Research On Personalized Recommendation Strategy Optimization Of Taobao Mobile Terminal Based On Big Data

With Apple's 2007 launch of touch screen mobile phones as the starting point,smart phones are rapidly gaining popularity around the world,and mobile Internet technology is also rapidly developing.By 2018,smart phones have become devices that are carried by billions of people at all times.Especially in China,the development of mobile Internet is leading the world,and the services provided by smart phone applications penetrate into every aspect of life.People's online shopping consumption habits have basically shifted to the mobile side,and the proportion of PCs has already been less than one-tenth.Under such spending habits,traditional marketing strategies are gradually losing their effectiveness.Marketing strategies based on mobile Internet spending habits are becoming more and more important.Under this background,this thesis starts with the research on the direction of personalized goods recommendation on the mobile platform of e-commerce platform,which has certain theoretical and practical significance.The final research object of this dissertation is the optimization of the personalized recommendation strategy of Taobao mobile search results.On the one hand,through the collection and analysis of Taobao platform user data,product data,and sellers' advertisement product data,as well as the collection and user intent recognition of the user's real-time operation behavior data on the mobile side,the personalized merchandise recommendation is finally provided to the user.On the other hand,providing sellers with better operational tools and training on the use of tools helps sellers to more effectively promote their own products on the Taobao platform.The final realization of the buyers,sellers,Taobao platform win-win.The significance of this paper's research lies in: through analyzing the original personalized recommendation strategy of Taobao mobile search,find the optimization point,carry out step-by-step optimization of algorithm level,operational tool level,and seller operation training level,and finally improve the seller's promotion of products.The conversion rate improved the user's satisfaction with Taobao's mobile-end shopping,and ultimately achieved a higher revenue from the Taobao e-commerce platform.The research and practice in this article have implications for the follow-up Taobao mobile's overall personalized recommendation strategy.It also has certain reference significance for other e-commerce platforms to do personalized recommendation on the mobile terminal.