# Predicting User Interests Based on Who Looks at Whose Profile > A method for predicting what a user might be interested in by analyzing the web of connections created when people view each other's social media profile pages. - **Patent:** US 8311950 - **Original title:** Detecting content on a social network using browsing patterns - **Owner:** Google LLC - **Granted:** 2012 - **Status:** Active - **Times cited:** 73 - **Field:** software, ai_ml, ecommerce ## What it does This patent describes a way to map social network activity by tracking who visits whose profile page. It creates a graph where nodes represent users and directional links represent a user viewing another user's profile. If a user is known to like a specific topic, the system assigns them a score and then propagates that score to other users who have viewed the same profiles. This allows the system to calculate a likelihood score for other users, suggesting they might also be interested in that same topic based on their browsing behavior. ## What it does NOT cover - Does not cover analyzing content based on text keywords or hashtags within a user's own posts. - Does not cover predicting interests based on direct social connections like 'friends' or 'followers'. - Does not cover real-time tracking of mouse movements or dwell time on a specific image. - Does not cover interest prediction based on external search engine queries. ## The clever bit The system treats the act of 'viewing a profile' as a signal of shared interest, effectively using the social graph as a proxy for taste, even if the users never interact directly. ## Real-world examples 1. Targeted advertising on social media platforms 2. Content recommendation feeds 3. Suggested user or interest discovery features ## Why it matters This technology is a foundation for interest-based recommendation engines. By using behavioral data—who looks at what—rather than just profile data, companies can build more accurate models of user preferences. It was filed during the early growth of social networks, when platforms were moving from simple directories to complex, data-driven advertising engines. ## Frequently asked questions ### What does Predicting User Interests Based on Who Looks at Whose Profile cover? A method for predicting what a user might be interested in by analyzing the web of connections created when people view each other's social media profile pages. ### Who owns patent US 8311950? Google LLC owns this patent, granted in 2012. ### When does this patent expire? This patent is expected to expire on October 1, 2029, when the invention enters the public domain. ### What is patent US 8311950 cited by? This patent has been cited by 73 later patents that build on its ideas. ### What problem does this patent solve? This technology is a foundation for interest-based recommendation engines. By using behavioral data—who looks at what—rather than just profile data, companies can build more accurate models of user preferences. It was filed during the early growth of social networks, when platforms were moving from simple directories to complex, data-driven advertising engines. ### What does this patent NOT cover? Does not cover analyzing content based on text keywords or hashtags within a user's own posts. **Full plain-English explainer:** https://patentbrief.org/patent/us/8311950/detecting-content-on-a-social-network-using-browsing-patterns **Original patent:** https://patents.google.com/patent/US8311950 --- _Source: PatentBrief — https://patentbrief.org. Patent facts are from public records; the plain-English explanation is PatentBrief's._ ## Related patents Semantically similar inventions in the PatentBrief corpus: - [How Facebook Uses Deep Learning to Predict What You Might Like](https://patentbrief.org/patent/us/10402750/automl-neural-architecture-search) — A method for training AI models to recommend new content by comparing a user's past interactions with unseen items in a social network. - [How Facebook Ranks Search Results Based on Your Friends' Activity](https://patentbrief.org/patent/us/8914392/facebook-events) — A method for ranking search results by prioritizing links that your social network friends have clicked on previously. - [How Eventbrite Recommends Events Based on Your Social Network](https://patentbrief.org/patent/us/8700540/facebook-messenger) — A system that suggests events to you by analyzing your social media connections and your past attendance history to see what your friends are doing. - [How Facebook's News Feed Picks Stories You'll Like](https://patentbrief.org/patent/us/8171128/facebook-social-graph) — Facebook's 2012 patent explains how it creates a personalized news feed by showing stories about friends' actions, ordered by your interest, and updating it based on what you click. - [How Software Detects What You Want Based on Your Social Media Posts](https://patentbrief.org/patent/us/8521818/facebook-share-button) — A system that reads your social media posts to figure out your intent, then automatically serves ads or updates your profile based on how likely you are to actually buy or do something.