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With the rapid expansion of information resources, the amount of image data in the network shows an explosive growth trend. The traditional search engines have not considered users’ different interests; therefore image retrieval efficiency is reduced. To solve the problem, this paper puts forward a research on user-based personalized image retrieval technologies. Firstly, this paper studies the user interest model, and provides its definitions and application strategies; secondly, it studies collaborative filtering algorithm based on Kmeans clustering, and solves the problem of sparse resources effectively; Finally, explicit tracking, implicit tracking and relevance feedback methods are adopted to learn and update user interest model constantly to meet the users’ needs and improve retrieval accuracy and efficiency. Based on the above studies, this paper presents a kind of user-based personalized recommendation technology, and completes an image retrieval system based on user personalization, proving that this recommendation technology is able to provide users with better personalized recommendation service. |
