Research On Collaborative Filtering Recommendation Algorithm For Agricultural Products

In recent years,e-commerce has promoted the development of social information with its advantages of facilitating information communication,flexible and convenient of payment method and so on.However,with the development of the Internet,more and more users and commodities enter e-commerce platform,resulting in many users are deeply affected by “information overload”,affecting their shopping experience.Under this background,recommendation system can help users mining their interests from huge amounts of data information appears.Recommendation systems mining users' interests based on users' demographic characteristics and behavior characteristics,and carry on the personalized recommendation to the target user according to user's interests.For operators,application of personalized recommendation can improve the trading volume and achieve the purpose of precision marketing.For consumers,they can save time,and quickly find resources they are looking for.Agricultural products e-commerce is an important part of e-commerce platform,the agricultural products recommendation system has important significance to increase farmers' income,improve the competitiveness of primary industry and promote the development of agricultural information.However,the research of personalized recommendation system of agricultural e-commerce is less.The paper focus on collaborative filtering recommendation algorithm for agricultural products,in order to achieve rapidly and effective recommendation.The purpose of this dissertation is to stimulate further and deeper research of more agricultural recommendation algorithms.The work and innovation points of this dissertation include the following aspects:(1)Select the basic algorithm of recommendation algorithm for agricultural products.Organizing the characteristics that differ commodity trading from electronic trading for agricultural products,as well as summarizing and classifying the collaborative filtering recommendation algorithm,and point out the current problems the collaborative filtering recommendation algorithm is facing.Analyzing the characteristics of electronic trading for agricultural products and characteristics of various collaborative filtering recommendation algorithm.Based on the above works,this dissertation selects item-based collaborative filtering recommendation algorithm as the basis of agricultural products e-commerce.(2)In view of the problem of cold-start problem,improve the basic algorithm.Traditional collaborative filtering recommendation algorithm cannot perform in the condition of cold-start,this dissertation put forward a novel item similarity measure according to this problem.The measure consisted of rating similarity and structural similarity.Rating similarity part is based on the rating difference between two items,the difference between the rating value and the median value,and the difference between the rating value and the average rating value of other items.Structure similarity is the IIF(Inverse Item Frequence,IIF)coefficient which fully reflected common-rating ratio and punished active users.(3)In view of the problem of scalability problem,improve the basic algorithm.Traditional collaborative filtering recommendation algorithm effected by scalability problem on the circumstance of big data,this dissertation propose the Spectral Clustering Item-based Collaborative Filtering(SC-ICF).The algorithm clusters items offline firstly,and when make recommendation online,determines the class which the target item belongs to firstly,and then find nearest neighbors in the class,according to the nearest neighbors' rating information obtained the forecast rating of the target item,and make recommendation list for the target user according to the rating sequence.(4)Experiment on the real data set.This part briefly introduce the overall framework of recommendation engines,then make experiments on Movie Lens 100 k data set and Grecs data set according to the existing data and the improved algorithm.First,input the number of target user according to the prompt,after manual inputing serial number,the program will automatically calculate the prediction rating of non-rating items,and display the recommendation list that on the basis of the calculation results to the target user.The experimental results show that the proposed collaborative filtering recommendation algorithm(remember SC-ICF)can effectively achieve the recommendation function of agricultural products,in addition,the recommendation effect is better than the traditional algorithm in cold start and large-scale data sets.