Data engine for ranking popularity of landmarks in a geographical area

A mechanism of rating, ranking and/or filtering landmarks located in a predefined geographic area is provided. More particularly, the subject mechanisms can facilitate rating landmarks and related criteria (e.g., points of interest (POI)) associated therewith. Thus, the mechanisms can facilitate intelligent rendering of landmark choices to users. The subject innovation facilitates rating the landmarks by analyzing items and entities in proximity to each landmark. A popularity index rating represents a sum of the number of POIs, number of commonly named entities and number of access routes to the landmark. Accordingly, this popularity index rating can allow users to rank, filter and sort landmarks in the order of popularity or interest thus maximizing available sightseeing time.

BACKGROUND

Vacationers and business travelers alike are frequently interested in locating landmarks and sites of significance when visiting a city. Landmarks can include sites and locales such as historical sites, architectural structures, and other locations of significant interest and/or importance. Traditionally, a traveler would research a destination city/location to identify “must see” locations. This research and decision making was done manually and sometimes took hours upon hours to complete.

As mapping applications continue to evolve on the Internet, there is an ongoing demand to provide additional focused and targeted information to users. Conventionally, mapping applications have been used primarily to provide users with directions to and/or from a particular location. As well, these applications sometimes provide additional generic information about the particular destination location.

By way of example, when planning for a business trip or vacation, a user can use a mapping application to easily request driving directions from one location to another. Additionally, these mapping applications can be employed to provide other information about a destination location. Many applications can assist a user to research a destination location with regard to “must see” locations.

Mapping applications today can assist in identification of well known landmarks and other “points of interest” (POIs) such as hot-spots, restaurants, theaters, hotels, etc. A listing of POIs can assist a user to plan a trip by providing the user with options and ideas regarding a destination location. However, traditional mapping applications do not provide information to assist a user to rank and/or narrow down a list of landmarks and/or POIs.

For instance, if a user travels to a large city, e.g., New York City (NYC), a landmark and POI list could include a seemingly unmanageable number of historical sites, hot-spots, restaurants and hotels. However, these applications are not equipped to intelligently assist a visitor and/or traveler to select particular sites to visit based upon POIs. As well, applications today do not provide any guidance regarding the popularity of any one POI from a complete list of POIs or other rating factors. Rather, mapping programs historically generate a laundry list of sites and POIs thereafter leaving any research, ranking, selecting and/or sorting to the user to plan details of their stay. Therefore, a user is oftentimes forced to sift through a voluminous number of landmark sites and PIOs when planning a visit or vacation.

SUMMARY

The invention disclosed and claimed herein, in one aspect thereof, comprises a system and methodology for ranking a landmark(s) and/or sites of interest. More particularly, the subject ranking mechanisms can facilitate ranking landmarks and criteria associated therewith (e.g., points of interest (POIs)) thus intelligently rating landmarks located in (or near) a defined area (e.g., city).

The subject innovation can be used in connection with mapping applications to facilitate rating, ranking, sorting and/or filtering landmarks by generating a popularity index associated with each landmark. Accordingly, these rating/ranking mechanisms can allow visitors (e.g., travelers) to choose from a list of landmarks based upon popularity or interest thus effectively maximizing available sightseeing time.

One novel feature of the subject landmark ranking and/or filtering mechanism is to identify and deliver the most popular and/or interesting landmarks to a user. In one aspect, the system can employ an algorithm to generate a popularity index rating. More particularly, for each landmark identified, a popularity index can be generated by identifying factors such as POIs, commonly named entities, access routes, etc. One aspect considers a sum of the identified number of factors. Other aspects apply weights to particular factors based upon preference, popularity, interest, availability, etc.

In still another aspect, a sum of the above three components, number of POIs, number of commonly named entities and number of access routes can be combined to generate the popularity index and effectively a popularity rating and/or rank. The landmark rating and/or rank can be employed to sort the landmarks thus facilitating rendering a short list of landmarks to a user based upon popularity and/or interest.

In yet another aspect thereof, an artificial intelligence (AI) component is provided that employs a probabilistic and/or statistical-based analysis to prognose or infer a user preference. For example, an AI component can be employed to automatically infer an applicable geographical area for which to identify index factors (e.g., POIs). Additionally, still another aspect can employ rules-based logic to effect implementation of identifying, ranking, sorting and/or filtering of landmarks.

DETAILED DESCRIPTION

As described above, traditionally, users can find sites of significant interest (e.g., landmarks) in a given city or locale using any mapping product. However, conventional applications do not facilitate rating and/or ranking the landmarks by popularity or in accordance with a user preference. Lack of such feature requires users to manually perform research outside the mapping products to make a short list of places to visit when time is limited.

The subject system employs a landmark ranking algorithm that enables users to rank the landmarks and places of interest. For example, the subject system can be employed within or in conjunction with a mapping product to rate and/or rank places of interest in a city or other specified location. In one aspect, the rating/ranking system can provide a user with suggestions from which a user can choose popular landmarks to visit.

Referring initially toFIG. 1, a system100that facilitates rating places of significant interest (e.g., landmarks) in accordance with one aspect of the innovation is shown. Although the examples described herein are directed to rating and/or ranking landmarks in a particular destination location, it is to be appreciated that the novel features and functionality described herein can be applied to any type of location.

Generally,FIG. 1includes a popularity index engine102that receives data relating to a geographic area104. Accordingly, in one aspect, the popularity index engine102can access a database (local or remote) thereby identifying one or more landmarks in proximity to the geographic area. Additionally, the popularity index engine can facilitate establishing a popularity index for each of the identified landmarks. The index can be based upon any predefined factors. As will be described in greater detail below, the index can be based upon popularity as well as other factors.

In general, the popularity index engine102can establish a popularity index rating106that corresponds to each particular landmark. This popularity index rating106can be useful in assisting a user in planning a trip or visit. For example, the popularity index rating106can provide a user with a benchmark of popularity or interest with regard to a site. Essentially, the popularity index engine102can automatically facilitate research and hierarchically rank landmarks based upon any predefined criteria or factors. Individual mechanisms and methodologies of ranking and filtering will be better understood upon a review of figures that follow.

Geographic area104is representative of any identifying data related to a location. For example, the geographic area104can be representative of a city (e.g., Seattle). As well, geographic area104can be representative of a state or even a region. It is to be appreciated that geographic area104can be descriptive of any physical place whereas, the popularity index engine102can identify and rate places of significance (e.g., landmarks) within the geographic area104.

One novel feature of the subject landmark ranking and/or filtering mechanism is to identify and suggest the most relevant information to a user. As will be described in more detail below, users oftentimes have very little time when visiting a city. For this reason it is particularly useful to render landmarks based upon the popularity of each individual landmark. This mechanism can enable a user to maximize limited time in a location while at the same time enabling the user to visit sites of particular interest and/or popularity.

Referring now toFIG. 2, the popularity index engine102can employ an algorithm(s) to arrange landmarks in order by generating an index and thus a popularity index rating106thereafter. It will be appreciated that a software product equipped with the novel landmark ranking algorithm can allow travelers to intelligently plan a visit to a city while maximizing available time to see the most popular landmarks. Effectively, users can employ the popularity index rating106to sort landmarks and other sites in the order of popularity or interest thus effectively maximizing time while visiting.

Illustrated inFIG. 2is an architectural diagram of system200that facilitates generation of the popularity index rating106. More particularly, popularity index engine component102can employ various factors in determining a popularity index rating106. Although specific factors are illustrated inFIG. 2with respect to the popularity index engine102, it is to be understood that all or a subset of these factors can be employed in an algorithmic manner to determine the popularity index rating106. Exemplary algorithms will be described in greater detail infra with reference to the figures that follow.

The popularity index engine102can employ any desired criteria (e.g., factors) in determining the popularity index rating106. The aspect illustrated inFIG. 2can employ points of interest (POIs)202, entities that share a common (or similar) name204and access routes206(e.g., transportation infrastructure) in determining the popularity index rating106.

As described supra, conventional mapping programs continue to evolve to provide users with the most informative experience. The landmark ranking mechanisms (e.g., popularity index engine102) described herein can be employed with any platform that can provide geographic related data. As well, it is to be understood and appreciated that the subject system can be employed in connection with any hardware device and/or system. By way of example, the subject mechanisms can be utilized with the assistance of a personal computer, handheld computer, smart phone, personal data assistant or the like. For example, looking at specific geographical area (e.g., Seattle area), the popularity index engine102can be employed to identify and deliver the most popular and/or interesting sites (e.g., landmarks) to a user. By way of further example, the popularity index engine102can facilitate ranking, sorting and/or filtering a large number of sites to identify the most popular and/or interesting sites to a given user.

Conventionally, mapping applications could merely return a list of landmarks without any analysis, sorting and/or filtering. Rather, the subject mechanisms described herein are capable of creating a “short list” of landmarks in view of relevant factors.

As such, the subject popularity index engine102can consider the relationship of the landmark to other locations and/or factors. In doing so, the popularity index engine102can consider the landmark itself together with other entities and entities in a predetermined area that surrounds it. More particularly, the popularity index engine102can consider other factors such as POIs in proximity to the landmark202, entities that share a common (or similar) name with the landmark204, and transportation infrastructure or access routes206to the landmark. Thus, the popularity index engine102can more accurately, based upon popularity, render a short list of landmarks to a user.

The popularity index engine102can consider 1 to M POIs, where M is an integer. It is to be appreciated that 1 to M POIs can be referred to collectively or individually as POIs202. By way of example, a POI202can be a hotel, restaurant, night club, hot spot, etc. in proximity to a landmark. A threshold to determine proximity can be defined by a radius, perimeter, or other area defining boundary that surrounds the landmark. This boundary can predefined by a user or inferred based upon the type of landmark, real time and/or expected weather conditions, time of day, etc.

As well, the popularity index engine102can consider other entities that share a common name with the landmark. It is to be understood that the popularity engine102can consider common (or similarly) named entities 1 to N, where N is an integer. It is to be appreciated that 1 to N common named entities can be referred to collectively or individually as common named entities204.

By way of example and not limitation, suppose a user is considering a visit to the Space Needle in Seattle, Wash. The popularity index engine102can determine a number of entities that share the same or similar name as the Space Needle. For instance, if there exists a Space Needle Restaurant, Space Needle Hotel and a Space Kneadle Bakery within a predetermined or inferred boundary, these factors will be considered in determining the popularity index rating106.

Continuing with the example, the popularity index engine102can consider 1 to P access routes to the landmark, where P is an integer. These 1 to P access routes206can represent any path granting access to the landmark. Additionally, it is to be understood that the subject ranking mechanisms can consider weights of particular factors (e.g.,202,204,206) whereby more weight can be given to one factor over another. For example, suppose a user is more interested in POIs within walking distance (e.g., radius, perimeter). Accordingly, the POI factor can be given more weight than the Common Name and/or Access Route factors in determining the popularity index rating106.

Additionally, temporal factors (e.g., time) can be considered to determine popularity and/or interest on a given day, time of day, season, etc. By way of example, a museum landmark may be more desirable in cold winter months than a beach venue. Moreover, the type landmark can be considered to assist in intelligently rendering suggested landmarks to a user. By considering all, or a subset of these factors, the popularity index engine102can generate a popularity index rating106for each landmark in a geographic area106thereby presenting a short list to a user. Although specific criteria and factors are shown inFIG. 2, it is to be understood and appreciated that other factors (e.g., demographics, weather, age of landmark, age of traveler) can be considered and thereafter included into the identification and ranking mechanisms herein. For example, a younger traveler might be more interested in night life and hot spots. Accordingly, these types of rating factors can be given more weight than other factors in order to adhere to a user preference. These additional factors are to be considered within the scope of this specification and claims appended hereto.

At302, a city request (or geographical area request) is received. Once received, the city and/or geographical area can be analyzed at304. It will be appreciated that landmarks and corresponding POIs, common name entities, transportation infrastructures, etc. can be determined as a result of this analysis. Additionally, other factors can be obtained which correspond to the received landmark. For example, time of day, time of year, weather, etc. can be determined and subsequently weighed into a ranking of the landmark at306.

Although aspects described herein are directed to employing a numerical value that represents a number of POIs, common name entities, access routes, etc., it is to be understood that other aspects can employ additional factors with respect to determining a rank of a particular landmark. By way of example, applicable POIs, common name entities, access routes, etc. can also be analyzed thus establishing a weight in addition to a numerical value of the number of items. This weight can then be factored into the determination of the landmark rating306. For instance, if one landmark has 3 five-star hotels within a given predefined distance whereas another landmark only has 1 motel. It will be understood and appreciated that a greater weight can be applied to the landmark near the five-star hotels. Accordingly, the increased weight of the POIs can be factored into establishing a landmark rating at306in accordance with a predetermined and/or inferred preference.

A determination can be made at308which enables a recursive mechanism of rating additional landmarks. If additional landmarks are determined at304, the methodology can return to304thereby analyzing the additional landmark(s) and thereafter establishing a corresponding landmark rating. If, on the other hand, additional landmarks are not identified, the landmark ranks can be rendered to a user at310.

It is to be understood and appreciated that the rated landmarks can be rendered (e.g., displayed) to a user in any format. In one aspect, the landmarks can be ranked based upon the overall rating determined at306. In another aspect, the landmarks can be ranked based upon a rating that considers an isolated factor (e.g., number of POIs). Moreover, the landmarks can be filtered based upon any desired criteria thus, limiting the number of items in a resultant set to a user. The act of filtering can include filtering, ranking and/or sorting landmarks based upon the item-specific rating established at306.

Turning now toFIG. 4, a system400that facilitates rating a landmark in accordance with an aspect of the rating mechanism is shown. Generally, system400can include a receiving component402and a popularity index engine102. In operation, the receiving component402facilitates receiving a request that corresponds to a geographic area or city. In one aspect, the receiving component402can accept manual requests from a user while other aspects employ automated mechanisms via receiving component402. For instance, other aspects exist that automatically request landmark information with respect to a particular city or geographic area. These automated aspects can be particularly useful for industries such as hotels, airlines, etc. In these aspects, when a traveler books a reservation to visit a particular area, an automatic request can be generated thereby prompting identification and subsequent ranking of nearby landmarks and/or sites to visit.

The popularity index engine102can include a landmark identification engine404and a rating engine406. The landmark identification engine404can facilitate identifying landmarks located within a predetermined or inferred boundary of a city or other geographic area. Once identified, the rating engine406can determine factors (e.g., POIs, common name entities, access routes) that correspond to the landmark. Subsequently, the rating engine406can employ the determined factors thereby establishing a rating (e.g., popularity index). As will be described infra, this rating can be used to rank, filter and/or sort the landmark with respect to other identified landmarks.

It is to be appreciated that artificial intelligence (AI) reasoning and/or rules-based logic can be employed in connection with the popularity index engine102and components associated therewith (e.g., landmark identification engine404, rating engine406). These alternative aspects will be described in greater detail with reference toFIGS. 8 and 9infra.

Illustrated inFIG. 5is yet another block diagram of system400in accordance with an aspect of the landmark rating mechanisms. In particular, landmark identification engine404can include 1 to R rating components, where R is an integer. It is to be appreciated that rating components 1 to R can be referred to individually or collectively as rating components502. As described above, aspects can be configured to consider any desired rating component into an overall rating of a landmark. This rating can be established via rating engine406which can employ the rating components502as well as optionally applicable weight factors thus determining a popularity index rating.

Additionally, as illustrated inFIG. 6, the landmark identification engine404can employ an analyzer component602that facilitates identification of landmarks in a given area (e.g., city, state, region) as well as rating factors502that correspond to each of the identified landmarks. For example, the analyzer component602can employ local or remote means (e.g., data store, Internet) to determine landmarks in a requested city and/or area. Additionally, once a landmark(s) is determined the analyzer component602can interrogate additional sources (e.g., Internet, local data stores, remote data stores) to obtain relevant rating components502(e.g., POIs).

The rating engine406can employ an output from the landmark identification engine404and accordingly determine an appropriate rating based at least in part upon a rating component(s)502. As well, rating engine406can consider other user defined and/or inferred threshold values and limits when determining a particular rating of a landmark.

FIG. 7illustrates a more specific example of a system700that ranks a landmark in accordance with an aspect of the invention. It is to be understood and appreciated that, althoughFIG. 7illustrates an aspect that employs specific criteria, other aspects exist that employ additional and/or modified versions of the criteria shown. These additional aspects are to be considered within the scope of this invention and claims appended hereto.

System700generally includes a landmark identification engine702that facilitates identifying a landmark(s) and/or rating criteria. More particularly, landmark identification engine702can include an analyzer component704that identifies a landmark or group of landmarks that corresponds to a desired city/area. Additionally, the analyzer component704can facilitate identifying rating factors that correspond to each of the identified landmarks. As shown in the aspect ofFIG. 7, a POI factor component706, a common name factor component708and an infrastructure factor component710can be identified. Although specific rating factor components (706,708,710) are illustrated inFIG. 7, it is to be understood that other factors can be employed to facilitate rating a landmark or site.

A rating engine714can be provided that employs a landmark rating algorithm to rate the landmark(s) locations in a city. Effectively, the rating engine714can build a popularity context or index. In the example illustrated inFIG. 7, a popularity context of a particular landmark can be calculated using several attributes of the landmark location.

More particularly, the rating engine714can consider a number of commercial and non-commercial entities or POIs within a pre-specified or inferred distance (e.g., radius, perimeter, boundary) from a landmark. This number of POIs is represented inFIG. 7as a POI factor706. As described earlier, other aspects can also consider weights associated to specific POI factors706in determining a landmark rating.

Entities that share a common, or similar, name with the identified landmark(s) can also be considered in accordance with the aspect ofFIG. 7. For example, if all, or a portion of, the landmark name is used in a name of another entity within the predefined or inferred distance, this entity can be considered in rating the instant landmark. A total number of commonly (or similarly) named entities will be considered. Moreover, these entities can also employ weight components in determining a rating. It is to be understood and appreciated that a fuzzy name matching mechanism can be employed to determine similarly named entities. The criteria and/or specifications of the matching mechanism can be user defined and/or inferred from the type or name of the landmark.

Still another rating factor considered in the example ofFIG. 7is an infrastructure factor component710. In other words, the analyzer engine704can determine, and the rating engine714can employ, the number of streets (e.g., access routes) that lead to the identified landmark(s). As well, the rating engine714can consider the types of streets or access routes (e.g., pedestrian only vs. car only vs. truck only, etc.) by applying appropriate weights thereto. Moreover, the infrastructure factor component710can include a component that identifies if public transportation is available to, or within a defined distance from the landmark.

A ranking component716and/or filter component718can be employed to more intelligently render resultant data to a user. For example, the ranking component716can facilitate hierarchically sorting the landmark ratings generated by the rating engine714. In other words, the ranking component716can compare all rated landmarks thus compiling a short list that can be rendered to a user.

Additionally, a filter component718can be employed to further personalize the landmark list. For example, if a user is only interested in museum venues, the filter component718can facilitate rendering a subset of the ranked landmarks in accordance with a predetermined query or filter constraint (e.g., museum venue). It will be understood that one novel feature of landmark ranking is that it does not necessarily return the “touristy” spots on the top but rather can recommend locations of interest based upon popular gathering locations, etc.

FIG. 8illustrates a methodology of computing a landmark rank in accordance with the aspect ofFIG. 7. More particularly,FIG. 8illustrates a summarization of an algorithm in accordance with an aspect of the landmark rating/ranking mechanism. Initially, at802a determination of landmarks within a predefined and/or inferred distance is generated. As described above, this determination can be prompted by a request and can be made from information relating to a city and/or geographic area identified in the request.

At804, a determination is made if the instant landmark is rated and/or ranked. If so, a stop block is reached. If the instant landmark is not yet ranked, at806, the landmark is analyzed. This analysis can include identifying POIs, common name entities, access routes, and other relevant rating factors. At808,810and812, a POI factor, common name factor and infrastructure factor can be calculated respectively. It is to be understood and appreciated that the calculation of these factors can be implemented in any order. As well, weights can be applied as appropriate and as described above.

A popularity index rating is generated at814. Effectively, in one aspect, the number of POIs, commonly named entities and number of access routes can be added to establish the landmark rating at814. The established popularity index rating can be stored locally or remotely at816. As illustrated, the methodology is recursive. In other words, once a popularity index rating is established for a landmark, the methodology returns to804where a determination is made if additional unrated landmarks exist. If so, another popularity index rating can be determined and stored for the new item(s). If not, a stop block is reached.

Once stored, it will be appreciated that the landmarks can be aggregated, ranked, sorted and/or filtered based at least in part upon the popularity index rating. It will be understood that these mechanisms and methodologies can facilitate identification and delivery of more focused landmark suggestions based at least in part upon POIs, commonly named entities, transportation infrastructure, interest, time/date of visit, popularity, years in existence, etc.

With reference now toFIG. 9, an alternate aspect of a landmark ranking system900is shown. In particular, system900includes a rule based logic component902. In accordance with this alternate aspect, an implementation scheme (e.g., rule) can be applied to define and/or implement a ranking scheme. It will be appreciated that the rule-based implementation can define constants, weights and/or thresholds (e.g., radius, perimeter, boundary) associated with identifying, ranking, sorting and filtering landmarks and corresponding criteria. In response thereto, the rule-based implementation can facilitate selection of landmarks based upon a calculated popularity index by employing a predefined and/or programmed rule(s).

It will be appreciated that any of the specifications utilized in accordance with the subject invention can be programmed into a rule-based implementation scheme. In the exemplary aspect ofFIG. 9, the rule based logic component902can be programmed or configured in accordance with any user-defined preference.

FIG. 10illustrates a landmark ranking system1000that employs an AI component1002which facilitates automating one or more features in accordance with the subject innovation. It is to be understood that this AI component1002can be employed in addition to, or in place of the rules-based logic component902shown inFIG. 9.

The subject invention (e.g., in connection with identification, ranking, sorting, filtering) can employ various AI-based schemes for carrying out various aspects thereof. For example, a process for determining a user preference to be employed in the popularity index generation can be facilitated via an automatic classifier system and process.

A classifier is a function that maps an input attribute vector, x=(x1, x2, x3, x4, xn), to a confidence that the input belongs to a class, that is, f(x)=confidence(class). Such classification can employ a probabilistic and/or statistical-based analysis (e.g., factoring into the analysis utilities and costs) to prognose or infer an action that a user desires to be automatically performed. In the case of landmark rating/ranking, for example, attributes can be word or phrases or other data-specific attributes derived from the words or name of the landmark (e.g., presence of key terms), and the classes are categories or areas of interest (e.g., types of landmarks).

As will be readily appreciated from the subject specification, the subject invention can employ classifiers that are explicitly trained (e.g., via a generic training data) as well as implicitly trained (e.g., via observing user behavior, receiving extrinsic information). For example, SVM's are configured via a learning or training phase within a classifier constructor and feature selection module. Thus, the classifier(s) can be used to automatically learn and perform a number of functions, including but not limited to determining, according to a predetermined criteria, when a particular criteria should be given special weight, when to consider landmark corresponding criteria (e.g., POI), when to identify a landmark, what a user preference is with respect to landmark at a given time, etc.

The system bus1108can be any of several types of bus structure that may further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. The system memory1106includes read-only memory (ROM)1110and random access memory (RAM)1112. A basic input/output system (BIOS) is stored in a non-volatile memory1110such as ROM, EPROM, EEPROM, which BIOS contains the basic routines that help to transfer information between elements within the computer1102, such as during start-up. The RAM1112can also include a high-speed RAM such as static RAM for caching data.

The computer1102further includes an internal hard disk drive (HDD)1114(e.g., EIDE, SATA), which internal hard disk drive1114may also be configured for external use in a suitable chassis (not shown), a magnetic floppy disk drive (FDD)1116, (e.g., to read from or write to a removable diskette1118) and an optical disk drive1120, (e.g., reading a CD-ROM disk1122or, to read from or write to other high capacity optical media such as the DVD). The hard disk drive1114, magnetic disk drive1116and optical disk drive1120can be connected to the system bus1108by a hard disk drive interface1124, a magnetic disk drive interface1126and an optical drive interface1128, respectively. The interface1124for external drive implementations includes at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies. Other external drive connection technologies are within contemplation of the subject invention.

A number of program modules can be stored in the drives and RAM1112, including an operating system1130, one or more application programs1132, other program modules1134and program data1136. All or portions of the operating system, applications, modules, and/or data can also be cached in the RAM1112. It is appreciated that the invention can be implemented with various commercially available operating systems or combinations of operating systems.

A user can enter commands and information into the computer1102through one or more wired/wireless input devices, e.g., a keyboard1138and a pointing device, such as a mouse1140. Other input devices (not shown) may include a microphone, an IR remote control, a joystick, a game pad, a stylus pen, touch screen, or the like. These and other input devices are often connected to the processing unit1104through an input device interface1142that is coupled to the system bus1108, but can be connected by other interfaces, such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, etc.

A monitor1144or other type of display device is also connected to the system bus1108via an interface, such as a video adapter1146. In addition to the monitor1144, a computer typically includes other peripheral output devices (not shown), such as speakers, printers, etc.

When used in a LAN networking environment, the computer1102is connected to the local network1152through a wired and/or wireless communication network interface or adapter1156. The adapter1156may facilitate wired or wireless communication to the LAN1152, which may also include a wireless access point disposed thereon for communicating with the wireless adapter1156.

When used in a WAN networking environment, the computer1102can include a modem1158, or is connected to a communications server on the WAN1154, or has other means for establishing communications over the WAN1154, such as by way of the Internet. The modem1158, which can be internal or external and a wired or wireless device, is connected to the system bus1108via the serial port interface1142. In a networked environment, program modules depicted relative to the computer1102, or portions thereof, can be stored in the remote memory/storage device1150. It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers can be used.

Referring now toFIG. 12, there is illustrated a schematic block diagram of an exemplary computing environment1200in accordance with the subject landmark rating mechanism(s). The system1200includes one or more client(s)1202. The client(s)1202can be hardware and/or software (e.g., threads, processes, computing devices). The client(s)1202can house cookie(s) and/or associated contextual information by employing the invention, for example.

The system1200also includes one or more server(s)1204. The server(s)1204can also be hardware and/or software (e.g., threads, processes, computing devices). The servers1204can house threads to perform transformations by employing the invention, for example. One possible communication between a client1202and a server1204can be in the form of a data packet adapted to be transmitted between two or more computer processes. The data packet may include a cookie and/or associated contextual information, for example. The system1200includes a communication framework1206(e.g., a global communication network such as the Internet) that can be employed to facilitate communications between the client(s)1202and the server(s)1204.

Communications can be facilitated via a wired (including optical fiber) and/or wireless technology. The client(s)1202are operatively connected to one or more client data store(s)1208that can be employed to store information local to the client(s)1202(e.g., cookie(s) and/or associated contextual information). Similarly, the server(s)1204are operatively connected to one or more server data store(s)1210that can be employed to store information local to the servers1204.