【摘要】：在大數據環(huán)境下,農戶在互聯網中獲取指導農業(yè)生產的信息更加困難,隨著"一帶一路"國家發(fā)展戰(zhàn)略的全面展開,廣大農民對農業(yè)信息服務的需求有增無減。針對傳統(tǒng)推薦系統(tǒng)不能反映用戶興趣遷移、推薦精度不高等問題,提出來基于組合模型的農業(yè)信息推薦系統(tǒng),提高農業(yè)信息推薦的自適應性和準確性。系統(tǒng)結合云計算技術提出一種基于Hadoop+Nutch的全網農業(yè)信息數據倉庫構建方法,通過納入時間權重、情景變更和興趣遷移的優(yōu)化向量空間模型構建了自適應性的用戶興趣模型,以及借助組合神經網絡提高推薦精度提出了組合推薦算法。最后通過評價召回率、準確率等指標表明,基于組合模型的推薦系統(tǒng)可大幅提高推薦準確性和魯棒性。
[Abstract]:Under the environment of big data, it is more difficult for farmers to obtain the information to guide agricultural production on the Internet. With the development of the "Belt and Road" national development strategy, the farmers' demand for agricultural information services is increasing. Aiming at the problems that the traditional recommendation system can not reflect the user's interest transfer and the recommendation accuracy is not high, the agricultural information recommendation system based on the combination model is put forward to improve the adaptability and accuracy of the agricultural information recommendation. Combined with cloud computing technology, the system proposes a method of constructing agricultural information data warehouse based on Hadoop Nutch. An adaptive user interest model is constructed by taking into account the time weight, scenario change and interest transfer optimization vector space model. A combined recommendation algorithm is proposed to improve the accuracy of recommendation by means of combinatorial neural networks. Finally, by evaluating the recall rate and the accuracy rate, it is shown that the recommendation system based on the combination model can greatly improve the accuracy and robustness of the recommendation.
【作者單位】： 中國農業(yè)科學院農業(yè)經濟與發(fā)展研究所;中國農業(yè)科學院農業(yè)環(huán)境與可持續(xù)發(fā)展研究所;
【基金】：中國農業(yè)科學院科技創(chuàng)新工程(編號:ASTIP-IAED-2016-03) 農業(yè)水生產力與水環(huán)境創(chuàng)新團隊項目
【分類號】：S126;TP391.3

【相似文獻】

相關期刊論文 前1條

1 陳清,張宏彥,李曉林;德國蔬菜生產的氮肥推薦系統(tǒng)[J];中國蔬菜;2000年06期

，

本文編號：2180764