Abstract: In an embodiment, a processor includes a plurality of processing engines to execute instructions and a power management unit. The power management unit is to: control an operating frequency and a supply voltage according to a first voltage/frequency curve associated with a first temperature; and in response to a detection of a second temperature in the processor, increase the operating frequency to a second frequency based on a second voltage/frequency curve, wherein, at least one voltage of a first range of voltages, the second voltage/frequency curve specifies a higher frequency than the first voltage/frequency curve. Other embodiments are described and claimed.

Abstract: In an embodiment, a processor includes a plurality of processing engines to execute instructions and a power management unit. The power management unit is to: control an operating frequency and a supply voltage according to a first voltage/frequency curve associated with a first temperature; and in response to a detection of a second temperature in the processor, increase the operating frequency to a second frequency based on a second voltage/frequency curve, wherein, at least one voltage of a first range of voltages, the second voltage/frequency curve specifies a higher frequency than the first voltage/frequency curve. Other embodiments are described and claimed.

Abstract: A recommender system for recommending items to a user based on geo-Tagged information related to him, in which items associated with a GeoTag are stored in a database. Feedback regarding the various items is obtained from the user and the provided rating of items is propagated to closely located items based on their associated GeoTags. A user-to-user similarity matrix is calculated and a predicted score is assigned for each user and item, using a recommendation server. All the items in the catalog of items are sorted according to their predicted scores as calculated for the user, and all items that have been already rated by the user are filtered out. Then, items from the catalog of items are presented to the user, according to their scores.

Abstract: A recommender system for recommending items to a user based on geo-Tagged information related to him, in which items associated with a GeoTag are stored in a database. Feedback regarding the various items is obtained from the user and the provided rating of items is propagated to closely located items based on their associated GeoTags. A user-to-user similarity matrix is calculated and a predicted score is assigned for each user and item, using a recommendation server. All the items in the catalog of items are sorted according to their predicted scores as calculated for the user, and all items that have been already rated by the user are filtered out. Then, items from the catalog of items are presented to the user, according to their scores.

Abstract: A recommender system for recommending items to a user based on geo-Tagged information related to him, in which items associated with a GeoTag are stored in a database. Feedback regarding the various items is obtained from the user and the provided rating of items is propagated to closely located items based on their associated GeoTags. A user-to-user similarity matrix is calculated and a predicted score is assigned for each user and item, using a recommendation server. All the items in the catalog of items are sorted according to their predicted scores as calculated for the user, and all items that have been already rated by the user are filtered out. Then, items from the catalog of items are presented to the user, according to their scores.