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Recommendation based on Deduced Social Networks in an educational digital library

机译：在教育数字图书馆中基于推论社交网络的推荐

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摘要

Discovering useful resources can be difficult in digital libraries with large content collections. Many educational digital libraries (edu-DLs) host thousands of resources. One approach to avoiding information overload involves modeling user behavior. But this often depends on user feedback, along with the demographic information found in user account profiles, in order to model and predict user interests. However, edu-DLs often host collections with open public access, allowing users to navigate through the system without needing to provide identification. With few identifiable users, building models linked to user accounts provides insufficient data to recommend useful resources. Analyzing user activity on a per-session basis, to deduce a latent user network, can take place even without user profiles or prior use history. The resulting Deduced Social Network (DSN) can be used to improve DL services. An example of a DSN is a graph whose nodes are sessions and whose edges connect two sessions that view the same resource. In this paper we present a recommendation framework for edu-DLs that depends on deduced connections between users. Results show that a recommendation system built from DSN-dependent parameters can improve performance compared to when only text similarity between resources is used. Our approach can potentially improve recommendation for DL resources when implicit user activities (e.g., view, click, search) are abundant but explicit user activities (e.g., account creation, rating, comment) are unavailable.
机译：在具有大量内容的数字图书馆中，发现有用的资源可能很困难。许多教育数字图书馆（edu-DL）拥有数千种资源。避免信息过载的一种方法涉及对用户行为进行建模。但这通常取决于用户反馈以及在用户帐户配置文件中找到的人口统计信息，以便建模和预测用户兴趣。但是，edu-DL经常托管具有开放公共访问权限的集合，从而使用户无需提供标识即可浏览系统。由于几乎没有可识别的用户，与用户帐户链接的构建模型提供的数据不足，无法推荐有用的资源。即使没有用户配置文件或先前使用历史记录，也可以进行基于会话的用户活动分析，以推断潜在的用户网络。由此产生的推论社交网络（DSN）可用于改善DL服务。 DSN的一个示例是一个图，该图的节点为会话，并且其边缘连接两个查看同一资源的会话。在本文中，我们提出了一个edu-DL的推荐框架，该框架依赖于用户之间的推导连接。结果表明，与仅使用资源之间的文本相似性相比，基于DSN的参数构建的推荐系统可以提高性能。当隐式用户活动（例如，查看，单击，搜索）丰富但显式用户活动（例如，帐户创建，评级，评论）不可用时，我们的方法可以潜在地改善对DL资源的推荐。

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