基於序列信任模式之文件推薦

Abstract

協同式過濾推薦已廣泛應用在不同領域上，且有效解決資訊量過大的問題。該方法最主要精神是尋求相似興趣使用者以進行推薦。最近開始有學者提出以信任機制導入協同式過濾推薦以增加推薦結果的準度與可信度。而計算信任程度的方法，則是有學者提出以過去預測評分的準確度來當作衡量信任程度的機制，如果一個使用者在過去推薦的準確度越高，則被認為越值得信任。然而到目前為止，鮮少有相關研究有考慮到序列式信任計算方式。本研究提出的方法，考量了使用者對文章評分的先後順序而導出的信任程度。在知識密集的環境裡，使用者通常會存取不同的文章以滿足其在不同時間點的資訊需求，而此過程就形成了文章序列。本研究所提的序列式信任計算方法涵蓋了兩個因素，分別是時間因素與文件內容相似度因素。而在推薦的程序中則是將序列式信任帶入協同式過濾推薦模式進行對使用者評分的預測。最後透過實驗結果來印證所提的方法的確有效提高推薦的準確度。

Collaborative Filtering (CF) recommender systems have emerged in various applications to support item recommendation, solving the information-overload problem by suggesting items of interest to users. Recently, trust-based recommender systems have incorporated the trustworthiness of users into CF techniques to improve the quality of recommendation. They propose trust computation models to derive the trust value based on users’ past ratings on items. A user is more trustworthy if s/he has contributed more accurate predictions than other users. Nevertheless, none of them derive the trust value based on a sequence of user’s ratings on items. We propose a sequence-based trust model to derive the trust value based on users’ sequences of ratings on documents. In knowledge-intensive environments, users normally have various information needs in accessing required documents over time, producing a sequence of documents ordered according to their access time. The model considers two factors - time factor and document similarity - in computing the trustworthiness of users. The proposed model is incorporated into a standard collaborative filtering method to discover trustworthy neighbors for making predictions. The experiment result shows that the proposed model can improve the prediction accuracy of CF method compared with other trust-based recommender systems.