Abstract: Co-occurrence data representing e.g. preferences and facts observed in a plurality of situations may be stored in a matrix as combinations of high-dimensional sparse vectors. The matrix may be called e.g. as an experience matrix. The data stored in the experience matrix may be subsequently utilized e.g. for predicting a preference of a user in a new situation.

Abstract: The invention relates to predictive browsing. A set of words for use with an experience matrix are formed, wherein the words are descriptive of a context of a system such as a current web page, and wherein said experience matrix comprises sparse vectors associated with words. At least a part of at least one sparse vector of said experience matrix is accessed to form a prediction output, and suggestions of web pages are provided to a user in response to said prediction output.

Abstract: The invention relates to forming a prediction using an experience matrix, a matrix based on sparse vectors such as random index vectors. At least a part of a first experience matrix and at least a part of at least a second experience matrix are caused to be combined (1410) to obtain a combined experience matrix. The experience matrices comprise sparse vectors or essentially similar vectors in nature, and said experience matrices comprise information of at least one system, for example contexts of a system. At least a part of at least one sparse vector of the combined experience matrix is accessed to form a prediction output (1420), and a system is controlled (1430) in response to said prediction output.

Abstract: Co-occurrence data representing e.g. preferences and facts observed in a plurality of situations may be stored in a matrix as combinations of high-dimensional sparse vectors. The matrix may be called e.g. as an experience matrix. The data stored in the experience matrix may be subsequently utilized e.g. for predicting a preference of a user in a new situation. A prediction may be determined by a method comprising providing a query comprising one or more query words, accessing the experience matrix containing co-occurrence data stored as vectors of the experience matrix, determining a first auxiliary vector by identifying a vector of the experience matrix associated with a first query word, forming a query vector by using the first auxiliary vector, and determining the prediction by comparing the query vector with the vectors of the experience matrix.

Abstract: The invention relates to forming a prediction using an experience matrix, a matrix based on sparse vectors such as random index vectors. At least a part of a first experience matrix and at least a part of at least a second experience matrix are caused to be combined (1410) to obtain a combined experience matrix. The experience matrices comprise sparse vectors or essentially similar vectors in nature, and said experience matrices comprise information of at least one system, for example contexts of a system. At least a part of at least one sparse vector of the combined experience matrix is accessed to form a prediction output (1420), and a system is controlled (1430) in response to said prediction output.

Abstract: The invention relates to predictive browsing. A set of words for use with an experience matrix are formed, wherein the words are descriptive of a context of a system such as a current web page, and wherein said experience matrix comprises sparse vectors associated with words. At least a part of at least one sparse vector of said experience matrix is accessed to form a prediction output, and suggestions of web pages are provided to a user in response to said prediction output.

Abstract: Co-occurrence data representing e.g. preferences and facts observed in a plurality of situations may be stored in a matrix as combinations of high-dimensional sparse vectors. The matrix may be called e.g. as an experience matrix. The data stored in the experience matrix may be subsequently utilized e.g. for predicting a preference of a user in a new situation. A prediction may be determined by a method comprising providing a query comprising one or more query words, accessing the experience matrix containing co-occurrence data stored as vectors of the experience matrix, determining a first auxiliary vector by identifying a vector of the experience matrix associated with a first query word, forming a query vector by using the first auxiliary vector, and determining the prediction by comparing the query vector with the vectors of the experience matrix.

Abstract: Co-occurrence data representing e.g. preferences and facts observed in a plurality of situations may be stored in a matrix as combinations of high-dimensional sparse vectors. The matrix may be called e.g. as an experience matrix. The data stored in the experience matrix may be subsequently utilized e.g. for predicting a preference of a user in a new situation.