University College Dublin
|As first author||22|
|As last author||89|
Michael P. O'Mahony(36)
Alan F. Smeaton(12)
Mark T. Keane(5)
Noel E. O'Connor(3)
... and 24 others
These arethe8 unique sources for Barry Smyth's 147 publications. A single publication may appear in multiple sources. Click on a name or publication count to see the publications for a particular source.
|Ireland -> Dublin City University||14|
|Ireland -> Dublin City University -> PubMed||3|
|Ireland -> Dublin Institute of Technology||1|
|Ireland -> Trinity College Dublin||17|
|Ireland -> Trinity College Dublin -> PubMed||1|
|Ireland -> University College Dublin||116|
|Ireland -> University College Dublin -> PubMed||4|
|Ireland -> University of Limerick||1|
A user inputs a request (1) for a commodity recommendation. A computer system accesses (2) a plurality of commodity reviews. The computer system extracts feature indicators (3) and sentiment indicators (4) from each commodity review. The computer system determines (5) the popularity of each feature indicator and the similarity between a first commodity (Q) and a second commodity (C). The computer system evaluates the sentiment indicators and evaluates the similarity indicator to form (7) the commodity recommendation. After the commodity recommendation has been formed in step (7), the computer system delivers (8) the commodity recommendation for the second commodity (C) to the user using a website interface.
A system for search and discovery of information in a real time network, comprising: means for gathering data indicative of a message posted in an real time network, the data comprising information identifying a uniform resource locator, URL and textual information associated with the URL
A method for generating product recommendations comprises analyzing a database of messages, comprising a set of messages posted by users of a micro-blogging service to generate a user index and a product index. The user index comprises for each of a plurality of users of the system, a ranked set of terms included by the user in their posted messages. The product index comprises for each product which is to be potentially recommended, a ranked set of terms derived from messages posted by users and referencing the product. Responsive to a query identifying a user, the user index for the user is compared to the product indices to return a limited set of product identifiers corresponding to product indices most similar to the user index. The set of product identifiers are provided as recommendations to a service provider.
A computer program product comprising a non-transitory computer readable medium for storing or recording instructions in machine readable form. The instructions, when executed in a motion sensor enabled smart phone, record target data associated with a response of the motion sensor to a desired sequence of movements of a user performing a therapeutic exercise while wearing the smart phone, map the target data to run-time target parameters of a software application, receive performance data associated with a response of the motion sensor to a subsequent sequence of movements of the user performing the therapeutic exercise while wearing the smart phone, map the performance data to the target parameters of the software application to determine the operation of the software application and provide information for display on a remote monitor visible to the user indicating the quality of the user's performance of the exercise.
A real time information feed system comprises an interface to receive a real time information feed and a data mining engine for retrieving data concerning a subscriber. A recommendation engine automatically modifies the real time information feed according to the mined data
A method and system for providing a recommendation to a user. The method includes uploading a user image, processing the user image, and generating at least one image of a product or service, depending on the purpose of the system, for the user based on the processing. The method also includes displaying at least one image of the product or service, accepting a feedback response, processing the feedback response, and producing an image of at least one type of the product or service based on the feedback response.
A meta search engine (1) receives a query (qT) from a user (U), and submits adapted queries (ql-qn) to n search engines (S1-Sn). The results (RI-Rn) are adapted (R1'-Rn') and combined to provide a result set R'. In parallel, the meta search engine (1) accesses a selected hit matrix (H) populated with values for correspondence of queries ql- qm with web pages pi-pn. These values are the number of "hits" for pervious searches. The pages (R") retrieved from a row for qT are combined with the search engine (S1-Sn) results to provide a final result but which is ranked according to previous search experience. The hit matrix may be dynamically selected according to a user community. The query (qT) need not exactly match a hit matrix query (q1-qn) as rows can be selected according to similarity to the current query qT with weightings applied.
A conversational recommender system retrieves k cases (3) and generates a structure for user feedback (4). The structure includes a list of critique units for each of which the user can specify a value range. It also generates compound critiques, each being a combination of features and value ranges presented together. An explanation of a compound critique can be generated in response to a user request. The system determines (7), from one cycle of a session to the next, if a preference has been carried. If so, a re-focus function implementing diversity is used for retrieval in the next cycle. If not, a refine function implementary similarity-based retrieval is used for the next cycle of the session.
A case base system (1) does not need a decision structure such as a rule base as it uses both automatic (46) and interactive steps (47-48) which are carried out in real time to retrieve the most appropriate case. Case filtering (5) is carried out to immediately provide an initial candidate set by elimination of incompatible case records according to received free features. The free features are addressable as such in the case records. A primary processor (6) carries out pattern matching operations (46) correlating feature values and solutions in case records (10) of a candidate set. By doing this, it automatically identifies the most discriminating feature for the candidate set. The primary processor (6) interactively provides feedback (47-48) and receives a value for the most discriminating feature and further reduces the candidate set.
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