A Recommendation System for Software Function Discovery

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Tobias Haynes
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1 A Recommendation System for Software Function Discovery Naoki Ohsugi Software Engineering Laboratory, Graduate School of Information Science, Nara Institute of Science and Technology Tuesday 16 December, International Workshop on Community-Driven Evolution of Knowledge Artifacts

2 Growth of Software Functions Application software is getting more complicated and providing more functions. Total number of menu items (Microsoft Office) Word 2000: 660 Word 2002: 772 Excel 2000: 705 Excel 2002: 792 PowerPoint 2000: 565 PowerPoint 2002: 646 Users can t find useful functions from too many functions. Screenshot of MS-Word of 14

3 Users Could Not Find Some Useful Functions! Number of Functions Subjects: 32 users in our lab. Period: 22 months Total Number of Different Functions Maximum Number of Functions Used Minimum Number of Functions Used Average Number of Functions Used % 10.5% 33.5% 22.8% 21.7% 15.5% 1.4% 1.5% 3.2% 4.8% 3.3% 1.4% 5.4% 3.3% 14.2% 10.4% 10.0% 4.1% Excel2000 Excel2002 PPT2000 PPT2002 Word2000 Word of 14

4 A Recommendation System for Software Function Discovery The system recommends individual users a set of candidate functions, which may be useful. Our solution is a Collaborative Filtering approach. Here s my recommendation: Tools Word Count Insert Date Time Tools Thesaurus Insert Footnote Tools Spelling 21 pts 20 pts 18 pts 18 pts 17 pts 4of 14

5 What is Collaborative Filtering (CF)? Collaborative means using some users knowledge for filtering. Filtering means selecting useful items from large amount of items. Selecting useful items F is good!? F? K K is cool! A B C D E F G H I J K L M N O P Q R S T Using some users knowledge Large amount of items 5of 14

com (Book recommendation system) http://www.amazon.

6 Voting-based Recommendation Systems with CF The systems collect explicit votes as users knowledge. Amazon.com (Book recommendation system) MovieLens (Movie recommendation system) 6of 14

7 Logging Usage as Users Knowledge The proposed system automatically collects the records of executed functions (Usage logs) as users' knowledge. Usage logs are collected from some users via the Internet. Application Software e.g. MS-Word, Excel User The Internet Log Collector VBA Plug-In Usage log as shown below: Server of 2002/02/03 the System18:50:41 Formatting->Font 2002/02/03 18:50:45 File->Save As 7of 14

8 Step1: Computing Similarities Computing similarities between the target user and the other users Function A Function B Function C Function D Function A Function BA Function C Function A Function B Function C Function D Function E Function F Function G Function H Function I Function J Function K User 1 Target user User 2 User 3 User 4 Similar users Dissimilar users 8of 14

9 Step 2: Delivering Knowledge Delivering the useful functions candidate, which were frequently used by the similar users'. Function A Function B Function C Function D Function A Function B Function C Function D Function A Function B Function C Function D Function E Function F Function G Function H Function I Function J Function K User 1 Target user User 2 User 3 User 4 Similar users Dissimilar users 9of 14

10 Conventional Similarity Calculation Calculating Similarities by Correlation Coefficient The dominant frequencies (e.g., Undo or Save ) over-affect similarity computations. Target user User 2 User Undo Save Redo Copy Paste Cut Clear 60% 20% 10% 4% 3% 2% 1% Save Undo Redo Copy Paste Cut Clear 55% 25% 10% 4% 3% 2% 1% Undo Save Clear Cut Copy Paste Redo 60% 20% 6% 5% 4% 3% 2% Correlation based similarity (Range of value [-1.00, +1.00]) 10 of 14

11 Better Similarity Calculation Calculating Similarities by Rank Correlation The dominant frequencies ("Undo" & "Save") do not affect similarity computations. Target user User 2 User Undo Save Redo Copy Paste Cut Clear 60% 20% 10% 4% 3% 2% 1% Save Undo Redo Copy Paste Cut Clear 55% 25% 10% 4% 3% 2% 1% Undo Save Clear Cut Copy Paste Redo 60% 20% 6% 5% 4% 3% 2% Correlation based similarity Rank correlation based similarity of 14

12 Evaluating Accuracy of Recommendation Yao s ndpm measure * Y.Y. Yao, Measuring Retrieval Effectiveness Based on User Preference of Documents, J. of American Society for Information Science, 46, 2, 1995, pp Interview for user System Usage logs 6 users 22 months 1. Function A 2. Function B 3. Function C 4. Function D User s Ideal Recommendation Ndpm [0.0, 1.0] 0.0 is the best 1.0 is the worst Comparison 1. Function A 2. Function B 3. Function C 4. Function D System s Recommendation 12 of 14

13 Experimental Result Ndpm 0.6 Collected usage logs of Ms-Word 2000 Subjects: 6 users in our lab. Period: 22 months Each user s ndpm Average of ndpm 0.5 of ndpm Random User Count Base Case Correlation based Similarity Rank Correlation based Similarity Algorithms 13 of 14

14 Conclusion I proposed a recommendation system to help users discover useful functions. I evaluated the accuracy of recommendation. The result suggested the proposed system has a potential to provide useful recommendation for software function discovery. 14 of 14

Home Moves to opening slide PowerPoint. Moves cursor to the end of a line. End Moves to end of slide show PowerPoint

Home Moves to opening slide PowerPoint. Moves cursor to the end of a line. End Moves to end of slide show PowerPoint Introduction Whether you are working in Microsoft, Microsoft PowerPoint or Microsoft there are helpful keyboard shortcuts you can use. Below is a list of the more common keyboard shortcuts and the program