HP Mini 210-1010EG Notebook Broadcom Bluetooth Driver
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HP Mini 210-1010EG Notebook Broadcom Bluetooth Driver
Implicit interactions are passive by-products of searching, browsing, or interacting with a mobile application.
For example, an implicit interaction can be recorded when a user clicks on an online detail page of a local venue. Merely clicking on a detail page for a venue does not positively identify the user's intent regarding the venue.
In some cases the user may read the detail page and decide that they do not like the venue or are unlikely to like the venue. The recommendation system may infer some interest in a venue based on a user's implicit interactions, but these implicit interactions can be handled differently from explicit interactions by the recommendation system. Finally, the recommendation system uses place or venue information as an input to calculating a place graph.
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In an example, the recommendation system uses specialized indexing and retrieval algorithms that leverage mathematical techniques and machine learning to identify patterns and relationships for venues based on the inputs described above. Given the inputs discussed above, the dynamically generated output consists of multiple levels of relationships of places for a particular user.
This machine HP Mini 210-1010EG Notebook Broadcom Bluetooth feature extraction can identify similarities between locations that are not readily apparent to users.
In this example, dimensionality reduction can be applied to further the concept of inferring relationships between places e. These techniques enable an example recommendation engine to develop previously unknown connections among places, thereby allowing for new personalized discoveries to be presented to users as recommendations. As mentioned HP Mini 210-1010EG Notebook Broadcom Bluetooth, explicit and implicit user interactions can be treated differently by the recommendation engine in developing a place graph.
Although there sometimes appears to be a high correlation between implicit and explicit actions—between places that have been browsed and have been rated by a user—attempts to model explicit interactions from implicit interactions generally produces lower quality recommendations. In an example, the quality of a recommendation can be measured by whether the user acts upon a recommendation either explicitly or implicitly.
For example, historical user interaction data demonstrates that trying to predict a user's rating for a particular place based on the number of times that user has HP Mini 210-1010EG Notebook Broadcom Bluetooth the detailed page of that place has not proven reliable. While hybrid models can be applied, most example embodiments use probabilistic similarity metrics to calculate relationships among places in a certain geographic area e.
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Generating a personalized place graph can be a difficult undertaking. As mentioned above, user-place interactions are generally sparse, and extremely sparse or non-existent in new geographical locations.
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To address the sparse data issue, the recommendation system can use dimensionality reduction and matrix factorization. In an example system, dimensionality reduction and matrix factorization are performed using the PCA and SVD algorithms mentioned above.
By factorizing an original user-place-interaction matrix, the system can uncover hidden connections among places in different geographic locations based on user profile data and successfully build a place graph for new geographic locations e. In certain examples, the recommendation system can also leverage more traditional collaborative filtering techniques, particularly when a user initially starts using the recommendation system e.
In an example, the recommendation system can create location-aware recommendations for a new geographic location.
As users move around with their mobile devices going to different places throughout the day, users demand that recommendations be constantly and dynamically recalculated according to the places around them e. Periodically calculating recommendations offline is not going to produce the results desired by mobile device users. Recommendation calculations need to be performed in real-time and with the user's current location in context.
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In an example, the recommendation system can employ rapid place graph node traversal to solve the real-time location-aware recommendation problem discussed above. The recommendation system discussed herein is capable of constantly recalculating user recommendations and updating a recommended place list based on a user's previous places e.
As a result, the discussed recommendation system is an optimal solution for local discovery that takes mobile usability into account. In an HP Mini 210-1010EG Notebook Broadcom Bluetooth, as users interact with places nodes within a place graphthe explicit and implicit interactions are mapped on a place graph e. Paths can then be calculated within the place graph to reflect the user's local tastes e.
Based on these taste paths, the system can predict places the user may like in the current location.
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As noted above, the recommendation system can use a hybrid model that takes into account information beyond a place graph, such as user profile information and social graph e. This additional information can be especially useful in a cold start scenario, where a user has not recorded many or any interactions either explicit or implicit.
Additional information on place graph calculations and systems for personalized location-aware recommendations can be found in the provisional patent application Ser. A real-time location-aware group recommendation system can merge place graphs associated with multiple users to produce a recommendation suitable for the unique collection of people within the group.
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Because place graphs can be merged in real-time or near real-timethe recommendation system does not need prior knowledge of the group members to produce a location-aware recommendation. In an example, a location-aware group recommendation system can generate a recommendation based on a location associated with HP Mini 210-1010EG Notebook Broadcom Bluetooth group and user identification information used to access user profile data associated with each member of a particular group.
In an example, the system can include a networka network-based recommendation systemand a plurality of users HP Mini 210-1010EG Notebook Broadcom Bluetooth collectively referred to as user or users In certain examples, the users can interact with the network-based recommendation system over the network via a mobile device such as mobile device discussed below in reference to FIG.