A cross-domain recommender system with consistent information transfer
A cross-domain recommender system with consistent information transfer
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DOI：
10.1016/j.dss.2017.10.002
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发表时间：
2017-12
期刊：
影响因子：
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通讯作者：
Qian Zhang;Dianshuang Wu;Jie Lu;Feng Liu;Guangquan Zhang
中科院分区：
文献类型：
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作者：
Qian Zhang;Dianshuang Wu;Jie Lu;Feng Liu;Guangquan Zhang
推荐系统为用户提供个性化的在线产品和服务推荐，并且是当今在线娱乐自助餐中无处不在的一部分。然而，由于缺乏足够的偏好数据，许多人遭受冷启动问题，这阻碍了他们的发展。跨域推荐系统已经被提出作为一种可能的解决方案。这些系统将知识从一个有足够偏好信息的领域转移到另一个没有偏好信息的领域。跨领域推荐前景广阔，但现有方法不能保证从源领域提取的知识与目标领域的知识一致，影响了推荐的准确性。为了解决这个具有挑战性的问题，我们提出了一个跨域推荐系统与一致的信息传输（CIT）。知识的一致性是基于用户和项目的潜在群体，域适应技术被用来映射和调整这些群体在这两个领域保持一致性迁移学习过程中。实验在三个类别的五个真实世界数据集上进行：电影，书籍和音乐。九个跨域推荐任务的结果表明，CIT优于五个基准，并提高了在目标领域的建议，特别是稀疏数据的准确性。在实践中，我们提出的方法被应用到电信产品推荐系统和业务合作伙伴推荐系统（智能BizSeeker），以提高企业和个人客户的个性化决策。
Recommender systems provide users with personalized online product and service recommendations and are a ubiquitous part of today's online entertainment smorgasbord. However, many suffer from cold-start problems due to a lack of sufficient preference data, and this is hindering their development. Cross-domain recommender systems have been proposed as one possible solution. These systems transfer knowledge from one domain that has adequate preference information to another domain that does not. The outlook for cross-domain recommendation is promising, but existing methods cannot ensure the knowledge extracted from the source domain is consistent with the target domain, which may impact the accuracy of the recommendations. To address this challenging issue, we propose a cross-domain recommender system with consistent information transfer (CIT). Knowledge consistency is based on user and item latent groups, and domain adaptation techniques are used to map and adjust these groups in both domains to maintain consistency during the transfer learning process. Experiments were conducted on five real-world datasets in three categories: movies, books, and music. The results for nine cross-domain recommendation tasks show that CIT outperforms five benchmarks and increases the accuracy of recommendations in the target domain, especially with sparse data. Practically, our proposed method is applied into a telecom product recommender system and a business partner recommender system (Smart BizSeeker) to enhance personalized decision making for both businesses and individual customers.