Opening Chat

Recently, I noticed something fascinating while browsing Taobao - the "Recommended for You" section on the homepage always precisely hits my sweet spot. For instance, after I briefly watched Li Jiaqi's livestream featuring a new YSL lipstick shade, the next day Taobao pushed numerous related reviews and lipsticks in similar shades. This isn't just happening on Taobao - I've noticed that platforms like TikTok and Xiaohongshu are understanding me better too. As I scroll, content from beauty bloggers, fashion sharing, and food reviews that interest me keeps popping up, as if they've found my perfect match.

To be honest, I was scared at first, feeling like I was being monitored. Later I realized this was all AI algorithms quietly observing our behavior patterns and providing recommendations best suited for us. As someone born after 1995, I think these smart recommendations have brought a lot of convenience to life, but they've also made me think. Today I'd like to share some of my observations and thoughts about AI recommendation systems.

Algorithm Decoded

When it comes to AI recommendation algorithms, the most basic is collaborative filtering. That might sound technical, but it's essentially the principle of "birds of a feather flock together." It's like how my bestie and I often shop together, and our aesthetic and shopping preferences are super similar. If she buys a dress, there's a high chance I'll like it too - that's the simplest manifestation of collaborative filtering.

However, today's AI recommendation systems have far surpassed this level. They're like attentive personal assistants, recording all our behaviors across different platforms. For example, when I search for "white sneakers" on Taobao, click through several pairs, compare prices, and linger particularly long on one pair, the AI will assume I'm especially interested in sneakers of that price range and style.

What's more impressive is that it also considers our social media behavior. If I follow Wang Yibo on Weibo and frequently like his outfit posts, Taobao might start recommending similar street-style clothing. I remember once, after watching a pet video on Weibo, when I opened Taobao, the homepage started recommending all sorts of cat food and cat beds, even though I didn't even own a cat. The correlation really amazed me.

Application Cases

TikTok can be said to have mastered personalized recommendations to the extreme. They reportedly process over 10 billion user behavior data points daily - a mind-boggling number. But it's this massive data analysis that allows TikTok to accurately know what content we like.

I've experienced this firsthand. When I first started using TikTok, there was all kinds of content, but I found myself spending more time on food videos and travel vlogs, often liking and saving these types of videos. Gradually, my homepage became a collection of food and travel content, filled with high-quality content about trending restaurants and hidden tourist spots.

Taobao's "Recommended for You" is even more impressive. Last year during Singles' Day, I just casually looked at a few projectors, and Taobao immediately started pushing comparative reviews of cost-effective projectors, user experience sharing, and even suggested usage scenarios. From home use to camping, business to movie watching, the analysis of various application scenarios was incredibly detailed. I ended up buying one and am quite satisfied with it now.

Alibaba once released data showing that AI recommendations improved user shopping conversion rates by 20%. What does this mean? It means AI not only accurately predicts our shopping needs but also significantly influences our purchasing decisions. These recommendation systems generate hundreds of billions in transactions annually for the platform - truly impressive numbers.

Technical Upgrades

Current AI recommendation systems have started applying more advanced deep learning technologies. For example, the GPT model you often hear about is being applied to recommendation systems. This technology doesn't just look at our behavioral data but can understand the meaning of content itself.

For example, if I'm watching a video about "steamed bass," traditional algorithms might just recommend more fish dishes. But with new deep learning algorithms, they can understand that this dish is characterized by being "light" and "healthy," so they'll recommend more dishes with similar characteristics, like poached shrimp or cucumber salad.

It's said that recommendation systems using deep learning have improved accuracy by 35% compared to traditional algorithms. This improvement is remarkable, which explains why we can always find content we like while scrolling. Sometimes it's almost scary how accurate it is, making you wonder if the AI has become sentient.

Reflections and Insights

While AI recommendations have brought many conveniences to our lives, I've recently started pondering a question: does this highly personalized recommendation somehow limit our opportunities to encounter new things?

Taking myself as an example, because I often watch food videos, my TikTok homepage is now basically all food bloggers. While every video appeals to me, I've slowly realized that I might be trapped in this food bubble, missing out on many other interesting content.

The other day, while chatting with a friend, she shared about a popular science blogger, and I realized there's actually so much in-depth knowledge content on TikTok. But because of the algorithm, these videos never appeared in my homepage recommendations.

This is the so-called "information cocoon" phenomenon. To keep us engaged, algorithms continuously reinforce our existing interests by recommending similar content. While this ensures every piece of content is to our taste, in the long run, our perspectives might become increasingly narrow.

Future Outlook

Looking ahead, I believe personalized recommendations still have huge development potential. Especially with the development of metaverse and AR/VR technologies, recommendation systems might have more interesting application scenarios.

Imagine in the future, in virtual fitting rooms, AI could not only recommend suitable clothes based on your body data but also suggest the most flattering makeup based on your skin tone and face shape. It might even predict your needs for different occasions and prepare complete outfit solutions in advance.

It's said that by 2025, the market size of AI recommendations in e-commerce will exceed 100 billion USD. What does this number imply? It means our future shopping experience will become more intelligent and personalized, possibly transforming even the act of shopping into a completely new experience.

Concluding Remarks

Overall, AI recommendation systems are like mirrors reflecting our interests and habits. They can make our lives more convenient and help us find truly interesting content in this age of information overload. However, we must maintain our ability to think independently and actively seek out different information and perspectives.

After all, no matter how powerful AI becomes, it's just a tool, and the real power of choice remains in our hands. We can enjoy the convenience it brings but shouldn't completely rely on it. Maintaining curiosity and actively exploring new things is how we can live more brilliantly in the AI era.