HeteroMF: recommendation in heterogeneous information networks using context dependent factor models
HeteroMF: recommendation in heterogeneous information networks using context dependent factor models
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DOI：
10.1145/2488388.2488445
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发表时间：
2013-05
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通讯作者：
Mohsen Jamali;L. Lakshmanan
中科院分区：
文献类型：
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作者：
Mohsen Jamali;L. Lakshmanan
随着在线可用信息量的不断增加，推荐系统开始为搜索引擎提供一种可行的替代和补充，帮助用户找到感兴趣的对象。基于矩阵分解(MF)模型的方法是推荐系统中最先进的方法。对MF的输入是用户反馈，采用评级矩阵的形式。然而，用户可以跨不同的上下文与多种类型的实体进行交互，从而产生多个评级矩阵。换句话说，用户可以在异类信息网络中进行交互。通常，在异类网络中，来自任何两个实体类型的实体可以与指示认可级别的权重(评级)进行交互。集合矩阵分解(CMF)被用来解决异质网络中的推荐问题。然而，CMF的一个主要问题是，实体在不同的上下文中共享相同的潜在因素。这在两种情况下尤其有问题：在一种情况下冷启动的实体的潜在因素将主要根据这些实体不是冷启动的其他情况下的数据来学习，因此对于冷启动情况没有适当地学习这些因素。此外，如果一个上下文与另一个上下文相比具有更多的数据，则主导上下文将主导在这两个上下文中共享的实体的潜在因素的学习过程。本文提出了一种上下文相关的矩阵分解模型HeteroMF，该模型考虑了每种实体类型实体的一般潜在因子和实体所涉及的每种上下文的上下文相关潜在因子。我们为每个实体学习一个通用的潜在因素，并为每个上下文学习传递矩阵，将通用的潜在因素转化为上下文相关的潜在因素。在Epinions和Flixster的两个真实数据集上的实验表明，HeteroMF的性能大大优于CMF，特别是对于冷启动实体和一个上下文中的交互由其他上下文主导的上下文。
With the growing amount of information available online, recommender systems are starting to provide a viable alternative and complement to search engines, in helping users to find objects of interest. Methods based on Matrix Factorization (MF) models are the state-of-the-art in recommender systems. The input to MF is user feedback, in the form of a rating matrix. However, users can be engaged in interactions with multiple types of entities across different contexts, leading to multiple rating matrices. In other words, users can have interactions in a heterogeneous information network. Generally, in a heterogeneous network, entities from any two entity types can have interactions with a weight (rating) indicating the level of endorsement. Collective Matrix Factorization (CMF) has been proposed to address the recommendation problem in heterogeneous networks. However, a main issue with CMF is that entities share the same latent factor across different contexts. This is particularly problematic in two cases: Latent factors for entities that are cold-start in a context will be learnt mainly based on the data from other contexts where these entities are not cold-start, and therefore the factors are not properly learned for the cold-start context. Also, if a context has more data compared to another context, then the dominant context will dominate the learning process for the latent factors for entities shared in these two contexts. In this paper, we propose a context-dependent matrix factorization model, HeteroMF, that considers a general latent factor for entities of every entity type and context-dependent latent factors for every context in which the entities are involved. We learn a general latent factor for every entity and transfer matrices for every context to convert the general latent factors into a context-dependent latent factor. Experiments on two real life datasets from Epinions and Flixster demonstrate that HeteroMF substantially outperforms CMF, particularly for cold-start entities and for contexts where interactions in one contexts are dominated by other contexts.