Add TIGER model for Semantic ID recommendation

[hieuddo]

Description

This PR integrates TIGER (Recommender Systems with Generative Retrieval) into cornac, along with dataset utilities to support its content-embedding requirements.

Key Changes

• Added load_text() to cornac/datasets/amazon_review.py to parse product metadata (title, price, brand, categories, and description).
• Added unit tests for metadata parsing in tests/cornac/datasets/test_amazon_review.py.
• TIGER model implementation, using NextItemRecommender interface
  • Included default configurations (paischer_config.py and grid_config.py) matching the settings used in popular benchmarks, to replicate results from the original paper.
• Added examples/tiger_example.py with training pipeline on the Amazon Review (beauty) dataset

Related Issues

Checklist:

• I have added tests.
• I have updated the documentation accordingly.
• I have updated README.md (if you are adding a new model).
• I have updated examples/README.md (if you are adding a new example).
• I have updated datasets/README.md (if you are adding a new dataset).