Abstract: Some embodiments can provide a user matching system configured to match a list of one or more users to a given user. The user matching system can be configured to employ a stage learning process including a user compatibility learning stage, an affinity learning stage, and a match optimization stage. In various exemplary implementations, various user data regarding user preferences, user traits, user behaviors, and/or any other user aspects can be collected. In those implementations, the user matching system is configured to divide the users into different user groups based on the learned user attributes, and determine similarities among users within a given group based on the user attributes. In this way, one or more users can be identified and can be suggested to the given user based on their similarities to the given user.

Abstract: A system for facilitating formation and management of a user group is disclosed. The system may be configured to facilitate forming of a user group for one or more purposes. A set of one or more user group rules may be associated with that user group. When the user group is requested to be formed, the set of group rule(s) may be examined against the request. The examination may include determining whether one or more conditions and/or policies specified in a given one of the group rule(s) are fulfilled. A notification may be generated to notify the initiator whether the one or more conditions are fulfilled to form the user group as requested and/or whether the requested user group can be formed.

Abstract: Some embodiments provide a bidding system configured to estimate a bid for a new item. In some implementations, the bidding system can be configured to build a statistics model for predicting a price difference between the listed price of an item and sold price of the same item. In some embodiments, the bidding system can be configured to train a classification model using extracted features. The prediction can be based on sales information regarding one or more items that were previously sold. In some implementations, building such a statistics model may include extracting features from structured data and as well as unstructured data regarding those previously sold items. Structured data may include one or more classifications of the items that are readily available in a classification system or classification systems. Unstructured data may include text description about those items.

Abstract: Some embodiments provide a recommender system configured to recommend a geographical location to a user is provided. The recommender system may be configured to process unstructured data from various data sources such as social media, planning documents, news media, and/or any other type data sources that may provide certain information, regarding a project. In some exemplary implementations, text mining techniques are configured into the recommender system to process the unstructured data from those data sources and to extract various phrases and/or sentences regarding various geographical locations.

Abstract: Some embodiments can provide a user matching system configured to match a list of one or more users to a given user. The user matching system can be configured to employ a stage learning process including a user compatibility learning stage, an affinity learning stage, and a match optimization stage. In various exemplary implementations, various user data regarding user preferences, user traits, user behaviors, and/or any other user aspects can be collected. In those implementations, the user matching system is configured to divide the users into different user groups based on the learned user attributes, and determine similarities among users within a given group based on the user attributes. In this way, one or more users can be identified and can be suggested to the given user based on their similarities to the given user.

Abstract: Some embodiments can provide a recommender system configured to recommend a new item to a user by estimating a user interest for the item. The recommendation system may be configured to collect data regarding user activities with respect to various existing items. Based on the user activity data, the user interest for the new item may be estimated and a determination may be made whether the new item is to be recommended to the user based on the estimated interest. In one embodiment, the new item's attribute vector is compared with a user vector to estimate the user's interest to the new item. In another embodiment, a score indicating the user's interest may be iteratively estimated through matrix factorization based on the user activity data.