Research Of Personalized Recommended Dased On Mobile Scene Model

With the rapid development of the Internet, the information on the Internet everyyear to increase geometrically. How accurate this massive information repository toobtain the required information has become a growing concern about the problem.The search engine is in this context, emerged and developed rapidly. Search enginetechnology to some extent, to meet the need for general inquiries, but it’s the massside search, still can not meet the mobile environment in different locations, differentbackgrounds, and different purposes and in different periods of personalized searchneeds. With people increasingly mobile and traditional Internet search technology isdifficult to meet the different backgrounds in the mobile environment, differentpurposes, different locations and different periods of demand for personalized search,personalized, intelligent and mobile technology is becoming the next generation ofsearch technology direction of development.The main contents and innovations of this dissertation are summarized asfollows:Mobile search, personalized search and recommendation services researchbackground, typical applications and research status, and specific personalizedrecommendation concept and analysis of the basic working principle of therecommendation engine and classification elaborated personalized recommendedclassification system.Details three typical personalized recommendation algorithm: recommendationalgorithm based on association rules, content-based recommendation algorithm andcollaborative filtering recommendation algorithm, and highlights three intelligentoptimization algorithms: genetic algorithms, cultural, genetic algorithms and artificialimmune algorithm. Finally, a detailed analysis of a specific application of theintelligent optimization algorithms in personalized recommendation.Relevant parameters on the basis of analysis of the model of the mobile scene forthe current scenario information and historical scenarios information define thesimilarity between the two, and design optimization model specific mobile scene.Mobile scenarios based personalized recommendation optimization model basedon the design of a new clone genetic quantum heuristic search algorithm CGQSA, theclone operator the CGQSA algorithm, mutation operator, crossover operator and selection operator specific the definition.Through the test design CGQSA algorithm and location-aware text search query,based on the Prestige location-aware text search query, nearest neighbor keywordquery comparative experiments, faster convergence speed, and maintain a high recalland precision.