User Interest Prediction Model for Hybrid Tag Recommender Automation Systems

The problem of web search has been discussed in variety of situations and there are many approaches recommended by different researchers earlier. Personalized recommender systems aim to help users access and retrieve relevant information or items from large collections, by automatically finding and suggesting products or services of likely interest based on observed evidence of the users‟ preferences. For many reasons, user preferences are difficult to guess, and therefore re-commender systems have a considerable variance in their success ratio in estimating the users tastes and interests. In such a scenario, self-predicting the chances that a recommendation is accurate before actually submitting it to a user becomes an interesting capability from many perspectives. Performance prediction has been studied in the context of search engines in the Information Retrieval field, but there is little if any prior research of this problem in the recommendation domain. Based on computed state support measure, the method computes the interest probability and finally generates recommendations to the prediction model. The proposed method increases the efficiency of the web search and reduces the overall search time complexity.