Research On Key Technology Of Agricultural Information Recommendation Based On Vertical Search Engine

The Internet has become an important channel for obtaining information resources in our daily life. In face of massive internet information, personalized information recommended services will become the direction of development in the future information services. In addition, governments and departments at different levels has invested substantial resources to establish information platform for agriculture technology, animal husbandry, fishery, agricultural machinery etc. However, due to the lack of information infrastructure in rural areas and the incompetence of agricultural producers in information analysis and processing, the information that is important for agricultural production is not able to be delivered to the agricultural producers and marketers. It is difficult to get a personalized information services for agriculture related people only by mass media, word-of-mouth communication of agricultural information organizations. The objective of this study is to collect, analyze and process the large amount of agriculture related information distributed on the internet, comprehend the will and needs of the users, and accurately deliver the information needed to the agriculture related people, thus improve the function of guidance of agricultural information in the process of agricultural production and increase social and economic benefits.There are three main problems in the application of the existing recommendation system in the field of agriculture. Firstly, there is a lack of information concentration in the field of agriculture. Secondly, there are problems of over fitting and cold start of agricultural users’ interests. Thirdly, personalized classification and recommendation based on the characteristics of agriculture are not realized by the existing information recommender system. Therefore, this paper studies forward the key technologies of data source, user interest model and recommendation algorithm in agricultural information recommender system. The key technologies including agricultural information collection and analysis, model construction of users’interests, construction of recommended model, improvement of recommended calculation, independent decision-making mechanism of software, and provide technical support to the personalized agricultural information recomended service.The main research work of the dissertation is summarized as follows:1. By the comparison of the functions of search engines and search results, agricultural vertical search engine based on nutch is designed to collect, screen and analysis agricultural information in the Internet and constructs the agriculture information recomended database. In view of the application characteristics and disadvantage of vertical search in agriculture, a new word recognition method based on word segmentation technology and reference agricultural terminology corpus is proposed to improve the search engine segmentation module. Experimental results show that compared with the effect of other word segmentation systems, this segmentation module improves segmentation accuracy of text information in the field of agriculture. And by combining the control of the URL quality of seed, this segmentation further increases the accuracy and depth of the agricultural vertical search engine on the agricultural related web pages.2. In regards to the nonuniform representation and inexplicit expression of the spatial attributes of agricultural network resources, a method to identify and extract the spatial attributes of agriculture by using the ontology base of administrative division is proposed. In view of the different types of attribute information, the dominant spatial attribute extraction algorithm and implicit spatial attribute extraction algorithm based on universal search engine is designed, which can effectively label the spatial attribute information of the text information and extract the regional features of users and projects. It provides effective information for the construction of regional label in the interest model of agriculture related users, and provides the basis for the realization of personalized agricultural information recomended mode based on regional characteristics.3. Questionnaire survey was used to study the demand for agricultural information and the ways to access information of agriculture related people. In view of the current status that the existing agricultural information service mode is not able to realize personalized service, an ATBUIM model which can fully reflect the interests of agricultural users is constructed. By selecting the sources of explicit and implicit information of agricultural users and studying the estimation methods and weights of user’s background and browsing behavior to the user’s interest degree, a Bayesian network user interest model is constructed based on mutual information and labeling of agricultural resources classification, which uses the mutual information of the labeling in the agriculture as the node’s conditional probability to update and optimize the model. In this model, the multi channels to obtain information of user’s interests were weighted, which reflects the proportion of different types of information in the model construction and reflects fields that attract the user’s most attention more comprehensively and accurately, laying foundation for the realization of accurate and effective agricultural information recommendation algorithm.4. The three kinds of recommendation algorithms are analyzed and compared. In view of the cold start and sparse data of traditional recommendation algorithm, a new method and ways to solve the problem is proposed, an efficient combined recommendation algorithm is designed. A new method to improve the calculating similarity by adding feature label is developed, which can realize the information recommendation of the new users. For the problem of data sparseness in collaborative filtering algorithm, the paper proposes a new collaborative filtering algorithm which combines the score and characteristic factors of agriculture related users with the score and characteristic factors of agricultural projects. In the algorithm, the predicted score of target user and target project are both the results of the scores of the nearest neighbor combined with the calculation of the user’s score similarity and the user’s characteristic similarity, then the final recommendation results by weighting the above two prediction scores can be obtained. The experiments show that under the same data scarcity, the improved collaborative filtering algorithm has a much smaller average absolute deviation and better recommendation accuracy. Aimed at the shortage of the existing single recommendation algorithm, a combined recommendation algorithm is proposed based on functional network, and a combined recommendation model is constructed. Experiments show that the predictive score of users on the project calculated by the combined recommendation algorithm is more close to the actual score of users on the project.5. In view of the need for autonomous adjustment of its structure, status and behavior of the information push service model in the environment of new network, an agriculture-oriented independent decision-making mechanism of software is developed. This paper constructs a model with knowledge, news and service information in the agricultural network, designs the AKDM thinking decision model targeting the agricultural knowledge, which turns environmental information into an aggregation with belief, desire and intention, and take advantage of decision inference between the above three factors to guide Agent to complete the agricultural information push behavior. Analysis and experiments show that under the restriction of agricultural knowledge and rules, the mechanism can realize the autonomous decision-making and the recommendation of agricultural information.In summary, the research of the effective search of agricultural information on Internet, the construction of user interest model, the personalized precise recommendation algorithm for agricultural information, the mechanism of independent decision-making of software in this paper can provide technical support to the realization of personalized information push service in the field of agriculture.