Patent Abstract:
Performing an on-line recommendation includes: analyzing real-time data from various sources; determining, from the analysis, events in which a user may be interested; extracting the determined events; storing the extracted events in a data store; and performing a recommendation function. The recommendation function includes: ranking the extracted events to determine the events in which the user is most likely to be interested; and performing location-based filtering, retaining those extracted events that are within a geo-location range proximate to the user, thus generating optimal events.

Full Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application references technology disclosed in commonly-owned, co-pending U.S. patent application Ser. No. 12/862,324, filed on Aug. 24, 2010 and entitled “Haptic Search Feature for Touch Screens;” which application is incorporated by reference in its entirety herein. 
     STATEMENT REGARDING FEDERALLY SPONSORED-RESEARCH OR DEVELOPMENT 
     None. 
     INCORPORATION BY REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC 
     None. 
     FIELD OF THE INVENTION 
     The invention disclosed broadly relates to the field of on-line social event processing, and more particularly relates to location-sensitive on-line social events. 
     BACKGROUND OF THE INVENTION 
     More and more Internet users are accessing the web through their mobile phones. The mobile phone has moved beyond providing telephone service. It is a communicator, organizer, social planner, shopping assistant, photo album, and so much more. Mobile phones help to keep their users up-to-date and informed by providing the boundless wealth of knowledge from the Internet in a portable device. It&#39;s no wonder the mobile phone is ubiquitous in our society and its use is growing. 
     However, with this wealth of information so readily available, a mobile device user is still hard-pressed to find meaningful information on events within the user&#39;s location that accommodates the user&#39;s tastes. 
     SUMMARY OF THE INVENTION 
     Briefly, according to an embodiment of the invention a method for performing an on-line recommendation includes steps or acts of: analyzing real-time data from various sources; determining, from the analysis, events in which a user may be interested; extracting the determined events to store in a data store; and performing a recommendation function. The recommendation function includes: ranking the extracted events to determine the events in which the user is most likely to be interested; performing location-based filtering to retain those extracted events that are within a geo-location range proximate to the user to generate optimal events; and providing the optimal events on a mobile device. 
     According to another embodiment of the present invention, a computer system for performing an on-line recommendation includes: a memory with computer-executable instructions stored therein; and a processor device operably coupled with the memory for executing the computer-executable instructions. The computer-executable instructions, when executed, cause a computer to perform the above-stated method for performing an on-line recommendation. 
     According to another embodiment of the present invention, a computer program product includes a non-transitory computer readable storage medium with program instructions stored therein. The program instructions, when executed by a computer, cause the computer to perform the above method for performing an on-line recommendation. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
       To describe the foregoing and other exemplary purposes, aspects, and advantages, we use the following detailed description of an exemplary embodiment of the invention with reference to the drawings, in which: 
         FIG. 1  is a simplified block diagram of a system configured to operate according to an embodiment of the present invention; 
         FIG. 2  is a flowchart of a method according to an embodiment of the invention; 
         FIG. 3A  shows an exemplary illustration of the display of events on a mobile device, according to an embodiment of the present invention; 
         FIG. 3B  shows an exemplary illustration of the display of events on a mobile device, according to another embodiment of the present invention; 
         FIG. 4  is a data flow diagram of event ranking, according to an embodiment of the present invention; 
         FIG. 5  is a high level block diagram showing an information processing system according to another embodiment of the invention; and 
         FIG. 6  is an exemplary illustration of how a geo-location can be specified, according to an embodiment of the present invention. 
     
    
    
     While the invention as claimed can be modified into alternative forms, specific embodiments thereof are shown by way of example in the drawings and will herein be described in detail. It should be understood, however, that the drawings and detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the intention is to cover all modifications, equivalents and alternatives falling within the scope of the present invention. 
     DETAILED DESCRIPTION 
     Before describing in detail embodiments that are in accordance with the present invention, it should be observed that the embodiments reside primarily in combinations of method steps and system components related to systems and methods for placing computation inside a communication network. Accordingly, the system components and method steps have been represented where appropriate by conventional symbols in the drawings, showing only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the disclosure with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein. Thus, it will be appreciated that for simplicity and clarity of illustration, common and well-understood elements that are useful or necessary in a commercially feasible embodiment may not be depicted in order to facilitate a less obstructed view of these various embodiments. 
     We describe a mobile-based real time geo-sensitive social event recommendation engine with a graphic human-mobile interface. Referring now in specific detail to the drawings, and particularly to  FIG. 1 , there is provided a simplified block diagram depicting a mobile-based real-time location-sensitive social event engine  110 , according to an embodiment of the present invention. The engine  110  includes components such as the Event Mining component  112 , the Event Extractor  114  which operates according to a Natural Language Processing (NLP)  116 , an Event Ranking component  118 , and a Filter  119 . The components may be separate physical components of a computer system, or they may be logical components housed within one processor. The engine  110  is operably coupled with a data store  150  where it stores extracted &lt;subject-verb-object&gt; (S-V-O) tuples. SVO identification is a well-defined task in natural language processing (NLP). It is used to identify the key subject, verb, and object from a sentence. We use SVO identification to identify the key subject, verb, and object from a Tweet or Facebook update. 
     The Event Mining component  112  mines data from sources such as Twitter, Facebook and Yahoo! Social Updates. This mined data is fed into the Event Extractor  114  which then processes the data into S-V-O tuples  125 . The tuples  125  are stored in the data store  150 . The Event Ranking component  118  takes the extracted tuples  125  of events and ranks those events that are most likely to appeal to the user of the mobile device  180 . The Filter  119  then performs a filtering algorithm based on the geo-location of the user&#39;s mobile phone  180  to further refine the output to include only those events that are most likely to appeal to the user and are within a reasonable distance from the user. 
     Referring now to  FIG. 2  there is provided a high-level flowchart  200  of the process for providing mobile-based real-time location-sensitive, social events, according to an embodiment of the present invention. In step  210  we mine and analyze real-time feeds from Twitter, Facebook, Yahoo! Social Updates, blogs, breaking news, and the like in order to uncover events in which the mobile device user might be interested. 
     We look for and in step  220  extract high-quality events with various useful attributes such as location, date/time, subject-verb-object (SVO) category, and the like. We assume that a social event takes the form of “who (subject) does (verb) what (object)” with additional attributes such as when and where. For example, given an event like “Chicago Bulls beats Lakers, 99-90 at the Rose Garden this Thursday evening,” Chicago Bulls, beat, and Lakers would be subject, verb, and object, respectively. The when and where are Thursday evening and Rose Garden, respectively. 
     We can perform this extraction within the WOO framework (used for generating websites and blogs). This is just one example, however; we can use any web tool that provides an easy-to-use interface and still remain within the spirit and scope of the invention. The events are extracted using the SVO format. Any sentence not taking the SVO form would not be considered as an important event; thus it would not be extracted. Conversely, taking the form of SVO is not sufficient to be categorized as an important event; therefore we need to further classify and rank the events. 
     We focus our event extraction on real-time social data such as Twitter, Facebook, Yahoo! Pulse updates, and other real-time updates, Blogs, local breaking news, and the like. NLP (natural language processing) technologies can be used to successfully extract events. We populate our real-time database  150  with SVO (subject-verb-object) tuples  125 , when, where, popularity, event category, and so forth. 
     In step  230  we rank the extracted events according to various attributes, including, but not limited to: categories of personal interests, distance to the center of circle (if using a circular location-bounding app), personal relevance, recency, social relevance (how many users from your social circle plan to attend or showed interest, etc.). Up to this point, steps  210 ,  220 , and  230  can be performed on a continuous basis as feeds are received. In an alternative embodiment, however, these steps and the subsequent steps are only performed when a user prompt is received. 
     Referring now to  FIG. 4 , based on our understanding of users, we build a personalized relevance ranking or recommendation function  118  which takes as inputs  410  features such as an event quality score, distance, personal profile vector, event vector, person-event matching features, and the like. The output of this ranking function  118  is a clickability/relevance score for each &lt;user/event&gt; pair  450 . Given a user, we then rank the events according to the corresponding clickability scores. 
     Some event features taken as input  410  are: 
     1. The event quality score: this score mainly indicates the importance or significance of an event itself (independent of users). It may be obtained by another machine learned model taking into consideration for example the confidence score of the event provided by the event extraction system  110 , the popularity of subject and object, the number of users Tweeting about the event, the number of comments on this event in Facebook, and so on. 
     2. Distance: the physical distance between the user and an event location. 
     3. Personal profile vector: the feature vector representation for a user based on his/her behavior data such as searched queries, viewed/clicked web pages, posted tweets/social updates, etc. the simplest version could be keyword vectors. 
     4. event vector: the feature vector representation for an event. the simplest version could be keyword vectors. 
     5. person-event matching features: based on 3 and 4, one can derive various matching features such as the dot product of the two vectors. 
     In step  240  we join data from the user&#39;s social networks to provide personalized information such as who from the user&#39;s social circle is interested in attending the events, or who has talked about it. In one embodiment, if we know what social connections are interested in attending what events, we can provide a Groupon-like service to book the tickets together so that the friends can receive discounts. Adding to this, we can build a real-time forum for the social connections to share opinions/feedback on the same events real-time. If it is a recurrent event, we can build an event-based social network. 
     In step  250  we perform location-based event filtering. Based on the geo-location indication received from a user&#39;s mobile device  180 , we perform a distance-based filtering on the event database  150  and retain those events close to the user&#39;s specified location/area. In step  260  we present the results to the user on the mobile device  180 .  FIGS. 3A and 3B  show exemplary illustrations of a display of events selected for the user. In both  FIGS. 3A and 3B  the user&#39;s location is displayed in the center of a map overlay on the user&#39;s screen at position  320 . In  FIG. 3A  the events selected for the user are represented by flags  345  at their respective locations. Using a mobile app gateway the interface shown in  FIG. 3A  allows the user to click on an event flag  345  to see more information about the event. 
     Referring now to  FIG. 6  we show one embodiment wherein the user has drawn a circle (or closed contour of any shape) on a map. This technology was disclosed in commonly-owned co-pending U.S. patent application Ser. No. 12/862,324, filed on Aug. 24, 2010 and entitled “Haptic Search Feature for Touch Screens.” The system will pop up the personally interesting social events happening or scheduled to happen in the location bounded the shape drawn by the user. 
     In  FIG. 3B  we show the same interface of  FIG. 3A , but the events are displayed in a scrollable bar  360 . Here the events are displayed in a what-where-when format, but different formats can be used within the spirit and scope of the invention. If the events are too numerous to display all at once, the scroll feature  365  can be used. 
     We can also automatically push events to the user&#39;s mobile device  180  based on where the user is right now, as obtained from geo-location within the mobile  180 . In this scenario we could alert the user to check his/her messages for an event notification. 
     In step  270  when online user click feedback is available, we store it in a learning data store to build an online learning system to take advantage of user real time click feedbacks to further refine the offline ranking/recommendation function. Each event is assigned a confidence score based on the quality of data sources, our ML (machine learning) based extraction confidence, etc. It will also be classified into predefined event categories. 
     Event extraction can be performed on an ongoing and recurring basis whenever new Tweet or Facebook feeds become available in the pipeline. The system analyzes the new feeds and extracts the social events, either pushing them to the user or storing them in a data store until the user requests them. 
     Hardware Embodiment. 
     Referring now to  FIG. 5 , there is provided a simplified pictorial illustration of an information processing system  500  in which the present invention may be implemented. For purposes of this invention, computer system  500  may represent any type of computer, information processing system or other programmable electronic device, including a client computer, a server computer, a portable computer, an embedded controller, a personal digital assistant, and so on. The computer system  500  may be a stand-alone device or networked into a larger system. Computer system  500 , illustrated for exemplary purposes as a networked computing device, is in communication with other networked computing devices (not shown) via network  510 . As will be appreciated by those of ordinary skill in the art, network  510  may be embodied using conventional networking technologies and may include one or more of the following: local area networks, wide area networks, intranets, public Internet and the like. 
     Throughout the description herein, an embodiment of the invention is illustrated with aspects of the invention embodied solely on computer system  500 . As will be appreciated by those of ordinary skill in the art, aspects of the invention may be distributed amongst one or more networked computing devices which interact with computer system  500  via one or more data networks such as, for example, network  510 . However, for ease of understanding, aspects of the invention have been embodied in a single computing device—computer system  500 . 
     Computer system  500  includes processing device  502  which communicates with various input devices, output devices and network  510 . The processor  502  is connected to a communication infrastructure  522  (e.g., a communications bus, cross-over bar, or network). The processor device  502  may be a general or special purpose microprocessor operating under control of computer program instructions executed from memory  504 . The processor may include a number of special purpose sub-processors, each sub-processor for executing particular portions of the computer program instructions. Each sub-processor may be a separate circuit able to operate substantially in parallel with the other sub-processors. Some or all of the sub-processors may be implemented as computer program processes (software) tangibly stored in a memory that perform their respective functions when executed. These may share an instruction processor, such as a general purpose integrated circuit microprocessor, or each sub-processor may have its own processor for executing instructions. Alternatively, some or all of the sub-processors may be implemented in an ASIC. RAM may be embodied in one or more memory chips. The memory may be partitioned or otherwise mapped to reflect the boundaries of the various memory subcomponents. 
     Memory  504  includes both volatile and persistent memory for the storage of: operational instructions for execution by CPU  502 , data registers, application storage and the like. Memory  504  preferably includes a combination of random access memory (RAM), read only memory (ROM) and persistent memory such as that provided by a hard disk drive. The computer instructions/applications stored in memory  504  and executed by processor device  502 . 
     The I/O subsystem  506  may comprise various end user interfaces such as a display, a keyboards, and a mouse. The I/O subsystem  506  may further comprise a connection to a network such as a local-area network (LAN) or wide-area network (WAN) such as the Internet. The computer system may include a display interface  508  that forwards graphics, text, and other data from the communication infrastructure  502  (or from a frame buffer not shown) for display on the display unit  510 . The computer system also includes a main memory  504 , preferably random access memory (RAM), and may also include a secondary memory  512 . The secondary memory  512  may include, for example, a hard disk drive  514  and/or a removable storage drive  511 , representing a floppy disk drive, a magnetic tape drive, an optical disk drive, etc. The removable storage drive  511  reads from and/or writes to a removable storage unit  518  in a manner well known to those having ordinary skill in the art. Removable storage unit  518 , represents a floppy disk, a compact disc, magnetic tape, optical disk, etc. which is read by and written to by removable storage drive  511 . As will be appreciated, the removable storage unit  518  includes a computer readable medium having stored therein computer software and/or data. 
     The computer system may also include a communications interface  524 . Communications interface  524  allows software and data to be transferred between the computer system and external devices. Examples of communications interface  524  may include a modem, a network interface (such as an Ethernet card), a communications port, a PCMCIA slot and card, etc. Software and data transferred via communications interface  524  are in the form of signals which may be, for example, electronic, electromagnetic, optical, or other signals capable of being received by communications interface  524 . These signals are provided to communications interface  524  via a communications path (i.e., channel). This channel carries signals and may be implemented using wire or cable, fiber optics, a phone line, a cellular phone link, an RF link, and/or other communications channels. 
     In this document, the terms “computer program medium,” “computer usable medium,” and “computer readable medium” are used to generally refer to non-transitory media such as main memory and secondary memory, a removable storage drive, and a hard disk installed in hard disk drive. These computer program products are means for providing software to the computer system. The computer readable medium allows the computer system to read data, instructions, messages or message packets, and other computer readable information from the computer readable medium. 
     Therefore, while we have described different embodiments, it will be understood by those skilled in the art that other modifications can be made within the spirit of the invention. The above description(s) of embodiment(s) is not intended to be exhaustive or limiting in scope. The embodiment(s), as described, were chosen in order to explain the principles of the invention, show its practical application, and enable those with ordinary skill in the art to understand how to make and use the invention. It should be understood that the invention is not limited to the embodiment(s) described above, but rather should be interpreted within the full meaning and scope of the appended claims.

Technology Classification (CPC): 6