Recommender System Large Language Modeling: LLM Training Data Agent Acquisition

When recommender system meets big model, how to glean data to be safe?

The brothers who engage in recommender system recently have a headache - the amount of data required for large language model training is like a bottomless pit, directly to the website hard to climb, minutes to be blocked IP. last month a friend to do the movie recommendation model, just climbed 3,000 comments on the website to be pulled black, so angry that he almost fell on the keyboard.

How did proxy IPs become a lifesaver for data collection?

Imagine you are a supermarket buyer, if you wear the same clothes every day to go to the goods, the security guard must be suspicious. Proxy IP is the reason, every time you collect data to change a "vest", the site will not recognize the same "buyer" in the work.

Here's one.Fatal Misconceptions: A lot of people think they can just find a free proxy and use it. In fact, those public proxies have long been recorded in the small book by major websites, using them is tantamount to shooting oneself in the foot. Reliable commercial proxy services like ipipgo, holding hundreds of thousands ofexclusive IP poolThe fact that each IP has a track of real users is what makes it possible to get away with "work clothes".

Hands-on with ipipgo to build a collection pipeline

Here's a real-world example for Python (don't be afraid to read the code, just follow along):

import requests
from itertools import cycle

 List of proxies provided by ipipgo (remember to replace them with your own account)
proxy_list = [
    '12.34.56.78:8888',
    '98.76.54.32:8888', ...
     ... More IPs
]

proxy_pool = cycle(proxy_list)

for page in range(1, 101):
    try.
         Pick a random proxy each time
        current_proxy = next(proxy_pool)
        response = requests.get(
            f'https://example.com/reviews?page={page}',
            proxies={'http': current_proxy},
            timeout=10
        )
         Here the collected data is processed...
    except Exception as e.
        print(f "Failed to capture page {page}, try next IP")

Here's the kicker.: Remember to setrequest interval! Even if you change the IP, if you send 100 requests per second, a fool knows that the machine is operating. It is recommended to use a random delay, like this:

import time
import random

 Wait 2-5 seconds for a randomized time
time.sleep(random.uniform(2, 5))

QA Time: The Most Common Pitfalls Newbies Step Into

Q: Why is it still blocked after using a proxy?
A: 80% of the IP quality is not good. Some agents in the market will sell the same IP to multiple people, this kind of shared IP has long been blacklisted. Choose ipipgo which providesExclusive Agents, each IP is for you alone.

Q: Do I need to maintain my own IP pool?
A: Never! I've seen people build their own proxy servers and it ends up costing more to maintain than buying the service. Leave the professional stuff to service providers like ipipgo who haveAutomatic IP replacementrespond in singingsurvival testingMechanisms.

The tawdry operation in the collection of the real world

There is a client who does e-commerce referrals, they found that using a fixed User-Agent is easy to be recognized. Later with ipipgo'sGeographic orientationFunction, the Beijing IP with Android UA, Shanghai IP with Apple UA, the collection success rate is directly doubled.

And here's another trick: add to the capture scriptReal-life operation simulation. For example, visit the home page first and click on a few random items before finally jumping to the target page. It takes a few more lines of code, but with ipipgo's high-speed proxy, the site can't tell if it's a real person or a machine.

Why do old birds go with ipipgo?

Name a few hard indicators that you care about:

• Survival rate 95%+Their IP has an automatic resurrection mechanism
• Millisecond response: More than 3 times faster than a normal agent
• nationwide coverage: 200+ city nodes to choose from

bottom line is this.after-sales serviceThe last time we had a collection task suddenly failed, ipipgo's technical guy gave a new scheduling program in 10 minutes, this kind of response speed is really rare in the industry.

Finally say a big truth: engage in data collection is like fighting guerrilla warfare, both to hit accurately and to hide well. Choosing the right agent service provider can really make you take a detour for three years less.