Research Of Text Recommended Methods Based On Synonym Network

Text recommendation system means that when a user interest model is determined, the system can be compared to determine the similarity of the target text and user interest model and recommend the necessary text to the user according to the similarity in descending order.The recommended method is commonly used text matching strictly text-based feature vector space in the form of the word literally. The more number of the same features of the word set between target text and topic text, the target text is more similar with the theme text, and it is recommended by the system more higher priority; The fewer number of the same features of the word set between target text and topic text, the target text is less similar with the theme text, and it is recommended by the system more lower priority. However, due to the flexibility of natural language, different text description of the same thing will use different synonyms. Without considering the synonym match, the inevitable recommended the results of the single coverage, not comprehensive, accuracy is not high, thus the system is unable to achieve the goal of recommending text of user required accurately, if in the process of text recommended only consider the key match in the form of the literal.Firstly, the article has carried on the study and analysis the current text recommended method, focusing on the numerical vector space model.Secondly, we propose a synonyms-based text recommended improvements. The method not only considered key words literally match, while considering the positive role of synonyms semantic matching in process of text recommendation. Thus, the process of text recommendation is no longer just a simple literal match, but a kind of semantic matching. To some extent, it can improve the accuracy of text recommendation. The experimental results show that the method can recommend the desired text to the user more accurately, the accuracy increases of most 40%.Finally, this paper proposes a synonym network-based link prediction method. Using the ideas of complex network, the method will build a synonym network, then predict the link, which not in the current network but does contain a new synonym relation. We can merge this part of the new synonym relationship and existing synonyms. The synonymous relationship between words in new network will be more rich. Then this part of the new link of synonym relationship, make the semantic matching coverage of some individual words more widely. Continuing to use the combined synonym network, can further improve the performance of text recommendation, when new synonym relation appear the user interest model and the target text at the same time.