Smart AI‑Powered Push Copy: From Templates to Sampling Strategies

Content Assets Overview

In the Internet, content assets include text , images and video . Each format has unique value: text conveys precise information, images attract attention through visual impact, and video combines sight and sound for immersive experiences.

Value of High‑Quality Content

Long‑term accumulation and efficient management of high‑quality content are crucial for enterprise growth. Quality content boosts user engagement, improves SEO, and can be turned into a powerful marketing tool that generates stable traffic and conversion.

Push Notification Copy Challenges

High manual creation cost

Lack of copy diversity

Insufficient management and reuse of existing copy

Low update frequency after launch

Anti‑cheat considerations

Push notifications aim to increase user activity, but low‑quality copy can harm user experience and even lead to permission revocation.

Intelligent Copy System Solution

An intelligent copy system is built to manage and produce copy, offering template generation and keyword generation modes. It automates creation, recommendation, processing, distribution, and feedback, enhancing copy richness and update speed to improve click‑through rates.

Template Generation

NLP extracts basic templates containing common attributes such as time, season, audience, location, activity, and benefit points (e.g., "A wave of #red‑packet# arrives, #audience# enjoys healthy eating..."). These templates are mined through content aggregation techniques.

Keyword Generation with Transformers

Pre‑trained models (BERT, UniLM, T5, GPT, etc.) are fine‑tuned to generate copy from keywords. These models share the Transformer architecture, with GPT‑2 using a stacked decoder and BERT using an encoder.

Model Architecture

For implementation, refer to HuggingFace Transformers .

Siamese BERT Example

<code>import torch</code><code>from transformers import BertTokenizer, BertModel</code><code>import torch.nn.functional as F</code><code>import numpy as np</code><code># Define Siamese model</code><code>class SiameseBERT(torch.nn.Module):</code><code>    def __init__(self):</code><code>        super(SiameseBERT, self).__init__()</code><code>        self.bert = BertModel.from_pretrained("bert-base-uncased")</code><code>        self.fc = torch.nn.Linear(self.bert.config.hidden_size, 1)</code><code>    def forward_once(self, input_ids, attention_mask):</code><code>        output = self.bert(input_ids=input_ids, attention_mask=attention_mask)</code><code>        pooled_output = output.pooler_output</code><code>        return pooled_output</code><code>    def forward(self, input_ids1, attention_mask1, input_ids2, attention_mask2):</code><code>        feature1 = self.forward_once(input_ids1, attention_mask1)</code><code>        feature2 = self.forward_once(input_ids2, attention_mask2)</code><code>        cosine_similarity = F.cosine_similarity(feature1, feature2)</code><code>        return cosine_similarity</code><code># Load tokenizer</code><code>tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")</code><code>sentence1 = "The impact of eventual consistency on data consistency."</code><code>sentence2 = "How does eventual consistency affect data consistency?"</code><code>inputs1 = tokenizer(sentence1, return_tensors="pt", padding=True, truncation=True)</code><code>inputs2 = tokenizer(sentence2, return_tensors="pt", padding=True, truncation=True)</code><code># Initialize model</code><code>model = SiameseBERT()</code><code>with torch.no_grad():</code><code>    similarity = model(inputs1['input_ids'], inputs1['attention_mask'], inputs2['input_ids'], inputs2['attention_mask'])</code><code>print(f"Cosine Similarity between the two sentences: {similarity.item()}")</code>

Output:

Cosine Similarity between the two sentences: 0.987

BLEU Evaluation Example

BLEU measures n‑gram overlap between generated copy and a reference. Example:

<code>Reference: 您的可用额度已提至${credit_lmt_new}元，别忘了使用！前往APP申请提现>${short_url}拒收请回复R</code><code>Candidate: 额度已临时提至${credit_lmt_new}元，别忘了去APP提现，审批通过最快1分钟放款${short_url}拒收请回复R</code>

Calculated BLEU ≈ 0.8513 using equal weights for 1‑gram to 4‑gram.

Semantic Similarity with Siamese Networks

Two identical networks process input texts; their outputs are concatenated to compute similarity, allowing semantically related copies to be identified.

Sampling Strategies

Greedy Search

Selects the highest‑probability token at each step. Fast but may miss globally optimal sequences.

Beam Search

Keeps the top‑k candidates at each step, balancing quality and computational cost.

Top‑K Sampling

Samples from the K most probable tokens, introducing randomness while limiting low‑probability words.

Top‑P (Nucleus) Sampling

Samples from the smallest set of tokens whose cumulative probability exceeds P, dynamically adjusting candidate size.

Temperature Scaling

Adjusts the sharpness of the probability distribution; lower T makes the model more deterministic, higher T increases diversity.

Each method includes pros and cons and illustrative examples.

Prompt Engineering Tips

Follow best practices from Google Cloud and OpenAI (links provided).

Model Fine‑Tuning Choices

LoRA (Low‑Rank Adaptation) and full fine‑tuning offer trade‑offs between parameter efficiency and performance; larger models generally achieve better results.