本文選題：判斷聚合模型 + 推薦系統(tǒng)��； 參考：《湖北大學(xué)學(xué)報(bào)(哲學(xué)社會(huì)科學(xué)版)》2016年02期

【摘要】：推薦系統(tǒng)是目前解決用戶信息過載的主要工具,協(xié)同過濾算法是推薦系統(tǒng)中應(yīng)用最為廣泛的技術(shù),它主要依賴用戶已有的歷史數(shù)據(jù)為其尋找有相似的其他用戶,然而,當(dāng)遇到新用戶第一次訪問的情況下,這類技術(shù)一般很難給出恰當(dāng)?shù)耐扑],這就是著名的用戶冷啟動(dòng)問題。運(yùn)用判斷聚合理論的技術(shù)手段把已有用戶的行為數(shù)據(jù)聚合成為集體判斷集,然后將這個(gè)集體判斷集推送給新用戶,新用戶根據(jù)自身的偏好購(gòu)買感興趣的物品,這一方法既解決了新用戶的冷啟動(dòng)問題,又豐富和拓展了推薦系統(tǒng)的功能。
[Abstract]:Recommendation system is the main tool to solve the overload of user information at present. Collaborative filtering algorithm is the most widely used technology in recommendation system. When a new user visits for the first time, it is difficult to give a proper recommendation for this kind of technology, which is the well-known cold start problem. Using the technology of judgment aggregation theory, the behavior data of existing users are aggregated into collective judgment sets, and then the collective judgment sets are pushed to new users, who buy items of interest according to their preferences. This method not only solves the cold start problem of new users, but also enriches and expands the function of recommendation system.
【作者單位】： 西南大學(xué)邏輯與智能研究中心;
【基金】：西南大學(xué)人文社會(huì)科學(xué)重要研究項(xiàng)目(編號(hào):12XDSKZ003)的資助
【分類號(hào)】：B812
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本文編號(hào)：1983428