Language Representations Can be What Recommenders Need: Findings and Potentials

核心思想

Linear

• 本文探索 LLM embeddings 的潜力, 方法极为简单:

  1. 将 title 的 embeddings 作为对应 item 的表示 (记为 $\bm{z}_i$), 其过程如下 (注意到, 实际上, 是将 decoder-only 的 next-token embedding 作为 item 的表示, 而不是平均之类的方式):

  # model_path = "meta-llama/Meta-Llama-3-8B"
  model_path = "meta-llama/Llama-2-7b-hf"
  # model_path = "meta-llama/Llama-2-13b-hf"
  
  tokenizer = AutoTokenizer.from_pretrained(model_path, device_map = 'auto')
  model = AutoModelForCausalLM.from_pretrained(model_path, device_map = 'auto')
  
  tokenizer.padding_side = "left"
  tokenizer.pad_token = tokenizer.eos_token
  
  
  item_df = pd.read_csv('raw_data/items_filtered.csv', index_col=0)
  item_df.rename(columns={'title': 'item_name'}, inplace=True)
  
  batch_size = 64
  
  for i in tqdm(range(0, len(item_df), batch_size)):
      # print(i)
      item_names = item_df['item_name'][i:i+batch_size]
      # 生成输出
      inputs = tokenizer(item_names.tolist(), return_tensors="pt", padding=True, truncation=True, max_length=128).to(model.device)
      with torch.no_grad():
          output = model(**inputs, output_hidden_states=True)
      seq_embeds = output.hidden_states[-1][:, -1, :].detach().cpu().numpy()
      # break
      if i == 0:
          item_llama_embeds = seq_embeds
      else:
          item_llama_embeds = np.concatenate((item_llama_embeds, seq_embeds), axis=0)
  

  2. 然后 user 表示为其所交互过的商品的平均:

     $$ \bm{z}_u = \frac{1}{|\mathcal{N}_u|} \sum_{i \in \mathcal{N}_u} \bm{z}_i. $$

  3. 然后通过共享的 projector 得到:

     $$ \bm{e}_u = \mathbf{W} \bm{z}_u, \quad \bm{e}_i = \mathbf{W} \bm{z}_i. $$

  4. score 以 cosine similarity 来计算:

     $$ s_{ui} = \frac{\bm{e}_u^T \bm{e}_i}{\|\bm{e}_u\| \| \bm{e}_i\|}. $$

  5. 通过 InfoNCE 进行训练 (温度参数 $\tau \approx 0.15$), 注意, $\bm{z}$ 是固定的.

• 下面是 linear projector 下的结果:

• 有一些很有趣的点:
  1. BERT, RoBERTa 等传统的 encoder 模型反而会取得很差的效果, 这个和之前的一些经验是相符的;
  2. 随着 LLM 的能力的增强, 效果也越来越好了, 很容易就能够超过 ID-based 的方法.

AlphaRec

• AlphaRec 的做法:

  1. linear projector 进行了一点点修改
  $$ \bm{e} = \mathbf{W}_2 \text{LeakyReLU} \big( \mathbf{W}_1 \bm{z} \big). $$
  2. 加上 LightGCN:
  $$ \mathbf{F} = \sum_{l=0}^{L+1} \mathbf{\tilde{A}}^l \mathbf{E}, $$

  这里 $\mathbf{E}$ 是所有的 $\bm{e}_u, \bm{e}_i$, $\mathbf{\tilde{A}}$ 是对应的 (对称) normalized 邻接矩阵.

• 如上图所示, AlphaRec 如此简单的方法就能够取得非常惊人的效果 (而且效率很高).

其它潜力

• 可以作为初始化而加速和提高传统模型

• 非常强的 zero-shot 能力, 甚至能够媲美传统模型 full-training 的效果

• intention-aware, 用户可以输入自己的意图, 同样通过 LLM 得到 embedding, 加权平均得到 user 的表示:

  $$ \bm{\tilde{e}}_u = (1 - \alpha) \bm{e}_u + \alpha \bm{e}_u^{Intention}. $$

个人测试

• 参考作者的代码, 自己实现了 AlphaRec, 在 Movies 进行了一下测试.

Movies

# AlphaRec
root: ../../data
dataset: AmazonMovies_Alpha
tasktag: Matching

embedding_dim: 64
num_layers: 2

epochs: 500
batch_size: 4096
optimizer: adam
lr: 5.e-4
weight_decay: 1.e-6

tau: 0.15
num_negs: 256
projector: mlp

monitors: [LOSS, Recall@1, Recall@10, Recall@20, HitRate@10, HitRate@20, NDCG@10, NDCG@20]
which4best: Recall@20

# LightGCN
root: ../../data
dataset: AmazonMovies_Alpha
tasktag: Matching

embedding_dim: 64
num_layers: 2

epochs: 1000
batch_size: 2048
optimizer: adam
lr: 1.e-3
weight_decay: 1.e-3

monitors: [LOSS, Recall@1, Recall@10, Recall@20, HitRate@10, HitRate@20, NDCG@10, NDCG@20]
which4best: NDCG@20

Beauty

# LightGCN

root: ../../data
dataset: Amazon2014Beauty_550811_ROU
tasktag: Matching

embedding_dim: 64
num_layers: 3

epochs: 1000
batch_size: 2048
optimizer: adam
lr: 1.e-3
weight_decay: 1.e-3

monitors: [LOSS, Recall@1, Recall@10, Recall@20, NDCG@10, NDCG@20]
which4best: NDCG@20

# LightGCN + InfoNCE

root: ../../data
dataset: Amazon2014Beauty_550811_ROU
tasktag: Matching

embedding_dim: 64
num_layers: 3
num_negs: 256
tau: 0.15

epochs: 500
batch_size: 2048
optimizer: adam
lr: 5.e-4
weight_decay: 1.e-2

monitors: [LOSS, Recall@1, Recall@10, Recall@20, NDCG@10, NDCG@20]
which4best: NDCG@20

root: ../../data
dataset: Amazon2014Beauty_550811_ROU
tasktag: Matching

embedding_dim: 64
num_layers: 3
tfile: llama2_7b_title.pkl # llama2_13b_title.pkl

epochs: 500
batch_size: 2048
optimizer: adam
lr: 5.e-4
weight_decay: 0.

tau: 0.15
num_negs: 256
projector: mlp

monitors: [LOSS, Recall@1, Recall@10, Recall@20, NDCG@10, NDCG@20]
which4best: NDCG@20

Baby

# LightGCN
root: ../../data
dataset: Amazon2014Baby_550811_RAU
tasktag: Matching

embedding_dim: 64
# num_layers: 3
# num_negs: 256
# tau: 0.25

epochs: 100
batch_size: 2048
optimizer: adam
lr: 1.e-3
weight_decay: 5.e-3

monitors: [LOSS, Recall@1, Recall@10, Recall@20, NDCG@10, NDCG@20]
which4best: NDCG@20

# LightGCN + InfoNCE
root: ../../data
dataset: Amazon2014Baby_550811_RAU
tasktag: Matching

embedding_dim: 64
num_layers: 3
num_negs: 256
tau: 0.25

epochs: 500
batch_size: 2048
optimizer: adam
lr: 1.e-3
weight_decay: 1.e-3

monitors: [LOSS, Recall@1, Recall@10, Recall@20, NDCG@10, NDCG@20]
which4best: NDCG@20

# AlphaRec
root: ../../data
dataset: Amazon2014Baby_550811_RAU
tasktag: Matching

embedding_dim: 64
num_layers: 3
tfile: llama2_7b_title.pkl

epochs: 500
batch_size: 2048
optimizer: adam
lr: 5.e-4
weight_decay: 0.

tau: 0.25
num_negs: 256
projector: mlp

monitors: [LOSS, Recall@1, Recall@10, Recall@20, NDCG@10, NDCG@20]
which4best: NDCG@20

• 从自己处理的数据集来看, LLM 并没有比传统的 BERT 好上太多.

• 不过 Next-token embedding 的能力还是比较令人惊讶的, 这方面的原因可能可以通过 这篇文章 解释.

参考文献

1. Sheng L., Zhang A., Zhang Y., Chen Y., Wang X., and Chua T. Language Representations Can be What Recommenders Need: Findings and Potentials ICLR, 2025. [PDF] [Code]