Abstract: Content Based Similarity Detection. A computer implemented method includes computing a hash of each word in a collection of books to produce a numerical integer token using a reduced representation and computing an Inverse Document Frequency (IDF) vector comprising the number of books the token appears in, for every token in the collection of books. The method also includes creating a token occurrence count vector for each book in the collection and normalizing the token occurrence count vector using the IDF vector to create a Term Frequency-Inverse Document Frequency (TF-IDF) vector. Further, the method includes reducing each TF-IDF vector by using random projections to obtain a final signature representing each book in the collection, reducing each TF-IDF vector by using random projections to obtain a final signature representing each book in the collection and using a trained machine learning algorithm, determining whether each of the list of candidate books is similar to the target book.

Abstract: A system and method for automatically identifying an electronic document. The method includes accessing, within an electronic device, an electronic document and extracting text from the electronic document. A signature is then determined based on the text of the electronic document and the signature is communicated over a communication channel. The method further includes receiving an identifier of the electronic document over the communication channel. In one embodiment, the identifier is determined by a server matching the signature against a signature library. The method further includes receiving a bookmark associated with the electronic document.

Abstract: Systems and methods for automatically generating replacement query terms that offer improved search efficiency. Recommended search query terms are generated based on statistic information derived from empirical data recording prior search sessions with respect to searching on a search engine. A query term entered later in a search session is treated as a possible replacement term for a query term entered earlier in the same session. Upon receiving an initial query term in a new search session, the replacement terms of the initial query term are identified from the empirical data and evaluated as candidates for replacing the initial query term in the new search session. The evaluation is based on the respective occurrence rates that the candidates are used as replacement terms for the initial query term in the empirical data, and based on the respective conversion rates of the candidates.

Abstract: System and method for determining book similarities based on text content and thereby discovering related books for recommending to customer-users. Each book is associated with a probability distribution on a set of topics that is derived from text content of the book against the set of topics. The pair-wise distances of the probability distributions between corresponding books are computed to derive similarities thereof. The probability distributions may be generated by leveraging a text topic model that defines a set of topics, a respective set of relevant terms under each topic, and a probability distribution on each set of relevant terms. The text topic model may be automatically generated by processing content of a corpus of training books via a training process.

Abstract: Systems and methods for automatically generating a user-specific recommendation list with diversified as well as relevant items. Recommendation categories and sample items for each category are diversified by virtue of probability distribution. Items in a user' collection can be clustered based on category in accordance with a similarity measure. The category probability distribution values may be derived from characteristics suggesting respective categories' importance or preference to the user. For each selected category, the sample probability distribution values may be derived from similarities of each candidate to the selected category.

Abstract: System and method for identifying commodities for recommendation to a target user based on side information that is pertinent to a specific target user and extrinsic to the commodities. Training data is exploited to derive a statistical correlation between users' side information of a plurality of attributes with a plurality of commodities. The training data includes side information of a set of training users and a plurality of commodities towards which the training users have manifested preference. Based on the derived statistical correlation and the target user's side information, a probability distribution representing the target user's tendency to purchase the plurality of commodities can be determined. As a result, a list of commodities can be automatically selected from the plurality of commodities and recommended to the target user.

Abstract: A system and method for automatically identifying an electronic document. The method includes accessing, within an electronic device, an electronic document and extracting text from the electronic document. A signature is then determined based on the text of the electronic document and the signature is communicated over a communication channel. The method further includes receiving an identifier of the electronic document over the communication channel. In one embodiment, the identifier is determined by a server matching the signature against a signature library. The method further includes receiving a bookmark associated with the electronic document.

Abstract: Content Based Similarity Detection. A computer implemented method includes computing a hash of each word in a collection of books to produce a numerical integer token using a reduced representation and computing an Inverse Document Frequency (IDF) vector comprising the number of books the token appears in, for every token in the collection of books. The method also includes creating a token occurrence count vector for each book in the collection and normalizing the token occurrence count vector using the IDF vector to create a Term Frequency-Inverse Document Frequency (TF-IDF) vector. Further, the method includes reducing each TF-IDF vector by using random projections to obtain a final signature representing each book in the collection, reducing each TF-IDF vector by using random projections to obtain a final signature representing each book in the collection and using a trained machine learning algorithm, determining whether each of the list of candidate books is similar to the target book.

Abstract: A server generates item recommendation lists for users, whose behaviors are registered related to user library items and assigned relevance in relation to available inventory items. First, similarity computed between any pair of inventory items based on user-specific relevance factors. Second, similarity is measured between inventory items by over content and/or metadata corresponding to the inventory items. Overall similarity is computed between the library and the available inventory items based on a sum of the first and second measured similarities. Available inventory items are recommended based on the computed overall similarity score. Relevance, which may diminish over a time, may be assigned based on behavior type and duration.

Abstract: Systems and methods for automatically generating replacement query terms that offer improved search efficiency. Recommended search query terms are generated based on statistic information derived from empirical data recording prior search sessions with respect to searching on a search engine. A query term entered later in a search session is treated as a possible replacement term for a query term entered earlier in the same session. Upon receiving an initial query term in a new search session, the replacement terms of the initial query term are identified from the empirical data and evaluated as candidates for replacing the initial query term in the new search session. The evaluation is based on the respective occurrence rates that the candidates are used as replacement terms for the initial query term in the empirical data, and based on the respective conversion rates of the candidates.

Abstract: A first subset of results, from testing a first version of an item versus a second version of the item, is accessed and displayed. The first subset includes first values associated with the first version and second values associated with the second version. The first subset is determined according to settings for parameters associated with the data. A representation of the first values includes a first band having a first width corresponding to confidence intervals for the first values, and a representation of the second values includes a second band having a second width corresponding to confidence intervals for the second values. In response to a change in the settings, a second subset of the results is automatically determined, accessed, and displayed and the first width and the second width of the first and second bands are automatically updated and displayed.

Abstract: A user inputs values for parameters that are used to plan a test of a second version of an item to be tested that includes a change relative to a first version of the item. The user inputs include a value that defines the size of the group of participants that are to use the second version instead of the first version. Milestones for the test are displayed, along with first information that is determined based on the user-specified inputs and that includes the amount of time needed to reach each of the milestones. Second information that is determined based on the user-specified inputs includes a display of test length versus milestone. The first information and the second information provide a basis for defining the length of the test.

Abstract: Systems and methods of managing a profile of a user of at least one device having access to electronic content. The systems and methods may involve receiving a user identifier for a user of at least one device configured to access electronic content; storing a profile of the user in a memory, where the profile of the user comprises the user identifier; if a user activity meets at least one event definition, identifying the user activity as an event; receiving data associated with the event; and recording at least a portion of the data associated with the event in the profile of the user.