推荐系统概述4

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文章列表
推荐系统概述1
推荐系统概述2
推荐系统概述3
推荐系统概述4
推荐系统概述5
推荐系统概述6
推荐系统概述7

本篇是第4篇

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本节主要内容：

参考文献

[1] C. Shi, C. Zhou, X. Kong, P. S. Yu, G. Liu, and B. Wang, “HeteRecom: A semantic-based recommendation system in heterogeneous networks,” in Proc. 18th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2012, pp. 1552–1555.

[2] C. Shi, X. Kong, P. S. Yu, and S. Xie. Relevance search in heterogeneous networks. In EDBT, 2012.

[3] C. Shi, Z. Zhang, P. Luo, P. S. Yu, Y. Yue, and B. Wu, “Semantic path based personalized recommendation on weighted heterogeneous information networks,” in Proc. 24th ACM Int. Conf. Inf. Knowl. Manage., 2015, pp. 453–462

文献依赖关系

[1]这篇论文中，作者运用基于路径的相关性测量构建了一个异构网络下的 non-personalized recommendation.论文测量相似性的方法来自于[2]HeteSim

基于语义的大众化推荐

[1]这篇论文中，作者运用基于路径的相关性测量构建了一个异构网络下的 non-personalized recommendation. 该推荐系统可以做semantic recommendation和relevance recommendation .如下图所示：

1. Data extraction: it extracts data from different data source (e.g., database and web) to construct the network.
2. Network modeling: it constructs the HIN with a given network schema. According to the structure of data, users can specify the network schema (e.g., bipartite, star or arbitrary schema) to construct the HIN database. The database provides the store and index functions of the node table and edge table of the HIN.
3. Network analysis: it analyzes the HIN and provides the recommendation services. It first computes and stores the relevance matrix of object pairs by the path-based relevance measure. Based on the relevance matrix and efficient computing strategies, the system can provide the online semantic recommendation service. Through the weight learning method, it can combine the relevance information from different semantic paths and provide online relevance recommendation service.
4. Recommendation service: it provides the succinct and friendly interface of recommendation services.

HeteSim[2]

1. Essentially, HeteSim(s; t|P) is a pair-wise random walk based measure, which evaluates how likely s and t will meet at the same node when s follows along the path and t goes against the path.
2. Since relevance paths embody different semantics, users can specify the path according to their intents. The semantic recommendation calculates the relevance matrix with HeteSim and recommends the top k objects.

举一个例子：

weight learning method

There are many relevance paths connecting the query object and related objects, so the relevance recommendation should comprehensively consider the relevance measures based on all relevance paths. It can be depicted as follows.

Although there can be infinite relevance paths connecting two objects, we only need to consider those short
paths, since the long paths are usually less important

现在的问题是我们如何决定$\omega_i$.论文认为，$\omega_i$由关联路径的重要性来表达。而关联路径的重要性可以用这个路径的长度和强度来表达。路径的强度可以由组成该路径的关系强度来表达。

关系强度：

where $O(A|R)$ is the average out-degree of type $A$ and $I(B|R)$ is the average in-degree of type $B$ based on relation $R$

路径强度：

关联路径重要性：

权重：

Efficient Computing Strategies

1. 对于频繁使用的关联路径，进行离线计算，线上查询的策略。
2. 快速矩阵乘法
3. 矩阵稀疏化：去掉那些不太重要的结点。

基于语义的个性化推荐[3]

相似度测量方案的改进

在第2部分中，我们介绍过PathSim，但是它的测量是没有权重的。[3]将PathSim的方案扩展到了加权元路径中。

但是，u1和u2从情景中来看，应该是最不相同的（u1喜欢的，u2都不喜欢），可是在这里它们的相似度为1，这是因为我们仅仅考虑了路径个数，却没有考虑路径的分数值（权重）。因此本文对传统的这种相似性测量方案提出了改进。改进的措施是：我们将路径按照评分值（权重）进行分类，每一个类别的路径叫做atomic meta path。当我们考虑评分值为1的路径时，我们就假定其他路径不存在。然后用传统的计算方案得到u1和u2在评分值为1这个路径集下的相似度。接着考虑评分值为2,…,5的路径。可以一次计算u1和u2对应的相似度。最后我们把这些相似度进行加和，得到改进后的相似度，如图：

后面的章节，请阅读《[推荐系统概述5](http://blog.youkuaiyun.com/github_36326955/article/details/71407058》

如果这篇博文对你有帮助，希望您可以打赏给博主相国大人。我可以和您建立更多的联系，并且在相关领域提供给您更多的资料和技术支持。
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