings, strong metadata, tight arguments, and helpful explanations lead to better summaries and more accurate interpretations. 2. Optimize for the “moment of help”. If users consistently run your content through LLMs, design for that behaviour. Make it easier, faster, and more valuable. 3. Treat LLMs as real distribution channels. Not social distribution, but interpretation distribution. Visibility now comes from being easy for models to digest and relay. This is a new layer of the internet’s reading experience, and we are only at the start.