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A Recommendation System for Software Function Discovery

A Recommendation System for Software Function Discovery. N aoki Ohsugi Software Engineering Laboratory, Graduate School of Information Science, Nara Institute of Science and Technology. Tuesday 16 December, 2003. International Workshop on Community-Driven Evolution of Knowledge Artifacts.

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A Recommendation System for Software Function Discovery

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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, 2003. International Workshop on Community-Driven Evolution of Knowledge Artifacts

  2. 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 Growth of Software Functions Users can’t find useful functions from too many functions. Screenshot of MS-Word 2002

  3. 10.6% 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% Users Could Not Find Some Useful 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 900 792 772 800 705 Number of Functions 660 700 646 565 600 500 400 300 189 200 147 143 120 83 80 75 67 66 100 38 32 31 26 22 18 12 11 10 0 Excel2000 Excel2002 PPT2000 PPT2002 Word2000 Word2002

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

  5. Selecting useful items F K F K What is Collaborative Filtering (CF)? • “Collaborative” means using some users’ knowledge for filtering. • “Filtering” means selecting useful items from large amount of items. A B C D E F is good! K is cool! ? ? F G H I J K L M N O P Q R S T Using some users’ knowledge Large amount of items

  6. Voting-based Recommendation Systemswith CF • The systems collect explicit votes as users’ knowledge. Amazon.com (Book recommendation system) http://www.amazon.com MovieLens (Movie recommendation system) http://www.movielens.umn.edu

  7. User Usage log as shown below: 2002/02/03 18:50:41 Formatting->Font… 2002/02/03 18:50:45 File->Save As… 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 The Internet Server of the System Log Collector VBA Plug-In

  8. Function A Function A Function C Function A Function B Function C Function D Function A Function B Function C Function D Similar users Dissimilar users Step1: Computing Similarities • Computing similarities between the target user and the other users Function A Function B Function C Function D Function A Function A Function C Function A Function B 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

  9. 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 D Function B Similar users Dissimilar users 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 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

  10. Target user User 2 User 3 1 2 3 4 5 6 7 Undo Save Redo Copy Paste Cut Clear 60% 20% 10% 4% 3% 2% 1% 1 2 3 4 5 6 7 Save Undo Redo Copy Paste Cut Clear 55% 25% 10% 4% 3% 2% 1% 1 2 3 4 5 6 7 Undo Save Clear Cut Copy Paste Redo 60% 20% 6% 5% 4% 3% 2% Correlation based similarity+0.41 +0.97 (Range of value [-1.00, +1.00]) Conventional Similarity Calculation • Calculating Similarities by Correlation Coefficient • The dominant frequencies (e.g., “Undo” or “Save”) over-affect similarity computations.

  11. Target user User 2 User 3 1 2 3 4 5 6 7 Undo Save Redo Copy Paste Cut Clear 60% 20% 10% 4% 3% 2% 1% 1 2 3 4 5 6 7 Save Undo Redo Copy Paste Cut Clear 55% 25% 10% 4% 3% 2% 1% 1 2 3 4 5 6 7 Undo Save Clear Cut Copy Paste Redo 60% 20% 6% 5% 4% 3% 2% Correlation based similarity+0.41 +0.97 Rank correlation based similarity +0.90 +0.05 Better Similarity Calculation • Calculating Similarities by Rank Correlation • The dominant frequencies ("Undo" & "Save") do not affect similarity computations.

  12. Interview for user System 1. Function A 2. Function B 3. Function C 4. Function D 1. Function A 2. Function B 3. Function C 4. Function D Ndpm [0.0, 1.0] 0.0 is the best 1.0 is the worst User’s Ideal Recommendation Comparison System’s Recommendation 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.133-145. Usage logs 6 users 22 months

  13. Experimental Result 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 Ndpm 0.6 0.514 0.5 0.404 0.4 0.396 0.383 0.355 0.3 0.2 Random Base Case Rank Correlation based Similarity Algorithms User Count Correlation based Similarity

  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.

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