GenLI generates diverse target-independent interest distributions via an IGM, retrieves behaviors with O(1) lookup in BRM, and fuses via IFM gating to balance accuracy and efficiency in CTR prediction.
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DIG unifies ranking and retrieval by training the tokenizer jointly inside a ranking model, producing improved models for both from a single run.
OneRec unifies retrieval and ranking in a generative recommender using session-wise decoding and iterative DPO-based preference alignment, achieving real-world gains on Kuaishou.
citing papers explorer
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Generative Long-term User Interest Modeling for Click-Through Rate Prediction
GenLI generates diverse target-independent interest distributions via an IGM, retrieves behaviors with O(1) lookup in BRM, and fuses via IFM gating to balance accuracy and efficiency in CTR prediction.
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Discrimination Is Generation: Unifying Ranking and Retrieval from a Tokenizer Perspective
DIG unifies ranking and retrieval by training the tokenizer jointly inside a ranking model, producing improved models for both from a single run.
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OneRec: Unifying Retrieve and Rank with Generative Recommender and Iterative Preference Alignment
OneRec unifies retrieval and ranking in a generative recommender using session-wise decoding and iterative DPO-based preference alignment, achieving real-world gains on Kuaishou.