以偏好導向為相似性量測的協同過濾式推薦

A Preference Based Similarity Measure for Collaborative Filtering Recommendation

蔡美慧(Mei-Huei Tsai)

指導教授 ： 李麗華

朝陽科技大學/資訊學院/資訊管理系/碩士(2010年)

摘要

在現今的電子商務(Electronic Commerce)環境中，協同過濾式推薦技術(Collaborative Filtering Recommendation, CF)經常被建構成推薦系統(Recommendation System, RS)，且運用層面相當廣泛，如電影、書籍等推薦運用。協同過濾式推薦之方法，旨在找出與目標使用者(Active User)有類似偏好(Preference)的相似使用者(Similar User)，利用相似使用者的已知評分(Given Rating)記錄，推測目標使用者對尚未評分的項目是否具有偏好，再找出可能偏好的項目，推薦給目標使用者。為了能找出與目標使用者偏好最相近的相似使用者，過去有學者提出許多的相似度量測(Similarity Measure)及挑選相似使用者的方法，這些方法都能評估使用者之間的關係，並找出偏好相似的使用者。然而，隨著推薦系統運作時間的增加，必須處理的資料也日益漸增，導致傳統相似度量測產生計算耗時的問題，因此將對線上的即時推薦效能造成極大的阻礙。
本研究為了強化協同過濾式推薦的推薦效能(Performance)，提出偏好導向相似性量測(Preference Based Similarity Measure, PBSM)的協同過濾式推薦方法。利用使用者對項目的評分，以二元值區別是否對項目具有偏好，同時針對推薦上的考量，篩選出對推薦結果具有影響力的正向評分使用者，再利用反互斥或邏輯運算(Exclusive-NOR, XNOR)，計算使用者的偏好一致性(Conformity)。
透過本研究的實驗證明，偏好導向相似性量測的協同過濾式推薦與傳統相似度量測之協同過濾式推薦互相比較，偏好導向相似性量測不但改善傳統相似度量測的計算複雜度問題，且能保有預測結果的準確性(Accuracy)。在推薦方面，偏好導向相似性量測的推薦成功率亦優於傳統相似度量測，故能同時提昇協同過濾式推薦之推薦效能，增加推薦準確度。此外，由於計算時間的降低，更能符合線上即時推薦的需求。

關鍵字

使用者偏好 ； 推薦系統 ； 協同過濾式推薦 ； 相似度量測

並列摘要

Collaborative Filtering (CF) recommendation technique is frequently used for building Recommendation Systems (RS). This technique exists for many applications such as recommendations for movies, books, music, and products. The reason of applying CF for recommendations is to find users with similar preferences such that the group of users can be utilized for prediction and the active user’s unrated item is, then, predicted.
To find similar users, the similarity measure must be employed. In the past, many similarity measures are proposed such as Pearson’s Correlation (PCC), Euclidean Distance (ED), Cosine Similarity (COS), or Constrained Pearson’s Correlation (CPC). These methods compute the correlation, distance, or direction for each pair of user’s information. If the number of user is large, the computation will take time. Moreover, as we all know, in e-commerce environment, user information and the amount of users will naturally increase with time. Therefore, a good on-line CF process becomes difficult if the user database grows rapidly.
To improve the performance of CF recommendations, this research proposes the Preference Based Similarity Measure (PBSM) for CF recommendation. The PBSM can distinguish a user’s preferences based on user ratings and can identify the conformity between users’ preferences. This is done by changing user ratings into binary values to represent user’s positive preferences. The proposed method uses the exclusive-NOR (XNOR), the logical operation, to compare the conformity between users’ preferences. Users that have high conformity with the active user are determined to be similar users. The preference of these similar users will be applied for recommendation.
To prove the proposed PBSM can generate better performance, the experiments are designed to allow comparison with traditional similarity measures for CF recommendation. Our experimental results indicate that the proposed PBSM has better prediction outcomes in terms of MAE measure. Also, the PBSM achieves a successful rate which is higher than that of traditional similarity measures. The study proves that the proposed PBSM can enhance the performance of recommendations, and is moreover simple to calculate, which in turn allows for more efficiency than traditional methods.

並列關鍵字

Recommendation Systems (RS) ； Collaborative Filtering (CF) ； Similarity Measure ； User Preference

參考文獻

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