Abstract: A method and a system for making contextual recommendations to users on a network-based system. For example, activity associated with a user interacting with a network-based system is tracked. Based, at least in part, on the tracked user activity on the network-based system, a recommendation relationship type is selected. The recommendation relationship type can be either a substitute relationship type or a complement relationship type. A recommended object can be selected based at least in part on the recommendation relationship type and a first object accessed by the user interacting with the network-based system. A recommendation can be generated for the recommended object for presentation to the user interacting with the network-based system.

Abstract: In some example embodiments, a system and method are illustrated to associate an item listing with one or more corresponding images. The system and method include receiving an item listing for an item from a user device. The system and method include generating a similarity score for a respective existing image associated with one or more existing item listings. The generating may be done by comparing the item listing with the existing item listings received from the user device. The similarity score may indicate a degree of similarity between the item listing and the existing item listings associated with the respective existing image. The system and method include proposing a specified number of existing images with the highest similarity scores to the user device. The system and method further include associating the item listing with one or more of the specified number of existing images accepted by the user device.

Abstract: A method and a system for making contextual recommendations to users on a network-based system. For example, activity associated with a user interacting with a network-based system is tracked. Based, at least in part, on the tracked user activity on the network-based system, a recommendation relationship type is selected. The recommendation relationship type can be either a substitute relationship type or a complement relationship type. A recommended object can be selected based at least in part on the recommendation relationship type and a first object accessed by the user interacting with the network-based system. A recommendation can be generated for the recommended object for presentation to the user interacting with the network-based system.

Abstract: Techniques for sharing item images based on a similarity score are described. For example, a machine receives an item listing for an item from a user device. The machine generates a similarity score for an existing image corresponding to one or more existing item listings by comparing the item listing received from the user device with the one or more existing item listings. The similarity score may indicate a degree of similarity between the item listing received from the user device and the one or more existing item listings corresponding to the existing image. The machine selects one or more existing images corresponding to the one or more existing item listings based on the one or more existing images having corresponding similarity scores that exceed a threshold value. The machine transmits a communication to the user device in response to the receiving of the item listing from the user device. The communication may include the one or more existing images.

Abstract: A method and a system for making contextual recommendations to users on a network-based system. For example, a first and second related category are selected and a relationship score is calculated representing an aspect of the relationship between the first and second related categories. A relationship type is selected to represent the relationship between the first and second categories using the relationship score. The relationship type is one of either a complement or a substitute. A complement relationship indicates a complementary relationship between the first and second related categories. While a substitute relationship indicates a substitutionary relationship between the first and second categories. The relationship type is stored associated with the first and second related categories. Using the selected relationship type, a recommendation is provided to a user on the network-based system.

Abstract: Embodiments for recommending gifts to users for their gift recipients over a network are described. The gift recommendation system may retrieve gender and age information of a gift recipient, for example, via a graphical user interface, and then may automatically present to the user a list of predetermined number of gift categories selected based on the gender and age information of the gift recipient. Once a gift category is selected from the presented list of predetermined amount of gift categories, the gift recommendation system may automatically present to the user a list of gift items belonging to the respective selected gift categories.

Abstract: A method and a system for making contextual recommendations to users on a network-based system. For example, a first and second related category are selected and a relationship score is calculated representing an aspect of the relationship between the first and second related categories. A relationship type is selected to represent the relationship between the first and second categories using the relationship score. The relationship type is one of either a complement or a substitute. A complement relationship indicates a complementary relationship between the first and second related categories. While a substitute relationship indicates a substitutionary relationship between the first and second categories. The relationship type is stored associated with the first and second related categories. Using the selected relationship type, a recommendation is provided to a user on the network-based system.

Abstract: A method and a system for making contextual recommendations to users on a network-based system. For example, a first and second related category are selected and a relationship score is calculated representing an aspect of the relationship between the first and second related categories. A relationship type is selected to represent the relationship between the first and second categories using the relationship score. The relationship type is one of either a complement or a substitute. A complement relationship indicates a complementary relationship between the first and second related categories. While a substitute relationship indicates a substitutionary relationship between the first and second categories. The relationship type is stored associated with the first and second related categories. Using the selected relationship type, a recommendation is provided to a user on the network-based system.

Abstract: Some embodiments provide personalized search services. The personalized search services operate to contextualize what is being searched for and to provide at least some search results in that context that are specific to the user to whom the results are to be provided.

Abstract: A computerized method and a system for providing geographic demand distribution and forecast are provided. Example embodiments may include receiving a criterion to identify an item and searching a database for data associated with a geographic distribution of demand for the item identified by the criterion. The computerized method may also include presenting the geographic distribution of the demand for the item on a map. The map may display multiple geographic areas; each one of the multiple geographic areas may be displayed with a first and a second attribute. The first and the second attributes may be associated with the demand.

Abstract: A method and a system for making contextual recommendations to users on a network-based system. For example, a first and second related category are selected and a relationship score is calculated representing an aspect of the relationship between the first and second related categories. A relationship type is selected to represent the relationship between the first and second categories using the relationship score. The relationship type is one of either a complement or a substitute. A complement relationship indicates a complementary relationship between the first and second related categories. While a substitute relationship indicates a substitutionary relationship between the first and second categories. The relationship type is stored associated with the first and second related categories. Using the selected relationship type, a recommendation is provided to a user on the network-based system.

Abstract: A method and a system are disclosed for predicting sales of item listings on a network-based system. For example, historical transaction data generated by the network-based system is accessed to create a prediction model. A feature predictive of an item being sold through the network-based system is selected. A training set is created by extracting the predictive feature from the historical transaction data. The prediction model is trained based on the training data set to predict the probability of an item listing being sold through the network-based system. The prediction model is used to rank search results, and the search results can be presented through the network-based system.

Abstract: Embodiments for recommending gifts to users for their gift recipients over a network are described. For example, a gift recommendation system may collect a set of historical transaction data of a plurality of transactions, and organize the set of historical transaction data based on information related to buyer gender, buyer age, item category, and transaction date of each transaction, in which the list of predetermined amount of gift categories are ordered based on an entire item purchase amount of each gift category. The gift recommendation system may retrieve gender and age information of a gift recipient, for example, via a graphical user interface, and then may automatically present to the user a list of predetermined amount of gift categories selected from the organized set of historical transaction data based on the gender and age information of the gift recipient.

Abstract: Methods and systems to suggest information to be used with an item being listed for sale may include analyzing stored search queries and identifying co-occurring terms or terms based on their desirability scores (or desirable terms). The co-occurring terms and the desirable terms may be identified based on their frequency of use in the search queries and/or in the title information or other information associated with items offered for sale.

Abstract: A system and method for personalized search. The system includes a search engine that receives search input from a user and a user identification and generates a search result based on the search input; and a profiling engine to gather profile data, generate a user profile associated with a user, and rank the search result personalized to the specific user using the user profile.

Abstract: A computerized method and a system for comparing regional demand and supply are provided. Example embodiments may include receiving a criterion to identify an item and searching a database for data associated with demand and/or supply for the item identified by the criterion. The computerized method may also include presenting a geographic distribution of demand, supply, and/or net-demand for the item on a map. The map may display multiple geographic areas; each one of the multiple geographic areas may be displayed with a first and a second attribute. The first and the second attributes may be associated with the demand, supply, and/or the net-demand.

Abstract: In some example embodiments, a system and method are illustrated to associate an item listing with one or more corresponding images. The system and method include receiving an item listing for an item from a user device. The system and method include generating a similarity score for a respective existing image associated with one or more existing item listings. The generating may be done by comparing the item listing with the existing item listings received from the user device. The similarity score may indicate a degree of similarity between the item listing and the existing item listings associated with the respective existing image. The system and method include proposing a specified number of existing images with the highest similarity scores to the user device. The system and method further include associating the item listing with one or more of the specified number of existing images accepted by the user device.

Abstract: A computerized method and a system for providing geographic demand distribution and forecast are provided. Example embodiments may include receiving a criterion to identify an item and searching a database for data associated with a geographic distribution of demand for the item identified by the criterion. The computerized method may also include presenting the geographic distribution of the demand for the item on a map. The map may display multiple geographic areas; each one of the multiple geographic areas may be displayed with a first and a second attribute. The first and the second attributes may be associated with the demand.