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Research on a Recommender System Based on Bayesian CBR

Author: WangKun
Tutor: SunZhaoHaoï¼›DongDong
School: Hebei Normal
Course: Applied Computer Technology
Keywords: Recommended system Bayesian network Case-Based Reasoning (CBR) Fuzzy mathematical theory Similarity
CLC: TP391.3
Type: Master's thesis
Year: 2011
Downloads: 41
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Abstract


With the widespread popularity of the Internet and WWW network information to the Web as a carrier , the rapid increase of transmission speed , get the information to buy products through the Web has become the mainstream of the times . Variety of information resources on the network , however , the speed of explosive growth in information overload so that the people in the choice of the most satisfactory products with a heavy burden , have been proposed many solutions to solve the above problems , the recommended system is one of them. However , the current e-commerce recommendation system has many deficiencies , recommendation algorithm is a single, sparse data led to the recommended low-quality product attribute types consider only consider property values ??are accurate or all non- precise shortcomings . For the above mentioned issues , the paper will be mainly in the following aspects of the recommendation system in-depth study : application of Bayesian network in the recommendation system , give full play to the powerful features it has expressed uncertainty knowledge combined with CBR to provide strong technical support for resource discovery and recommendation . Propose an overall architecture model based on Bayesian CBR recommender system (BCRS) , the design of the system 's functions , structure and processes . The reasoning method based on Bayesian CBR introduced into the recommendation system can be improved to some extent the difficulty of the problem of large-scale case base retrieval . Analyzed case representation method , a complete and effective expression of user characteristics and various information required in the purchase . Algorithms , fully consider the property classification and attribute interdependencies proposed similarity measure based on the distance of mixed data types . In this algorithm , the user needs can be precise type , but also non - exact type , may be intact or may not complete , having a greater practical significance . Based on the above technology and algorithms , preliminarily achieved a the Books Web services Recommended prototype system , and design the corresponding experimental data test . The experimental results show that the algorithm has a better precision in recommending . In short, we will integrate Bayesian network CBR method and fuzzy mathematical theory applied to e-commerce recommendation system , and propose a similarity measure algorithm combined with Bayesian network the CBR system architecture and mixed data types , the results for the e-commerce recommendation system will some reference value .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Retrieval machine
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