用物品关联图增强马尔可夫链，提升序列推荐准确率
Item Association Factorization Mixed Markov Chains for Sequential Recommendation
- 构建物品关联图，融合用户行为与物品间关系
- 在4个公开数据集上显著提升推荐排序效果
- 轻量设计，参数增长少，适合工业部署
序列推荐旨在根据用户历史行为序列预测其下一个感兴趣项目。尽管已有大量基于马尔可夫链的模型研究，但多数仅关注用户行为序列，忽视了物品间的整体关联。本文提出物品关联因子混合马尔可夫链算法，通过物品关联图引入物品间关联信息，并与用户行为序列结合。在四个公开数据集上的实验表明，该算法显著提升推荐排序性能，且参数量增加有限。对先验平衡参数的调优研究进一步证实，不同数据集下引入物品关联信息具有重要价值。
原文摘要 · Abstract (English)
Sequential recommendation refers to recommending the next item of interest for a specific user based on his/her historical behavior sequence up to a certain time. While previous research has extensively examined Markov chain-based sequential recommendation models, the majority of these studies has focused on the user's historical behavior sequence but has paid little attention to the overall correlation between items. This study introduces a sequential recommendation algorithm known as Item Association Factorization Mixed Markov Chains, which incorporates association information between items using an item association graph, integrating it with user behavior sequence information. Our experimental findings from the four public datasets demonstrate that the newly introduced algorithm significantly enhances the recommendation ranking results without substantially increasing the parameter count. Additionally, research on tuning the prior balancing parameters underscores the significance of incorporating item association information across different datasets.
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