基于短期记忆与遗忘系数的用户个性化建模方法研究

搜索引擎的一个标准是不同的用户用相同的查询条件检索时,返回的结果相同。为解决准确性问题,个性化搜索引擎被提出,它可以根据用户的不同个性化特征提供不同的搜索结果。然而,现有的方法更注重用户的长时记忆和独立的用户日志文件,从而降低了个性化搜索的有效性。获取用户短时记忆模型来提供准确有效的用户偏好的个性化搜索方法被广泛采用。首先,根据基于查询关键词的相关概念生成短期记忆模型；接着,基于用户的时序有效点击数据生成用户个性化模型；最后,在用户会话中引入了遗忘因子来优化用户个性化模型。实验结果表明,所提出的方法可以较好地表达用户信息需求,较为准确地构建用户的个性化模型。

Building User Personalization Model Based on Short Term Memory and Forgetting Factor

One criticism of search engines is that when queries are submitted,the same results are returned to different users.To address the accuratcy problem,personalized search was proposed,since it could provide different search results based upon the preferences of users.However,the existing methods concentrate more on the long-term and independent user profile,thus reducing the effectiveness of personalized search.We introduced an approach that captures the user context to provide accurate preferences of users for effectively personalized search.First,the short-term query context is generated to identify related concepts of the query.Second,the user context is generated based on the click through data of users.Finally,a forgetting factor is introduced to merge the independent user context in a user session,which maintains the evolution of user preferences.The experimental results fully confirm that our approach can successfully represent user context according to individual user information needs.