Add DiffGRM generative recommender - #718

Description

This PR integrates DiffGRM (Diffusion-based Generative Recommendation Model) into cornac through the NextItemRecommender interface.

The official repository at commit ad7b971c7e525e9fea6fb8e362a5c49dccb2473c did not contain a root repository license, so we tried to implement DiffGRM without copying the code directly (ours is Apache-2.0).

Key changes

• Added a native DiffGRM model using Cornac's NextItemRecommender and FeatureModality interfaces.
• Added configurations for Amazon Beauty, Sports, and Toys.
• Added detailed documentation covering configuration, reproduction results.

Verification and reproducibility

The evaluation uses Amazon Sports 2014 5-core, chronological leave-one-out, max_len=50, the frozen released Semantic-ID table, paper beam size 128, and seeds 2024-2026. Checkpoints are selected using the SID-level validation objective 0.8 * NDCG@10 + 0.2 * Recall@10.

The three-seed mean is 5.19%-7.29% below the paper across the four metrics. Seed 2025, which also has the highest declared validation score, is within 0.46%-0.91% of every paper value. The paper likely used results from one seed instead of average of multiple runs (see https://github.com/hieuddo/cornac/blob/c0d200c560de6d3708b329395a6f327d61963e39/cornac/models/diffgrm/README.md#reproduction-path)

This verification shows the Cornac implementation achieves reasonably comparable results.

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).