章内容、使用者记录档与合着网路(coauthorship network),推荐文章给使用者以满足短期的爱好。


网路 (LDA-coauthorship-network-based),此技术使用潜藏狄利克里分配(latent Dirichlet allocation, LDA)与任务导向(task-focused)技术做为文献的推荐技术。实验结果显示我们的方法比传统的合着网路在所有的实验环境都更有效,与内容导向技术相比,当每个任务档(task profile)包含的内容相似度非常相近时,我们的方法比内容导向技术好,但任务档的内容相似度低时,我们的方法结果较差。因此我们进一步发展一套混合方法,可自动切换内容导向与潜藏狄利克里分配为基础的合着网路。此方法可根据任务档中内容相似程度来进行切换至最适合的方法。实验结果显示了混合方法在所有实验环境都表现最优。
【英文摘要】:Most literature database systems use content-based technique to retrieve articles to users. However, the content-based technique relies on exact keywords provided by users to search for articles the users are interested in. On the other hand, most recommender system techniques are based on user’s long-term browsing/transaction history so as to recommend items that meet users’ long term interest. However, in literature database system, users’ information need is often short-term. Previous works in recommending articles to satisfy users’ short-term interest have util


ized article content, usage log, and coauthorship network.
In this study, we extend coauthorship network method and incorporate scholars’ collaboration topics into the coauthorship network. Specifically, we propose a LDA-coauthorship-network-based technique that integrates topic information into links of the coauthorship network using latent Dirichlet allocation (LDA), and a task-focused (short-term) technique is proposed for recommending literature articles. Experimental results show that the proposed approach is more effective than the traditional coauthorship network method under all operating regions. When compared to the content-based technique, it has better performance when each task profile contains articles that are similar in their content but is less effective otherwise. We further develop a hybrid method that switches between content-based technique and LDA-coauthorship-network-based technique based on the content coherence of a task profile. Experimental results show that the hybrid method outperforms all the other methods under all operating regions.

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  • 来源:中山大学;作者:陈裕翔


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