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Research and Application of Collaborative Recommendation Algorithm Based on User Context

Author: ZhouTao
Tutor: LiHua
School: Chongqing University
Course: Computer System Architecture
Keywords: User context different degrees clustering Slope One collaborative recommendation
CLC: TP301.6
Type: Master's thesis
Year: 2010
Downloads: 131
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Abstract


With the popularity of the Internet and the rapid development of e-commerce, personalized recommendation system has become an important research area of electronic commerce content. Recommended algorithm is the core of personalized recommendation system. Its performance has closely related to the recommended efficiency of the whole recommendation system, recommended quality and users’experience. At present, the popular use of algorithms is based on association rules for recommendation algorithm, content-based recommendation algorithm, and collaborative recommendation algorithm in the existing recommendation systems.The amount of association rules would hugely increase with the system size increasing in recommendation algorithm based on association rules. Content-based recommendation algorithm can only provide the user for profile similar resources without discovering new or potential user interest. Collaborative Filtering technologies as the most successful recommendation techniques have been implemented and applied in many practical systems. Although it can identify potential interest for the user, the traditional collaborative recommendation algorithm is the project’s score by the user as a starting point.The score does not fully reflect the interests of a person. Meanwhile, the personal interests and hobbies have the inextricably link with the occupation, age, education level and a series of factors, and similar attributes of people are also prone to similar interest. So, this paper proposed the collaborative recommendation algorithm based on the user context. According to user context, the algorithm clusters the users, allows each user to be able to accurately find the neighbors with their high similarity. In the same class, according to the user’s history score and the dissimilarity between projects, it predicted score calculated for the target item to obtain the results of recommended target which users need.This thesis completes the following work:(1) After deep analyzing the problem of context loss, this thesis puts forward the user context and its formalization combined with Situation Semantics and the factors affecting the interests of users, and their natural attributes and social attributes. And further study for user context, this thesis puts forward the classification method of user context. (2) The clustering method of static user context with multiple situational factors is given by the research of dissimilarity degree calculation and dissimilarity degree matrix. After that, this thesis puts forward the collaborative recommendation algorithm based on user context binding predictive value for target item method of Slope One algorithm.(3) The collaborative recommendation algorithm based on user context is compared with traditional collaborative recommendation algorithm based on items and Slope One, using Matlab to carry out simulation experiment in MovieLens. Experimental results show that the algorithm is better in the mean absolute error against traditional algorithm and the Slope One algorithm. So, the feasibility and validity of this algorithm are verified.(4) In combination with the intelligent educational platform project of Higher Education Press based on ontology, semantic and pragmatic, the article designs a scenario based on the user’s personalized recommendation algorithm for collaborative recommendation system model as an example of the algorithm in practice application form.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > General issues > Theories, methods > Algorithm Theory
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