WePlay
WePlay is a Progressive Web App (PWA) that delivers personalized video recommendations by fetching user preferences from Spotify. Based on these preferences, the app recommends videos from platforms like TikTok and Instagram, offering seamless integration and allowing users to explore more content on those platforms. If users like an artist, they can easily navigate to Instagram or TikTok from within WePlay. This unique platform positions itself as a competitor to industry giants like YouTube and Instagram in the PWA space.
The app needed to extract and process user preferences from Spotify, specifically focusing on sound parameters such as tempo, energy, and genre to accurately recommend related videos.
To provide a personalized experience, WePlay had to dynamically fetch video content from TikTok and Instagram based on users’ Spotify likes.
As a PWA, WePlay required offline storage capabilities to cache essential data, ensuring users could interact with the app even in low-connectivity areas.
Users should be able to navigate smoothly from WePlay to TikTok or Instagram if they like the recommended artist or content.
A custom backend data pipeline was developed to fetch and analyze user music preferences on Spotify. Data extraction was performed using sound parameters, allowing the app to make highly accurate video recommendations based on the user’s liked music.
WePlay dynamically pulls video content from TikTok and Instagram, creating a personalized video feed for users. The backend performs real-time data extraction, matching sound profiles with relevant content.
The PWA, built with Vanilla JavaScript, leverages the browser’s offline storage capabilities to ensure essential data and previous user interactions are cached. This allows users to enjoy uninterrupted browsing even when their internet connection is unstable.
Smooth integration was achieved, allowing users to jump directly from the app to TikTok or Instagram to follow or explore more about the artists they discover through WePlay.
WePlay successfully bridges the gap between music and social video platforms, providing users with curated video recommendations based on their music tastes. This unique approach kept users engaged with personalized content.
Users appreciated the easy navigation from the app to TikTok and Instagram, allowing them to follow their favorite artists instantly without leaving the app environment.
With smooth offline capabilities and real-time video recommendations, WePlay stands out in a competitive space alongside YouTube and Instagram PWAs.
Built entirely in Vanilla JavaScript, the app was optimized for speed and offline functionality, leveraging the latest browser storage APIs.
A robust data pipeline was developed to handle Spotify data extraction and real-time video recommendation. Advanced data analytics techniques were employed to map sound parameters from Spotify to relevant videos on TikTok and Instagram.
Capital Compute successfully brought WePlay’s vision to life, crafting a PWA that merges user music preferences with social video recommendations. Utilizing Vanilla JavaScript and building a powerful backend data pipeline, Capital Compute created a competitive and engaging platform, proving that the right blend of simplicity and intelligence can rival even the biggest names in the PWA world.
This case study highlights how Capital Compute built WePlay, a unique fusion of music and social video recommendations, powered by data extraction and seamless social media integration.
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