计算机科学 ›› 2022, Vol. 49 ›› Issue (6): 142-148.doi: 10.11896/jsjkx.210400173
蒲岍岍, 雷航, 李贞昊, 李晓瑜
PU Qian-qian, LEI Hang, LI Zhen-hao, LI Xiao-yu
摘要： 随着数据信息的不断扩增,点对点推荐模型作为深度学习中常用的推荐算法,能一定程度上解决信息过载的问题。但其仅通过单个用户与单个新闻预测得分,未利用新闻列表之间的交互信息。为了提升个性化推荐的质量,如何充分利用用户的历史信息、新闻的文本语义信息和列表信息等,以精准全面地表征用户和新闻是当前新闻推荐系统亟待解决的问题。对此,提出了一种增强列表信息和用户兴趣的新闻个性化推荐算法。利用用户历史点击新闻序列与新闻数据训练点对点模型进行特征构造,实现用户兴趣的个性化信息抽取,通过注意力网络处理用户与整个新闻列表特征来增强列表信息,实现直接对全局列表的推荐排序。实验结果表明,此种增强列表信息和用户兴趣的个性化推荐算法能够建模全局列表信息,与前沿的新闻推荐算法相比效果有显著提升。
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