Recommendation system for information retrieval based on web logs mining

The Web page recommendation systems can effectively predict users' next clicking results in information retrieval process and the research will be beneficial to many applications ranging from intelligent recommendation to improving effectiveness of search engines. In this paper, in order to deal with the problem of lack of semantic processing in present systems, the technology of Web log mining has been adopted to use word frequency and the concept relevancy model of HOWNET to compute document relevancy, and the result is used to guide the process of pages recommendation. In the end, a relevancy-based recommendation system based on query logs mining is proposed, which combines document relevancy calculation with the method of statistical language model. Furthermore, the prototype system has been built and the experiment showed that this method significantly improved the accuracy of recommendation systems. In conclusion, this method outperforms other models in the web page recommendation systems and overcomes the problem of the lack of effective semantic process. The performance of recommendation system has been improved greatly and the technique can be widely used on the intelligent recommended services in information retrieval field.