Sony Corporation

Naoki Kamimaeda

Tomohiro Tsunoda

Hiroyuki Masuda

Masanori Miyahara

Takehiro Hagiwara

Katsuyoshi Kanemoto

Setsushi Minami

Thomas Kemp

Masatomo Kurata

Yasutaka Fukumoto

Takuya Nishimura

Tomohiro Takagi

Takahito Migita

Ryo Nakahashi

An information processing apparatus may include a processor to acquire information associated with behavior of a user and information associated with satisfaction degree of the user, and to analyze an association between the information associated with behavior and the information associated with satisfaction degree.

Information processing apparatus, information processing method, and program

There is provided a device that generates a first feature based on information of a first type included in first content selected by a target user in past, generates a second feature based on information of a second type included in second content selected by the target user after selecting the first contest, generates a relational feature showing a relationship between the first content and the second content, based on the first feature and the second feature, and searches for content to be recommended to the target user by using the information of the first type included in content newly selected by the target user and the relational feature.

Content recommendation device, recommended content search method, and program

Recommending one or more items among a plurality of items to a first user by generating a weighted data set by applying a weight factor to each of a plurality of user event data sets each derived from a previous user event of a second user of the recommendation system. Each of the user event data sets includes a user identifier of the second user, an item identifier of an item involved in the previous user event, and time information regarding a time of the previous user event. The weight factor is derived from the time information of the corresponding previous user event. One or more recommended items are determined based on an algorithm using the weighted data set.

System, apparatus, and method for recommending items

There is provided an information processing device including: a storage unit that stores a plurality of exercise models each including one or more kinds of measured values measured in time series for a person doing exercise and exercise description data specifying exercise recognized based on the measured values; a selection unit that selects one or more exercise models from the plurality of exercise models stored in the storage unit according to a designated condition; and an output unit that outputs a series of output images representing the measured values and exercise specified by the exercise description data in time series for the exercise model selected by the selection unit.

Information processing device, image output method, and program

An information processing apparatus includes a managing unit that manages a scale calculated based on descriptions of an operation performed on a predetermined item and a series to which the item belongs in association with each other, a first extracting unit that extracts items belonging to the same series as top N series, obtained by sorting the scales, from among a plurality of items, a taste managing unit that manages taste of a user, a second extracting unit that calculates the degree of similarity between the taste managed by the taste managing unit and each item to extract items with higher degrees of similarity, and a recommending unit that recommends a predetermined number of items among the items extracted by the first extracting unit and the items extracted by the second extracting unit to the user.

Method and apparatus for image processing, program, and recording medium

Techniques are disclosed for selecting an item to recommend to a user from a plurality of items. A first set of scores indicating degrees of association between items of the plurality of items may be calculated using a first scoring algorithm having a first scale, and a second set of scores indicating degrees of association between items of the plurality of items may be calculated using a second scoring algorithm having a second scale. The second set of scores may be adjusted to match the second scale to the first scale, and a third set of scores may be calculated for the plurality of items by combining the first set of scores and the adjusted second set of scores. An item may be selected from the plurality of items to recommend to the user based on the third set of scores.

Information processing apparatus, terminal apparatus, information presentation system, calculation method of evaluation scores, and program

There is provided an information processing apparatus including: an acquiring unit acquiring a title of content; an analyzing unit dividing the title into tokens; a calculating unit calculating, for each token, an evaluation value based on a token length and weighted according to the token's position in the title; a mapping unit mapping, for each token, a token point shown by an ordinal number showing the token's position in the title and the evaluation value, onto a coordinate plane; a deciding unit deciding, based on the mapped token points, coordinates of a criterion point used as a criterion for extracting a series identifier and an extraction criterion based on the criterion point; an extracting unit extracting token points that conform to the extraction criterion out of the token points; and a generating unit generating the series identifier from the character strings included in tokens associated with the extracted token points.

An information processing apparatus is configured to search for and recommend content by considering both the feature quantities of content and the references of the user. A correction unit corrects item feature quantities of a recommendation target item by using user preferences of a recommendation target user. A matching unit compares the item feature quantities corrected by the correction unit with the item feature quantities of another item and searches for content having item feature quantities that resemble those of the recommendation target item. A recommendation unit recommends related items to the recommendation target user having features resembling those of the recommendation target item.

Information processing apparatus and method for recommending content to a user

Methods and systems include receiving feedback from one of a plurality of users for one of a plurality of items, storing the received feedback and past feedback received from the plurality of users, wherein the received feedback and past feedback are stored as combinations of users and items having feedback given by the users, calculating relevance between the one of the plurality of items and another one of the plurality of items, based on first information indicating a degree of feedback given by the one of the plurality of users for the plurality of items, and second information indicating a degree of stored past feedback given by the plurality of users to the plurality of items, and immediately recommending items to the one of the plurality of users, the recommended items having the highest relevance to the one of the plurality of items, based on the calculated relevance.

Item recommendation system, item recommendation method and program

There is provided an information processing apparatus including: an acquisition unit successively specifying fields to be processed out of a plurality of fields included in program information in electronic program guide information and acquiring a character string included in each specified field; an analysis unit analyzing the acquired character string and outputting an analysis result for dividing the character string into a plurality of units; a sequence generating unit generating, for each field, a sequence corresponding to character string lengths of the units based on the outputted analysis result; a variance value calculating unit calculating, for each field, a variance value showing a degree of variance of magnitudes of values of items in the sequence; and a specifying unit specifying, for each program, a cast member field, in which cast member names are listed, out of the plurality of fields based on the calculated variance values.

Information processing apparatus, information processing method, and program for extracting information from an electronic program guide

The present invention provides an information processing apparatus including: a first similarity degree calculating section for calculating the degree of similarity between a predetermined item and a calculation target item; and a related item determining section for determining a predetermined quantity of the calculation target items as the related items of the predetermined items in the descending order of the degree of similarity, wherein meta information related to the calculation target item based on the behavior history of user is added to the predetermined items and the degree of similarity between the predetermined item and the calculation target item is calculated.

Information processing apparatus and presenting method of related items

An attribute weight adjustment engine adjusts a weight of a user in accordance with a record of an identified member ID out of records of an attribute weight adjustment database. The weight adjustment is performed through the multiple regression analysis based on a target similarity resulting from the record of the attribute weight adjustment database and a similarity of each attribute of the attribute weight adjustment database. The resulting weight is identified as the weight of each attribute of the identified user. To account for the weight of each attribute of the user, an attribute weight database is updated, and an unwanted record is deleted from the attribute weight adjustment database.

Apparatus, method and computer program for content recommendation and recording medium

A base/inversion component extractor calculates an occupancy rate of each component of a vector of user preference information obtained from information stored on a user preference database, and extracts a base component from the user preference information in accordance with the calculated occupancy rate of the base component. The base/inversion component extractor extracts a similar base component from item preference information obtained from information stored on an item metadatabase. A recommendation engine calculates a similarity between a base vector of the user preference information and a base vector of the item preference information, and identifies, as candidate items, items of the predetermined number in the order of from high to low similarity. The recommendation engine further calculates a similarity between an inversion vector of the user preference information and an inversion vector of the item preference information, and identifies an item candidate having a low similarity.

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Kanagawa, Japan

Naoki Kamimaeda