Abstract: The present disclosure discloses a method and a system of displaying cross-website information. The method includes obtaining, by a first server of a first website, product information under a category of at least one desired product of a first user. Next, the first server obtains information of similar product units under a product category of a second server, where a similar product unit includes a set of displayable products. The first server then computes degrees of similarity between the product information under the category of the at least one desired product and respective product information of the similar product units. For the product information under the category of the at least one desired product, the first server selects a predetermined number of top similar product units in a descending order of the degrees of similarity, and renders products included in the predetermined number of top similar product units as products to be displayed.

Abstract: Data processing includes accessing a product category tree, the product category tree comprising a plurality of hierarchical levels. A leaf category level of the plurality of hierarchical levels comprises a leaf category node. The leaf category node includes product information. The product information comprises a plurality of product attribute parameters. The plurality of product attribute parameters comprises standard product information. Data processing further includes selecting, among the plurality of product attribute parameters that correspond to the leaf category node, a representative product attribute parameter that is representative of the product information; and partitioning standard product information of the leaf category node to obtain a plurality of sets using the representative product attribute parameter, wherein each of the plurality of sets includes at least some of the standard product information.

Abstract: Classifying a user into a user group is described, including: obtaining action record information associated with a user with respect to a plurality of information categories; determining a preference value for at least one of the plurality of information categories based at least in part on the action record information; determining a demand preference dispersion value for the user based at least in part on the preference value, wherein the demand preference dispersion value represents a degree to which the user has dispersed interest across the plurality of information categories; and determining whether the user is to be associated with a demand-focused user group based at least in part on the demand preference dispersion value.

Abstract: The present disclosure discloses a method and a system of displaying cross-website information. The method includes obtaining, by a first server of a first website, product information under a category of at least one desired product of a first user. Next, the first server obtains information of similar product units under a product category of a second server, where a similar product unit includes a set of displayable products. The first server then computes degrees of similarity between the product information under the category of the at least one desired product and respective product information of the similar product units. For the product information under the category of the at least one desired product, the first server selects a predetermined number of top similar product units in a descending order of the degrees of similarity, and renders products included in the predetermined number of top similar product units as products to be displayed.

Abstract: Embodiments of the present application relate to a personalized information pushing method, a personalized information pushing device, and a computer program product for pushing personalized information. A personalized information pushing method is provided. The method includes retrieving network behavior data related to access operations performed by user terminals, determining a numerical value of a degree of correlation between a user terminal and a plurality of information providing terminals having a correlation within a set time window based on the network behavior data, retrieving information providing terminals corresponding to a first user terminal to form a first data set based on the numerical values, retrieving information of the information providing terminals from the first data set to generate a first information, and pushing the first information to the first user terminal.

Abstract: The present disclosure discloses a method and a system of displaying cross-website information. The method includes obtaining, by a first server of a first website, product information under a category of at least one desired product of a first user. Next, the first server obtains information of similar product units under a product category of a second server, where a similar product unit includes a set of displayable products. The first server then computes degrees of similarity between the product information under the category of the at least one desired product and respective product information of the similar product units. For the product information under the category of the at least one desired product, the first server selects a predetermined number of top similar product units in a descending order of the degrees of similarity, and renders products included in the predetermined number of top similar product units as products to be displayed.

Abstract: Embodiments of the present application relate to a personalized information pushing method, a personalized information pushing device, and a computer program product for pushing personalized information. A personalized information pushing method is provided. The method includes retrieving network behavior data related to access operations performed by user terminals, determining a numerical value of a degree of correlation between a user terminal and a plurality of information providing terminals having a correlation within a set time window based on the network behavior data, retrieving information providing terminals corresponding to a first user terminal to form a first data set based on the numerical values, retrieving information of the information providing terminals from the first data set to generate a first information, and pushing the first information to the first user terminal.

Abstract: Data processing includes accessing a product category tree, the product category tree comprising a plurality of hierarchical levels. A leaf category level of the plurality of hierarchical levels comprises a leaf category node. The leaf category node includes product information. The product information comprises a plurality of product attribute parameters. The plurality of product attribute parameters comprises standard product information. Data processing further includes selecting, among the plurality of product attribute parameters that correspond to the leaf category node, a representative product attribute parameter that is representative of the product information; and partitioning standard product information of the leaf category node to obtain a plurality of sets using the representative product attribute parameter, wherein each of the plurality of sets includes at least some of the standard product information.

Abstract: Data processing includes accessing a product category tree, the product category tree comprising a plurality of hierarchical levels. A leaf category level of the plurality of hierarchical levels comprises a leaf category node. The leaf category node includes product information. The product information comprises a plurality of product attribute parameters. The plurality of product attribute parameters comprises standard product information. Data processing further includes selecting, among the plurality of product attribute parameters that correspond to the leaf category node, a representative product attribute parameter that is representative of the product information; and partitioning standard product information of the leaf category node to obtain a plurality of sets using the representative product attribute parameter, wherein each of the plurality of sets includes at least some of the standard product information.

Abstract: Data processing includes accessing a product category tree, the product category tree comprising a plurality of hierarchical levels. A leaf category level of the plurality of hierarchical levels comprises a leaf category node. The leaf category node includes product information. The product information comprises a plurality of product attribute parameters. The plurality of product attribute parameters comprises standard product information. Data processing further includes selecting, among the plurality of product attribute parameters that correspond to the leaf category node, a representative product attribute parameter that is representative of the product information; and partitioning standard product information of the leaf category node to obtain a plurality of sets using the representative product attribute parameter, wherein each of the plurality of sets includes at least some of the standard product information.

Abstract: Data processing includes accessing a product category tree, the product category tree comprising a plurality of hierarchical levels. A leaf category level of the plurality of hierarchical levels comprises a leaf category node. The leaf category node includes product information. The product information comprises a plurality of product attribute parameters. The plurality of product attribute parameters comprises standard product information. Data processing further includes selecting, among the plurality of product attribute parameters that correspond to the leaf category node, a representative product attribute parameter that is representative of the product information; and partitioning standard product information of the leaf category node to obtain a plurality of sets using the representative product attribute parameter, wherein each of the plurality of sets includes at least some of the standard product information.

Abstract: The present disclosure discloses a method and a system of displaying cross-website information. The method includes obtaining, by a first server of a first website, product information under a category of at least one desired product of a first user. Next, the first server obtains information of similar product units under a product category of a second server, where a similar product unit includes a set of displayable products. The first server then computes degrees of similarity between the product information under the category of the at least one desired product and respective product information of the similar product units. For the product information under the category of the at least one desired product, the first server selects a predetermined number of top similar product units in a descending order of the degrees of similarity, and renders products included in the predetermined number of top similar product units as products to be displayed.

Abstract: The present disclosure and data processing technology discloses a method and apparatus of forecasting repurchase inclinations of one or more clients. The disclosed technique improves the forecast accuracy of the clients' repurchasing inclination. The method includes: retrieving a list of a plurality of target clients from a specified storage location; determining historical benefits and a variation trend in historical benefits for at least one client based on respective historical benefits within a specified range of time; determining a purchasing power parameter and a degree of maturity for the client; computing a comfort level score for the client based on the respective historical trends, variation trend in historical benefits, purchasing power parameter, and degree of maturity; and determining a list of clients with repurchase inclination, the list of clients with repurchase inclination including those clients having a respective comfort level score satisfying a threshold condition.

Abstract: Determining which product information to send to a user based on a determined preference value is disclosed, including: determining pieces of user action data that are associated with a current information collection period, the user, and the product category; determining a new visit quantity associated with the current information collection period; determining an updated cumulative visit quantity associated with the product category based at least in part on the new visit quantity associated with the current information collection period; determining a total duration value associated with the product category based at least in part on a duration value associated with the current information collection period; determining a visit interval value associated with the product category and the current information collection period; and determining a long-term preference value associated with the user for the product category.

Abstract: Determining one or more preferred categories for a user is disclosed, including: determining a plurality of access attribute values corresponding to a plurality of types of access attributes associated with an access of the website by the current user; determining a plurality of categories corresponding to the plurality of access attribute values based at least in part on stored corresponding relationships between categories and access attribute values, wherein at least a portion of the determined plurality of categories comprises one or more preferred categories from which one or more products are configured to be recommended to the current user; and presenting product information associated with the one or more preferred categories.

Abstract: Data processing includes accessing a product category tree, the product category tree comprising a plurality of hierarchical levels. A leaf category level of the plurality of hierarchical levels comprises a leaf category node. The leaf category node includes product information. The product information comprises a plurality of product attribute parameters. The plurality of product attribute parameters comprises standard product information. Data processing further includes selecting, among the plurality of product attribute parameters that correspond to the leaf category node, a representative product attribute parameter that is representative of the product information; and partitioning standard product information of the leaf category node to obtain a plurality of sets using the representative product attribute parameter, wherein each of the plurality of sets includes at least some of the standard product information.

Abstract: Matching text sets is disclosed, including: extracting a text set from data associated with a current period; storing the text set with a plurality of text sets; extracting a keyword from the text set; determining a weight value associated with the keyword associated with the text set; determining a degree of similarity between the text set and another text set based at least in part on a weight value associated with the keyword associated with the text set and a weight value associated with a keyword associated with the other text set; and determining whether the text set is related to the other text set based at least in part on the determined degree of similarity.

Abstract: A technique of product recommendations is disclosed, including: determining user characteristic information and product characteristic information for a user; determining at least one of a basic recommended product set and an auxiliary recommended product set for the user; receiving an indication associated with a type of the user's network operation; generating product recommendations based at least in part on type of the user's network operation and at least one of the basic recommended product set and the auxiliary recommended product set for the user; and presenting the generated product recommendations.

Abstract: Product recommendation is disclosed, including retrieving user behavior data associated with a predetermined statistical period; sorting the user behavior data into one or more groups of data corresponding to one or more types of products based at least in part on associated product identifiers; determining a plurality of interest levels associated with the predetermined statistical period for at least one or more groups of data; determining a plurality of purchase peak probabilities using at least the plurality of interest levels, wherein a purchase peak probability is associated with a predicted likelihood of user interest in receiving recommendations associated with a type of product; ranking at least a portion of the plurality of purchase peak probabilities in response to receipt of an indication to present recommendation information; and presenting recommendation information based at least in part on the ranked at least portion of the plurality of purchase peak probabilities.