Recommender System for Multiple Databases Based on Web Log Mining

Finding information from a large collection of resources is a tedious and time-consuming process. Due to information overload, searchers often need help and assistance to search and find the information. Recommender system is one of the innovative solutions to the problem related to information searching and retrieval. It helps and assist searchers by recommending the possible solution based on the previous search activities. These activities can be obtained from the web log, which requires a web log mining approach to extract all the keywords. In this study, keywords obtained from the library web log were analysed and the search keyword patterns were obtained. These keyword patterns were from several databases or resources that were subscribed by the library. The finding revealed some of the popular keywords and the most searchable databases among the searchers. This information was used to design and develop the recommender system that can be used to assist other searchers. The usability test of the recommender system showed that it is beneficial and useful to the searchers. These findings will also benefit the management in planning and managing the subscription of online databases at the university’s library.

Implementation of Web Log Mining Device Under Apriori Algorithm Improvement and Confidence Formula Optimization

With the increasing number of internet users, a large number of network alarm information increases, resulting in the increasing pressure of SMS gateway and frequent alarm delay. Therefore, in order to effectively improve the above problems, the article is based on the improved Apriori algorithm and confidence formula, points and grabbing module, model training module and the test evaluation module device, three steps to realize the web log mining and mining system design for the data acquisition module, data preprocessing module, mining model building blocks, mining model checking module and mining model analysis and evaluation module five modules. Finally, the Python program was used to verify the test data of about two million pieces of original alarm data in a company's network management database for a consecutive month. The verification results show that the design of this paper has greatly reduced the number of original alarms and completed the merging of related rules.

A Meta-Heuristic Algorithm Approximating Optimized Recommendations for E-Commerce Business Promotions

To provide personalized services such as online-product recommendations, it is usually necessary to model clickstream behavior of users if implicit preferences are taken into account. To accomplish this, web log mining is a promising approach that mines clickstream sessions and depicts frequent sequential paths that a customer follows while browsing e-commerce websites. Strong attributes are identified from the navigation behavior of users. These attributes reflect absolute preference (AP) of the customer towards a product viewed. The preferences are obtained only for the products clicked. These preferences are further refined by calculating the sequential preference (SP) of the user for the products. This paper proposes an intelligent recommender system known as SAPRS (sequential absolute preference-based recommender system) that embed these two approaches that are integrated to improve the quality of recommendation. The performance is evaluated using information retrieval methods. Extensive experiments were carried out to evaluate the proposed approach against state-of-the-art methods.