Ebay, Incorporated

Verity, Inc.

Business Objects S.a.

Alpha Kamchiu Luk

Jianchang Mao

Sumit Taank

Christina Yip Chung

David Goldberg

Michael Ching

Jinhui Liu

Shervin Moghaddam

Vinayak Agarwal

Ben Chisell

Johannes Braun

Tom Pugh

Tom Wagstaff

Gajanan Adalinge

Vamsi Vutukuru

Disclosed are systems, methods, and non-transitory computer-readable media for message timing optimization. A message timing optimization system determines optimized times to transmit messages to users based on historical transaction data. For example, the message timing optimization system may determine an optimal time to transmit a recommendation message to a user to perform a subsequent, such as repurchasing an item, refreshing a password, and the like. The message timing optimization system uses historical transaction data describing previous transactions performed by the user and/or other users to determine probability values indicating the likelihood that a user will perform a subsequent action at various time periods after the user performed an initial transaction.

System and methods for message timing optimization

A system may receive information representing a first item and an item price of the first item from a first user device operated by a buyer. The first item may be available for sale by a merchant of a brick-and-mortar store. A plurality of sellers may be identified, the plurality of sellers may list a second item as being available for sale at the calculated reduced item price in an item listing for the second item. Moreover, the second item may be similar to the first item. A reduced item price may be calculated based on the item price of the first item received from the user device, the reduced item price representing a price lower than the item price of the first item. The item listing for the second item available for sale may be presented to the first user device operated by the buyer.

Dynamically generating a reduced item price

Systems and methods to predict bidding behavior are described. The system identifies a listing that includes listing information that describes an item that is being auctioned on a network-based marketplace. The system further identifies bid classification information based on the number of bids received for the item. Finally, the system predicts whether no more bids are expected to be received for the item based on the classification information.

Systems and methods to predict bidding behaviour

A system to generate an index for a closest match search is described. The system receives a corpus of information that includes member information. The system parses the member information to generate signatures for each of the members and stores the signatures in the index. The signatures are unique to the members. Accordingly, the signatures signify the respective members. The system subsequently utilizes the index to identify input information that matches signatures in the index to identify a closest match of the input information to one or more members in the corpus of information.

Methods and systems for a closest match search

A system to generate an index for a closest match search is described. The system receives a corpus of information that includes member information. The system parses the member information to generate signatures for each of the members and stores the signatures in the index. The signatures are unique to the members. Accordingly, the signatures signify the respective members. The system subsequently utilizes the index to identify input information that matches signatures in the index to identify a closest match of the input information to one or more members in the corpus information.

Methods and systems for a closet match search

An automatic rule generation system generates rules for fact extraction. A rule generation module receives a sample and generates a rule from the sample. A rule relaxation module generates a relaxed rule from the rule. A rule testing module generates a reverse index from a corpus, applies the relaxed rule to the reverse index, and generates text segments. An information extraction module generates modified text segments from the relaxed rule and the text segments. A candidate suggestion module performs a candidate generation process: if the candidate generation process generates no candidates, the candidate suggestion module signals the rule relaxation module to generate a further relaxed rule to use as the relaxed rule. A user evaluates a candidate and provides the candidate as an additional sample for the automatic rule generation system to generate another rule to use as the rule. As a result of performing these actions iteratively, the rule is eventually generated and relaxed to result in an appropriate rule to use for fact extraction.

System and method of machine-aided information extraction rule development

The invention is a method, system and computer program for automatically discovering concepts from a corpus of documents and automatically generating a labeled concept hierarchy. The method involves extraction of signatures from the corpus of documents. The similarity between signatures is computed using a statistical measure. The frequency distribution of signatures is refined to alleviate any inaccuracy in the similarity measure. The signatures are also disambiguated to address the polysemy problem. The similarity measure is recomputed based on the refined frequency distribution and disambiguated signatures. The recomputed similarity measure reflects actual similarity between signatures. The recomputed similarity measure is then used for clustering related signatures. The signatures are clustered to generate concepts and concepts are arranged in a concept hierarchy. The concept hierarchy automatically generates query for a particular concept and retrieves relevant documents associated with the concept.

System and method for automatically discovering a hierarchy of concepts from a corpus of documents

The present invention provides a method, system and computer program for naming a cluster, or a hierarchy of clusters, of words and phrases that have been extracted from a set of documents. The invention takes these clusters as the input and generates appropriate labels for the clusters using a lexical database. Naming involves first finding out all possible word senses for all the words in the cluster, using the lexical database; and then augmenting each word sense with words that are semantically similar to that word sense to form respective definition vectors. Thereafter, word sense disambiguation is done to find out the most relevant sense for each word. Definition vectors are clustered into groups. Each group represents a concept. These concepts are thereafter ranked based on their support. Finally, a pre-specified number of words and phrases from the definition vectors of the dominant concepts are selected as labels, based on their generality in the lexical database. Therefore, the labels may not necessarily consist of the original words in the cluster. A hierarchy of clusters is named in a recursive fashion starting from leaf clusters. Dominant concepts in child clusters are propagated into their parent to reduce the labeling complexity of parent clusters.

Method and system for naming a cluster of words and phrases

A method of identifying features for a classifier includes identifying a set of elements that share a common characteristic, and then identifying a subset of elements within that set which share a second characteristic. Features are then selected that are more commonly possessed by the elements in the subset than the elements in the set but excluding the subset, and that are more commonly possessed by the elements in the set but excluding the subset, as compared to the elements outside the set. A further method of identifying features for a classifier includes defining a list of features, selecting a first feature from that list, identifying a set of elements that possess that first feature, and then identifying a subset of elements within that set which possess any other feature. A feature is then selected that is more commonly possessed by the elements in the subset than the elements in the set but excluding the subset, and that is more commonly possessed by the elements in the set but excluding the subset, as compared to the elements outside the set. If this feature is not already in the list of features, it is added to it. Another feature that has not already been selected is chosen from the list, and the process is repeated using this feature. This continues until every feature in the list of features has been selected.

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San Jose, CA, United States of America

Alpha Kamchiu Luk