Web Session Identification Based on Probabilistic Latent Semantic Analysis

To improve the validity and efficiency of the user session identification, a new approach of user session identification is presented, which is based on the probabilistic latent semantic analysis model and the competitive reward and punishment algorithm.The probabilistic latent semantic analysis model is used to compute the probabilities of a request and each active session.The model identifies which active session a request should belong to in terms of the probabilities.A competitive reward and punishment method is applied to determine the end of sessions.The experimental results show that the average percentage of successfully reconstructed sessions based on PLSA and CRP is higher than other session identification algorithms.