A parallel approach for web session identification to make recommendations efficient.

A sequence of web pages visited by the clients over a particular timeframe is called a session. Web log mining is done to analyse the behaviour of the users, using the web access patterns. Sessions are identified as the significant part of the construction of the recommendation model. The novel part of the work makes use of backward moves made by the user, considering both the referrer url and the requested url extracted from the extended web log for session identification which is not taken into consideration in the existing heuristic-based approach. Two noteworthy issues in session identification are: 1) framing excessively numerous smaller length sessions; 2) taking longer time for identifying the sessions. In the proposed work, the length of the sessions are maximised using split and merge technique and the time taken for session identification is reduced using thread parallelisation. For efficient storage and retrieval of information the hash map data structure is used. The proposed work shows significant improvement in performance in terms of execution time, standard error, correlation coefficient and the objective value.