具有生成檢索功能的推薦系統

摘要

現代推薦系統利用大規模檢索模型，包含兩個階段：首先訓練雙編碼器模型，將查詢和候選項目嵌入到相同的空間中，然後進行近似最近鄰（ANN）搜索，以根據查詢的嵌入選擇頂級候選項目。在本文中，我們提出一種新的單階段範式：一種生成式檢索模型，它自回歸地解碼目標候選項目的標識符號。為了做到這一點，我們不是為每個項目分配隨機生成的原子ID，而是生成語義ID：每個項目的一個具有語義意義的代碼詞元組，作為其唯一標識符號。我們使用一種稱為RQ-VAE的階層方法來生成這些代碼詞。一旦我們為所有項目生成了語義ID，就會訓練一個基於Transformer的序列到序列模型，來預測下一個項目的語義ID。由於這個模型以自回歸方式直接預測識別下一個項目的代碼詞元組，因此可以被視為一種生成式檢索模型。我們展示了在這種新範式下訓練的推薦系統改善了在Amazon數據集上當前最先進模型所取得的結果。此外，我們證明，序列到序列模型結合階層語義ID提供更好的泛化性，從而改善了冷啟動項目的推薦檢索。

English

Modern recommender systems leverage large-scale retrieval models consisting of two stages: training a dual-encoder model to embed queries and candidates in the same space, followed by an Approximate Nearest Neighbor (ANN) search to select top candidates given a query's embedding. In this paper, we propose a new single-stage paradigm: a generative retrieval model which autoregressively decodes the identifiers for the target candidates in one phase. To do this, instead of assigning randomly generated atomic IDs to each item, we generate Semantic IDs: a semantically meaningful tuple of codewords for each item that serves as its unique identifier. We use a hierarchical method called RQ-VAE to generate these codewords. Once we have the Semantic IDs for all the items, a Transformer based sequence-to-sequence model is trained to predict the Semantic ID of the next item. Since this model predicts the tuple of codewords identifying the next item directly in an autoregressive manner, it can be considered a generative retrieval model. We show that our recommender system trained in this new paradigm improves the results achieved by current SOTA models on the Amazon dataset. Moreover, we demonstrate that the sequence-to-sequence model coupled with hierarchical Semantic IDs offers better generalization and hence improves retrieval of cold-start items for recommendations.

具有生成檢索功能的推薦系統

Recommender Systems with Generative Retrieval

摘要

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