Research And Implementation Of Hybrid Recommendation System Based On Multi-Factor Context Awareness

The recommendation system plays an increasingly important role in daily life.The recommendation system can effectively filter mass information,and satisfy the user's personalized needs.At present,it has been widely used in many fields,such as e-commerce,movies and video web sites,social networks and personalized advertising,creating considerable business value.However,the development of recommendation system is facing new challenges at this stage.On the one hand,for the research of recommendation methods,such as collaborative filtering recommendation and tag-based recommendation,can only measure the user's preferences from the perspective of a single method,or lack of consideration of the feature relevance between items when mixing recommendation.The conclusion is not ideal,but the clustering method can excavate the semantic relationship of the items,and then make the user's preference features more significant.On the other hand,with the passage of time,the recommendation system will gradually accumulate the trend of user's interest and induce inertia effects,ignoring the comprehensive impact of multiple context-aware factors on the recommendation result,and the recommendation effect needs to be improved.This paper starts from the characteristics of the zhihu online community,making use of the tag clustering method to improve and analyze Tag-User CF recommendation algorithm,so that the items under the same tag cluster are more cohesive and representative,and can better reflect the user's preference features.The design and implementation of a hybrid recommendation algorithm based on collaborative filtering and tag clustering(TBC-User CF)are completed.On this basis,a recommendation system which is integrated with multi-factor context awareness is realized.Finally,the evaluation of effectiveness of the recommendation schemes are completed.The main research work of this paper is as follows:(1)On the basis of the present existing Tag-User CF recommendation algorithm,we put forward an improvement method.Through the integration of tag clusters to explore the feature relevance between items,the recommendation result is more accurate.The theoretical research process of the improved TBC-User CF recommendation algorithm is elaborated,and the related steps are analyzed.At the same time,the complexity of the recommendation algorithm is analyzed and evaluated,and the recommendation effect is explained.(2)Based on the research results,we design and implement a recommendation system incorporating TBC-User CF and context awareness.The principles and functions of the data module,the recommendation module and the user interaction module of the recommendation system are introduced.The context-aware recommendation process is discussed in detail.The interface of the recommendation system is displayed and the user experience is enhanced.(3)From the two aspects of performance test and function test,we evaluate the recommendation schemes by verifying the utility of the recommendation algorithm and the effectiveness of the recommendation system.The experimental analysis shows that the recommendation quality of the recommendation algorithm is improved from three aspects of precision,recall and coverage.Through the work of this paper,it can be demonstrated that the use of tag clustering can better reflect the user's preferences,and the aggregation of tag clusters makes the recommendation process more optimized.Moreover,the idea of combining recommendation with context awareness makes the recommendation system more adaptability and diversity,and can provide satisfactory recommendations.