Accelerating cross-project knowledge collaboration using collaborative filtering and social networks
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Correlation-based content adaptation for mobile web browsing
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Recommendation method that considers the context of product purchases
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Correlation-based content adaptation for mobile web browsing
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Recommendation-based editor for business process modeling
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Since many of today's application software provideusers with too many functions, the users sometimes cannotfind the useful functions. This paper proposes arecommendation system based on a collaborative filteringapproach to let users discover useful functions at low costfor the purpose of improving the user's productivity inusing application software. The proposed systemautomatically collects histories of software functionexecution (usage histories) from many users through theInternet. Based on the collaborative filtering approach,collected histories are used to recommend the user a setof candidate functions that may be useful to the individualuser. This paper illustrates conventional filteringalgorithms and proposes a new algorithm suitable forrecommendation of software functions. The result of anexperiment with a prototype recommendation systemshowed that the average ndpm of our algorithm wassmaller than that of the conventional algorithms; and, italso showed that the standard deviation of ndpm of ouralgorithm was smaller than that of the conventionalalgorithms. Furthermore, while every conventionalalgorithm had a case whose recommendation was worsethan the random algorithm, our algorithm did not.