INTEREST CHANGES IN MULTI USER ENVIRONMENT FOR EFFICIENT HYBRID TAG RECOMMENDER SYSTEMS
DOI:
https://doi.org/10.61841/dzpppr05Keywords:
Web Search, Web Mining, Web Inference Model,, User Interest, Multi User Environment, HYBRID TAG RECOMMENDERAbstract
The modern world spends their maximum time in surfing internet and they perform their most activities through the internet. Adaptations of search performance predictors from the Information Retrieval field, and propose new predictors based on theories and models from Information Theory and Social Graph Theory. We show the instantiation of information-theoretical performance prediction methods on both rating and access log data, and the application of social-based predictors to social network structures. Recommendation performance prediction is a relevant problem per se, because of its potential application to many uses. Thus, we primarily evaluate the quality of the proposed solutions in terms of the correlation between the predicted and the observed performance on test data. This assessment requires a clear recommender evaluation methodology against which the predictions can be contrasted. Given that the evaluation of recommender systems is an open area to a significant extent, the thesis addresses the evaluation methodology as a part of the researched problem. We analyse how the variations in the evaluation procedure may alter the apparent behaviour of performance predictors, and we propose approaches to avoid misleading observations. In addition to the stand-alone assessment of the proposed predictors, we re-search the use of the predictive capability in the context of one of its common applications, namely the dynamic adjustment of recommendation methods and components. We research approaches where the combination leans towards the algorithm or the component that is predicted to perform best in each case, aiming to enhance the performance of the resulting dynamic configuration. The thesis reports positive empirical evidence confirming both a significant predictive power for the proposed methods in different experiments, and consistent improvements in the performance of dynamic recommenders employing the proposed predictors.
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