arXiv 1710.083212017-10-23cs.CLcs.IR

使用深度学习的基于内容的文档推荐

Content Based Document Recommender using Deep Learning

Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）；ParallelDots（ParallelDots 公司）

Nishant Nikhil, Muktabh Mayank Srivastava

备注：: Accepted in ICICI 2017, Coimbatore, India

摘要：

随着信息技术的进步，可用数据量激增。但是信息检索技术已经不能跟上信息生成的这种速度，导致用于检索相关信息的时间过度消耗。尽管存在用于辅助用户搜索数据库以及过滤和推荐相关信息的系统，但是使用文档内容进行推荐的推荐系统仍然有很长的路要走。在这里，我们提出了一种基于深度学习的监督方法，根据内容的相似性推荐相似的文档。我们将C-DSSM模型与Word 2 Vec分布式表示相结合，建立了一个新的模型，通过给文档对打分来区分文档对的相关性和不相关性，使用我们的模型，文档检索的时间复杂度为O（1），存储复杂度为O（n），其中n是文档的个数。

英文摘要：

With the recent advancements in information technology there has been a huge surge in amount of data available. But information retrieval technology has not been able to keep up with this pace of information generation resulting in over spending of time for retrieving relevant information. Even though systems exist for assisting users to search a database along with filtering and recommending relevant information, but recommendation system which uses content of documents for recommendation still have a long way to mature. Here we present a Deep Learning based supervised approach to recommend similar documents based on the similarity of content. We combine the C-DSSM model with Word2Vec distributed representations of words to create a novel model to classify a document pair as relevant/irrelavant by assigning a score to it. Using our model retrieval of documents can be done in O(1) time and the memory complexity is O(n), where n is number of documents.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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