用生成式模型提升长序列推荐准确率
Autoregressive Generation Strategies for Top-K Sequential Recommendations
- 采用自回归生成策略，通过多序列采样聚合预测未来物品
- 新方法在长时序推荐上优于传统Top-K和单序列策略
- 适合需要精准长期推荐的场景，如电商、内容平台
现代序列推荐系统通常以预测下一个物品为目标。本文探讨基于生成式Transformer模型在Top-K序列推荐任务中的适用性，目标是预测用户在未来短时间内可能交互的物品。研究评估了常见的自回归生成策略，包括贪婪解码、束搜索和温度采样。此外，提出基于温度采样多序列生成后聚合的新型策略：互逆排名聚合（RRA）和相关性聚合（RA）。在多个数据集上的实验表明，这些策略在长时序推荐中表现更优，显著优于广泛使用的Top-K预测方法和单序列自回归生成策略。
原文摘要 · Abstract (English)
The goal of modern sequential recommender systems is often formulated in terms of next-item prediction. In this paper, we explore the applicability of generative transformer-based models for the Top-K sequential recommendation task, where the goal is to predict items a user is likely to interact with in the "near future". We explore commonly used autoregressive generation strategies, including greedy decoding, beam search, and temperature sampling, to evaluate their performance for the Top-K sequential recommendation task. In addition, we propose novel Reciprocal Rank Aggregation (RRA) and Relevance Aggregation (RA) generation strategies based on multi-sequence generation with temperature sampling and subsequent aggregation. Experiments on diverse datasets give valuable insights regarding commonly used strategies' applicability and show that suggested approaches improve performance on longer time horizons compared to widely-used Top-K prediction approach and single-sequence autoregressive generation strategies.
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