A Framework for Using Co-Clustering Algorithms to Recommend Smartphone Apps

近年來，智慧型手機(Smartphone)的銷量超過其他型式手機。智慧型手機具有更先進、更開放的行動作業系統，可允許使用者自行安裝應用程式軟體(Application)來擴充手機功能。目前市面上的應用程式數量非常龐大，在眾多的應用程式和有限的時間下，使用者不太可能將所有的應用程式下載試用，所以對使用者而言，找出自己所想要和需要的應用程式，是個困難的問題。推薦系統可依照使用者的喜好，或是準備推薦項目的相似程度來做推薦，讓使用者能較快得到想要的資訊，目前主要的方式有協同過濾(Collaborative Filtering, CF)、內容過濾(Content-Based Filtering, CBF)，還有結合前述兩種方式的混和式推薦(Hybrid Approach)。 本研究所使用的資料集是由政治大學資訊科學系所開發的實驗平台蒐集而來。資料以側錄的方式，將使用者實際操作手機應用程式的狀況記錄下來，其中包含了25位使用者和1125個應用程式。我們將原始資料集以三種方式整理成三個資料集：一、是否使用應用程式；二、使用應用程式的次數；三、使用應用程式的頻率，其值表示使用者在該應用程式的使用狀況。我們並將資料分成前段與後段時間兩部分，以前段時間的資料當作基準，推薦最多同群使用者使用的應用程式、同群使用者使用次數最多的應用程式，以及同群使用者最常使用的應用程式，然後以後段時間的資料做驗證，計算推薦結果的準確率與召回率加以比較。 我們使用知名的Information Theoretic Co-Clustering Algorithm和兩種基於Minimum Squared Residue Co-Clustering Algorithm的演算法將使用者與應用程式分群，利用分群結果做計算，推薦應用程式給使用者。實驗發現三種演算法在第一個資料集的準確率與召回率表現最好，此資料集以0和1的值，來紀錄使用者在各應用程式的使用狀況。實驗比較三個演算法的結果，在大部分的情況之下，一個基於Minimum Squared Residue Co-Clustering Algorithm的演算法，給出的結果較好。 此外，我們也發現應用程式開發者將應用程式上架提供下載時，以個人主觀想法對該應用程式定義其分類，與我們利用雙分群方法，以使用者實際操作的情況將應用程式分類的結果有些差異，或許在Google Play的分類上可做調整。 本研究提出推薦系統的框架具有彈性，未來可以使用不同的雙分群演算法做分群，也能套用其他的推薦方式。With the rapid evolution of smartphone devices, tens of thousands applications have been supplied on online stores such as App Store (operated by Apple Inc.) and Google Play (operated by Google Inc.). Since there are many applications, recommending applications to users becomes an important topic. In this thesis, we present a framework for using a co-clustering algorithm to recommend applications to users. Recommendations are a part of everyday life. People usually rely on some external knowledge to make informed decisions about a particular artifact or action. Using recommender systems is one of general approaches that help people make decisions. There are three common types of recommender systems, namely collaborative filtering, content-based filtering, and hybrid recommender systems. In this thesis, we use the dataset that was collected by a tool developed by the Department of Computer Science at the National Chengchi University. It recorded the users’ behavior when they were using their smartphones. We transform the original dataset into three types of datasets: 1) indicating whether a user used an application; 2) indicating the number of uses made by a user for an application; 3) indicating the frequency of uses made by a user for an application. Furthermore, we divide each dataset into two parts: The first part containing data for the early time period is used as the recommending base, and the second part containing data for the late time period is used for verifying the results. We utilize three famous co-clustering algorithms, which are the Information Theoretic Co-Clustering Algorithm and two algorithms based on the Minimum Squared Residue Co-Clustering Algorithm, in the proposed framework. According to the clusters given by a co-clustering algorithm, we recommend top five applications to each user by referring to the maximum number of users, the maximum number of uses, and the most frequently used applications that are in the same cluster. We calculate the precision and recall values to compare the results. From the experimental results, we find that the best result corresponds to the first type of dataset and also that one of the algorithms based on the Minimum Squared Residue Co-Clustering Algorithm is better than the other two algorithms in terms of the precision and recall values. From the clusters of applications, we obtain some interesting insights into the categories of applications. The categories of applications are set by their developers, but the users may not totally agree with the settings. There might be space for improvement for the categories of applications on the online store. In the future, we can utilize different co-clustering algorithms and other recommended methods in the proposed framework