Disclosed is an invention for methods, processes and systems that, among its enabling features and benefits, enhance retrieval of relevant information over a communication network. For instance, methods, processes and systems for performing annotation of digital information are provided. One method includes searching for items of interest using a search engine. Once the URIs associated with the item of interest are identified, a plurality of attributes are provided that may be associated with each URI. A user may provide the values for the attributes or the system may suggest values for the attributes based on information associated with each URI. Once the attributes and values are assigned, the annotated URI along with the attributes and values is stored. Another method provides for sharing of the annotated information. A user may communicate annotated information to an external storage system for sharing with other users having access to the external storage system. Another method provides for automatic updates of the annotation entries by periodically fetching the digital information associated with each URI and updating the values associated with each attribute. In addition, a system, method or process may provide a more reliable, inclusive, or otherwise effective way of collecting or identifying quality reviews for products, services, and sellers. Furthermore, a system, method or process may enable or otherwise enhance generation of actionable information for online shopping or comparative shopping. Furthermore, according to one embodiment, an interface or protocol that a computer uses to communicate with other computers is associated with a subject matter context. User-level contents or digital resources received across that interface or protocol are then associated with that subject matter context, and the computer may respond accordingly. For instance, a computer may associate a given network port with a subject matter context of shopping, and treat all digital resource requests received on that port as applying to only a shopping subject matter context. A web server may also listen on a network port associated with a subject matter context, thereby contextualizing the overall nature of the web site that the web server hosts.

Methods and apparatus relating to the generation, management and use of taxonomies are described. Over time statistics are collected based on the user interaction and the effectiveness of the taxonomy determined with respect to one or more groups of users. Decisions are automatically made when a new e.g., custom, taxonomy is to be generated for a set of users. Over time multiple custom taxonomies are generated for different user groups with each group of users being provided access to items through the taxonomy which provides the best results for the group. Generated taxonomies are compared and merged or combined when similar to avoid the generation and maintenance of a large number of similar taxonomies.

Generally discussed herein are systems, apparatuses, and methods for providing information regarding a product that is unique to a geographical region. In one or more embodiments a method may include determining a location of a user, receiving information indicative of one or more products unique to a particular geographical region corresponding to the determined location of the user, wherein each of the one or more products are offered for sale in the particular region by one or more merchants, and providing information about at least one of the products and about a merchant, from the one or more merchants, offering that product for sale in the geographical region.

Systems and methods are provided for receiving at least a first query color, and searching an electronic catalog including a plurality of product images for the first query color to determine a similarity measure between the first query color and a product image of a plurality of product images. The similarity measure may be determined by determining a Euclidean distance between values in a three-dimensional color space for the first query color and a target color of the product image, and determining the similarity measure between the query color and the product image by determining a sum of the similarity measures from all target colors on the product image, weighted by the coverage of each target color. The search results may be transmitted based on the searching of the electronic catalog including the plurality of product images for the first query color.

Aspects described herein may allow for generating a customized price rating using a machine learning algorithm. This may have the effect of improving the display of information about merchants by including customized, personalized price ratings that better reflect the tastes and preferences of a user or group of users. According to some aspects, these and other benefits may be achieved by using a machine learning model, trained to receive input corresponding to both user data and merchant data and output an indication of customized price rating(s) for the merchant that is specific to one or more users, and then to generate information about the merchant for display that includes the customized price rating(s).

In a database, product image data is accumulated. A search portion acquires product image data having the image characteristics information that is the same as or similar to the image characteristics information that indicates the characteristics of the image of input image data from the database for the input image data. A search server outputs information on another product that is different from the product corresponding to the product image data together with the product image data acquired by the search portion.

Techniques for creating a user network based on user interest in a product are described. For example, a first search query is received from a first client device. The first client device is associated with a first user. A second search query is received from a second client device. The second client device is associated with a second user. The first search query is matched to a product. The second search query is matched to the product. A user network corresponding to the product is created. The first user is added to the user network based on the matching of the first search query to the product. The second user is added to the user network based on the matching of the second search query to the product.

A system and method of an intelligent shopping infrastructure is provided. A data storage of current inventory from a plurality of merchants is maintained. User preferences that indicate an item affinity of a user are retrieved. Furthermore, a location of the user is detected. Using the detected location, available inventory, and item affinity, an available item from the current inventory of a merchant located within a predetermined distance to the location of the user that match the item affinity of the user is identified. The available item is presented on a user interface of a client device of the user.

A method for categorizing a product, the method including receiving information for the product; inputting the information into a trained machine learning model for a taxonomy tree; receiving a plurality of arrays, each array representing a level in the taxonomy tree and consisting of probabilities for each category represented in the level that the product is categorized in that category; choosing, from a highest level tier array, a category having a highest probability, thereby designating a tier prediction; collecting, from a second level tier array, all children of the tier prediction; determining whether a highest probability from the children of the tier prediction exceeds a threshold, and if yes, choosing the category with the highest probably as a new tier prediction; and repeating the determining; when the threshold is not exceeded or if the tier prediction has no children, and selecting the tier prediction as a predicted category.

A ranking system includes: an exposure module configured to, in response to receiving an input from a computing device via a network, determine, based on an anisotropic intensity value and using a Dynamic Bayesian Network (DBM) exposure model, an exposure value for ranking items for output via the computing device, where the anisotropic intensity value corresponds to a tradeoff between utility to users and fairness to item producers; a ranking module configured to generate a ranking of the items based on the exposure value; and a response module configured to transmit a response including the ranking of the items to the computing device via the network.