以顯性評價為主之相似性推薦

A Rating-based Similarity Measure for Recommendation Systems

廖品妍(Pin-Yen Liao)

指導教授 ： 李麗華

朝陽科技大學/資訊學院/資訊管理系碩士在職專班/碩士(2010年)

摘要

推薦系統(Recommendation Systems)現今在網際網路的商用行為中，經常被用來協助解決資訊過載(Information Overload)、推薦及預測服務，由過去學者的研究發現推薦系統中最常運用相似度量測(Similarity Measure)方法，來找出相似使用者或尋找具有相似行為之鄰居群(Neighborhood)，而這類相似度量測方法，多半利用使用者對產品的評價(rating)通常為顯性評分值(explicit rating)，來找出使用者間的相似度，藉由相似使用者的資訊對目標使用者做預測推薦，因此一個良好的相似度量測方法，往往是決定推薦成效的關鍵所在。
過去學者常用的相似度量測方法最常見的有皮爾森相關係數(Pearson Correlation Coefficient, PCC)、餘弦相似(Cosine Similarity, COS)、限制性皮爾森相關(Constrained Pearson’s Correlation, CPC)、修正餘弦相似(Adjusted Cosine, ACOS)、PIP相似性及歐幾里德距離(Euclidean Distance, ED)等，這些方法在計算使用者相似度時都會以顯性評分來做為計算的依據，但這些方法都有一些問題，例如：(1)未考慮到正向評分值在推薦時的重要性、(2)當資料量大時則計算相似度時間可能會過長、(3)當評分值與平均值相同時會發生除以零的問題等。
為了改善上述所提的問題，本研究提出一個新的以顯性評分為主之相似度量測方法(Rating-based Similarity Measure, RBSM)，本研究提出以快速的二元相似度量測方法為基礎，以正向評分值做為分析相似度之要項，藉由此方法快速找出具推薦價值之相似使用者及其推薦項目，本研究同時也考量使用者的評分特性，將實驗資料依使用者特性分群之後進行相似度計算，以期求得更準確的使用者相似度。利用本研究所提的顯性評分為主之相似度量測方法，經由初步實驗驗證能提升預測的效能，本研究的初步貢獻為經由實驗驗證本研究所提之方法有較高之準確度，同時可以縮短推薦計算之系統執行時間。

關鍵字

相似度量測 ； 顯性評分 ； 推薦系統

並列摘要

Recommendation systems (RS) are usually used for handling the information overloading, for recommendation, and for prediction, especially under current Internet environment. According to previous studies, the most commonly applied method for RS is the similarity measure. Similarity measure is usually used for finding similarity user or neighbors. To apply similarity measure, one of the commonly approach is to used the user rating, which is also call explicit rating for calculation. The rating difference or distance between the active user and the similar user is used for prediction. Therefore, a good similarity measure can affect the result of RS.
It is noticed that the similarity measures such as Pearson Correlation (PCC), Cosine Similarity (COS), Constrained Pearson’s Correlation (CPC), Adjusted Cosine (ACOS), PIP, or Euclidean Distance (ED) are highly used for finding similar users or similar items. These similarity measures usually rely on a user-item matrix in which the explicit ratings are used for calculation. The outcome of recommendation is usually made based on the information of the similar user (item). Hence, the similarity measure for finding the similar user (item) is critical for RS. But, if we examine the traditional similarity measurements, there exists some problems when applying to the RS. (1)They did not take the positive rating and the co-rating count into consideration. (2) When the rating value is equal to the average rating value, the similarity measure of PCC and COS will encounter the problem of division by zero problem, which will cause system failure. (3) The scalability problem is usually not discussed.
In order to handle these problems, this research proposes A Rating-based Similarity Measure (RBSM). Our method transforms the explicit-ratings into binary and considers both the positive-rating and co-rating count for finding the similar user. A simple similarity computation is proposed to find the neighborhood. For finding similar neighbor efficiently, users are divided, based on the co-rating amount, into three groups i.e., high, medium, and low. From the experiments, it is proved that our method has better outcome in recall, F1 value, and MAE value if compare to the traditional methods. Our results also show that the proposed method can handle the scalability problem.

並列關鍵字

Similarity Measures ； Explicit Rating ； Recommendation Systems

參考文獻

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