Abstract: A project generating method comprises extracting a keyword from a plurality of text files with a specified category, determining whether the keyword is a theme, extracting a geographical name from the text files corresponding to the theme, determining whether to keep the theme according to an internet volume of the theme, filtering a plurality of review files from a review website according to the geographical name, calculating a first ratio therein to determining whether to keep the theme, and generating a project including the geographical name and the theme serving as a recommendation row.

Abstract: A fund tracking system, a fund tracking method and a graphic user interface are provided. The fund tracking method is used to track a target fund. The fund tracking method includes the following steps. Several ETF asset classes are obtained according to a fund benchmark index of the target fund. Several representative ETFs are obtained according to the ETF asset classes. A simulated investment portfolio is generated according to the representative ETFs. Whether the simulated investment portfolio meets a verification condition is verified. If the simulated investment portfolio meets the verification condition, the simulated investment portfolio is outputted.

Abstract: A project generating method comprises extracting a keyword from a plurality of text files with a specified category, determining whether the keyword is a theme, extracting a geographical name from the text files corresponding to the theme, determining whether to keep the theme according to an internet volume of the theme, filtering a plurality of review files from a review website according to the geographical name, calculating a first ratio therein to determining whether to keep the theme, and generating a project including the geographical name and the theme serving as a recommendation row.

Abstract: A decision factors analyzing device and a decision factors analyzing device for analyzing a plurality of decision factors which cause a product of a product type to be purchased are provided. The method includes identifying a plurality of product sequences corresponding to the product type from a plurality of browse history data and a plurality of purchase history data corresponding to a plurality of consumers of a consumer database, wherein each of the product sequences includes a unpurchased product and a purchased product; obtaining a feature sequence according to the produce sequences and a plurality of product information; training a regression model corresponding to the product type according to K decision factors of the feature sequence to obtain an optimized regression model, and obtaining K decision values respectively corresponding to the K decision factors according to the optimized regression model to generate a decision factor sequence corresponding to the product type.

Abstract: A decision factors analyzing device and a decision factors analyzing device for analyzing a plurality of decision factors which cause a product of a product type to be purchased are provided. The method includes identifying a plurality of product sequences corresponding to the product type from a plurality of browse history data and a plurality of purchase history data corresponding to a plurality of consumers of a consumer database, wherein each of the product sequences includes a unpurchased product and a purchased product; obtaining a feature sequence according to the produce sequences and a plurality of product information; training a regression model corresponding to the product type according to K decision factors of the feature sequence to obtain an optimized regression model, and obtaining K decision values respectively corresponding to the K decision factors according to the optimized regression model to generate a decision factor sequence corresponding to the product type.

Abstract: A method for predicting user preference comprising: obtaining a users group with multiple users and a history shopping record associated with multiple goods; selecting a goods parameter, each goods having the goods parameter; selecting a plurality of first values of the goods parameter according to the history shopping record; determining a determining a representative users group with multiple representative users according to the history shopping record and the first values; calculating a correlation between a user and one of the representative users in the representative users group; and predicting the preference of the users according to the correlation and representative users' preference.