Personalized Query Recommendation Using Semantic Factor Model

In the field of query recommendation,the current techniques for semantic analysis technology can’t meet the demands of users.In order to meet diverse needs,we improved the LDA model and designed a new query recommendation model based on collaborative filtering-Semantic Factor Model(SFM),which combines text information,user interest information and web source.First,we improved the LDA model from bag-of-word to bag-of-phrase to understand the topics expressed by users’frequently used sentences.The phrase bag model treats phrases as a whole and can capture more accurate query intent.Second,we use collaborative filtering to build an evaluation matrix between user interests and personalized expressions.Third,we designed a new scoring function that can recommend the top n resources to users.Finally,we conduct experiments on the AOL data set.The experimental results show that compared with other latest query recommendation techniques,SFM has higher recommendation quality.

Web Page Recommendation Using Distributional Recurrent Neural Network

In the data retrieval process of the Data recommendation system,the matching prediction and similarity identification take place a major role in the ontology.In that,there are several methods to improve the retrieving process with improved accuracy and to reduce the searching time.Since,in the data recommendation system,this type of data searching becomes complex to search for the best matching for given query data and fails in the accuracy of the query recommendation process.To improve the performance of data validation,this paper proposed a novel model of data similarity estimation and clustering method to retrieve the relevant data with the best matching in the big data processing.In this paper advanced model of the Logarithmic Directionality Texture Pattern(LDTP)method with a Metaheuristic Pattern Searching(MPS)system was used to estimate the similarity between the query data in the entire database.The overall work was implemented for the application of the data recommendation process.These are all indexed and grouped as a cluster to form a paged format of database structure which can reduce the computation time while at the searching period.Also,with the help of a neural network,the relevancies of feature attributes in the database are predicted,and the matching index was sorted to provide the recommended data for given query data.This was achieved by using the Distributional Recurrent Neural Network(DRNN).This is an enhanced model of Neural Network technology to find the relevancy based on the correlation factor of the feature set.The training process of the DRNN classifier was carried out by estimating the correlation factor of the attributes of the dataset.These are formed as clusters and paged with proper indexing based on the MPS parameter of similarity metric.The overall performance of the proposed work can be evaluated by varying the size of the training database by 60%,70%,and 80%.The parameters that are considered for performance analysis are Precision,Recall,F1-score and the accuracy of data retrieval,the query recommendation output,and comparison with other state-of-art methods.