Methods and systems for providing residence recommendations based on personal interests

Embodiments for providing residence recommendations by one or more processors are described. At least one interest associated with a user is determined. At least one interest location associated with the at least one interest is identified. A score for each of a plurality of potential residences for the user is calculated at least based on a distance between the respective potential residence and each of the at least one interest locations. A signal representative of the calculated score for each of the plurality of potential residences is generated.

BACKGROUND OF THE INVENTION

Field of the Invention

The present invention relates in general to computing systems, and more particularly, to various embodiments for providing residence recommendations based on personal interests.

Description of the Related Art

In modern society, many people relocate, or move, more than a dozen times in their lifetime. In many cases, these moves are to a new city, town, or state, and the individual has little to no knowledge about the new location/area. Typically, the individual hires a realtor who provides them with various options and pricing. However, the realtors often cannot provide advice in the same way a friend of the individual can. That is, a friend living in the area knows the tendencies of residents who live in the area, the good and bad parts of town, and most importantly, the individual looking to move (e.g., their interests and lifestyle).

Although there are many resources that contain information to help an individual in selecting a new residence, current systems do not tailor search results in a way that takes the individual's personal interests and lifestyle into account.

SUMMARY OF THE INVENTION

Various embodiments for providing residence recommendations by one or more processors are described. In one embodiment, by way of example only, a method for providing residence recommendations, again by one or more processors, is provided. At least one interest associated with a user is determined. At least one interest location associated with the at least one interest is identified. A score for each of a plurality of potential residences for the user is calculated at least based on a distance between the respective potential residence and each of the at least one interest locations. A signal representative of the calculated score for each of the plurality of potential residences is generated.

DETAILED DESCRIPTION OF THE DRAWINGS

As discussed above, many people relocate, or move, more than a dozen times in their lifetime. In many cases, these moves are to a new area about which the individual knows very little. Although realtors can assist with providing options and pricing, they often do not know the individuals interest, lifestyle, etc. in the same way a close acquaintance (e.g., friend, family member, etc.) does.

There are also various services (e.g., websites, applications, etc.) available for assisting individuals in finding new residences by providing various types of information. For example, some websites combine listings from various sources on the internet into a single map-based interface. The individual may be able to set his/her work location and the target travel time from the location to highlight a specific region for possible listings. The user may also be asked for preferences, such as location, housing type (e.g., stand-alone house, apartment, etc.), number of bedrooms and/or bathrooms, price, pet policy, laundry, and amenities. After specifying a move in date and monthly budget, users may be able view suggested listings, perhaps including photographs, and highlight their favorite options. When reviewing the preferred options, appointments may be arranged for viewing or more information.

While the current systems provide users with information on listings based on location, they do not provide options based on their personal interests. Also, although maps may be provided that give the user some idea of the neighborhood, the user usually must conduct extensive research to learn more about typical neighbors and nearby activities from other resources. If the user was to search for a house or apartment using only one of these services, they would have no idea what the local area is like, what type of residents reside at the complex, and what shops, restaurants, activities, etc. are near that location.

That is, although there are many resources that contain information to help an individual in selecting a new residence, current systems do not tailor search results in a way that takes the individual's personal interests and lifestyle into account and/or cannot provide recommendations based on personal knowledge of the individual.

For example, a friend or family member of the individual may know that he/she is an avid runner (or jogger) and would most likely prefer to live near running trails and routes, such as a greenway. The friend may also have an understanding of the general lifestyle preferences of the individual. For example, the friend may realize that the individual recently graduated from college, is new to the area in question, and may prefer to live relatively close to other individuals who are young professionals, as well as near restaurants and bars.

There are currently no systems available that combine the individual's preferences (e.g., budget, location, room number, etc.) with the individual's personal interests, lifestyle, etc. in order to tailor recommendations with respect to housing searches.

To address these needs, some embodiments described herein provide methods and systems for providing residence recommendations based on, at least in part, the personal interests, lifestyle, preferences, etc. of the individual.

According to some embodiments described herein, this is accomplished utilizing data (structured and/or unstructured data) from external sources, such as various online sources (e.g., websites, databases, etc.) related to, for example, housing searches. Based on data from these sources, the system may map locations associated with interests and preferences, which are near each residence. For example, if it is determined that the individual (or user) is interested in outdoor activities, residences near parks and greenways may be identified. If the user enjoys trying new restaurants and unique finds, new up-and-coming restaurants in the area may be indicated and/or used to provide recommendations.

The individual's personal interests may be identified and/or determined in various ways. For example, in some embodiments, the individual may actively or explicitly indicate (or choose) his/her interests to the system (e.g., manual entry, pull down menus, etc.). However, in some embodiments, the individual's personal interests are (also) determined from searching and/or scanning data sources associated with the individual, such as social media activity and electronic communications (e.g., email, text messages, etc.). For example, the individual's social media activity (e.g., feedback left on social media, comments, etc.) may indicate that the individual has an interest in a particular subject or activity (e.g., running, golf, books, swimming, etc.), via, for example, keywords and key phrases (e.g., in social media posts and/or the user's comments). As such, in some embodiments, the individual's personal interests may (also) be determined or identified with little or no active input from the individual. Additionally, if the individual has social media contacts (or “friends”) who live or work in the area, the individual may be alerted to this so that he/she may request validation of the recommendations from those people, assisting the individual in selecting an option.

In some embodiments, the individual's personal interests are utilized together with specific preferences with respect to housing, such as location, size, costs, amenities, etc. For example, prospective or potential residences may be initially selected based on the preferences indicated by the individual. The prospective residences may then be scored and/or ranked based on, for example, the proximity thereof to locations associated with the individual's personal interests.

In some embodiments, the individual may be able to provide feedback to the system regarding, for example, the identified personal interests and/or the scoring (or ranking) of the prospective residences. For example, if the individual believes that the system has inaccurately prioritized a particular interest (or activity), the individual may alert the system of such so that the prospective residences may be rescored and/or such feedback may be utilized in future searches. Further, the methods and systems described herein may utilize feedback left by other individuals (e.g., with respect to the embodiments described herein and/or feedback left on external data sources related to accommodations).

Although the present disclosure repeatedly refers to “housing” and/or “residential” searches, it should be understood that the embodiments described herein may be applied to any type of accommodations, related to personal living, recreation, and/or business. That is, besides assisting individuals in searching for residences, the methods and systems described herein may also be applied to searching for hotels, vacation rentals, commercial/business locations, etc.

In particular, in some embodiments, a method, by one or more processors, for providing residence recommendations, again by one or more processors, is provided. At least one interest associated with a user is determined. At least one interest location associated with the at least one interest is identified. A score for each of a plurality of potential residences for the user is calculated at least based on a distance between the respective potential residence and each of the at least one interest locations. A signal representative of the calculated score for each of the plurality of potential residences is generated.

A plurality of residence preferences associated with the user may be received. The plurality of potential residences may be selected based on the plurality of residence preferences.

Each of the selected plurality of potential residences may match at least a predetermined percentage of the received plurality of residence preferences. The determining of the at least one interest associated with the user may include at least one of receiving an indication of the at least one interest from the user, automatically searching at least one data source associated with the user, or a combination thereof.

The determining of the at least one interest associated with the user may include automatically searching at least one data source associated with the user, wherein the at least one data source includes at least one of social media activity, electronic communications, or a combination thereof.

The received plurality of residence preferences may include at least one residence amenity preference. The calculating of the score for each of the plurality of potential residences may include comparing amenities for each of the plurality of potential residences to the at least one amenity preference.

The generating of the signal representative of the calculated score for each of the plurality of potential residences may include causing an indication of the calculated scores for each of the plurality of potential residences to be displayed on a display device.

In some embodiments, the methods and/or systems described herein utilize “machine learning,” “cognitive modeling,” “predictive analytics,” and/or “data analytics,” as is commonly understood by one skilled in the art. Generally, these processes may include, for example, receiving and/or retrieving multiple sets of inputs, and the associated outputs, of one or more systems and processing the data (e.g., using a computing system and/or processor) to generate or extract models, rules, etc. that correspond to, govern, and/or estimate the operation of the system(s), or with respect to the embodiments described herein, users' feedback, reactions, satisfaction, etc. with respect to the scoring of prospective residences based on personal interests, etc. as described herein. Utilizing the models, the performance (or operation) of the system (e.g., utilizing/based on new inputs) may be predicted and/or the performance of the system may be optimized by investigating how changes in the input(s) effect the output(s).

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows:

In the context of the present invention, and as one of skill in the art will appreciate, various components depicted inFIG. 1may be located in, for example, personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, mobile electronic devices such as mobile (or cellular and/or smart) phones, personal data assistants (PDAs), tablets, wearable technology devices, laptops, handheld game consoles, portable media players, etc., as well as computing systems in vehicles, such as automobiles, aircraft, watercrafts, etc. For example, some of the processing and data storage capabilities associated with mechanisms of the illustrated embodiments may take place locally via local processing components, while the same components are connected via a network to remotely located, distributed computing data processing and storage components to accomplish various purposes of the present invention. Again, as will be appreciated by one of ordinary skill in the art, the present illustration is intended to convey only a subset of what may be an entire connected network of distributed computing components that accomplish various inventive aspects collectively.

Referring now toFIG. 2, illustrative cloud computing environment50is depicted. As shown, cloud computing environment50comprises one or more cloud computing nodes10with which local computing devices used by cloud consumers, such as, for example, cellular telephone or PDA54A, desktop computer54B, and/or laptop computer54C, and vehicles (e.g., automobiles, aircraft, watercraft, etc.)54N, may communicate.

Still referring toFIG. 2, nodes10may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment50to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices54A-N shown inFIG. 2are intended to be illustrative only and that computing nodes10and cloud computing environment50can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser).

In some embodiments, the individual (or user) utilizes the methods and systems described herein using, for example, a software application (e.g., mobile application) or a website via a computing device, such as a mobile electronic device, desktop PC, etc. The individual may first create a “user account,” as is commonly understood. For example, the individual may provide basic information, such as their name, age, gender, contact information, current address, move-in date, budget, etc. This profile may be stored by the system.

The individual may then enter (or select) the region in which they are looking for a residence. In some embodiments, the individual has the option to select a single, particular city (or town, county, locale, etc.) or a city and the surrounding area(s). If several cities are within a certain distance of each other (e.g., 10 miles), the individual may be provided with the option of selecting multiple cities, essentially creating a larger region for the residence search. Similarly, in some embodiments, the individual may be able to select a city along with any other cities within a particular distance (e.g., 20 miles) of the selected city. The user may also be able to limit the region. For example, the individual may be able to enter/select his/her work location and choose a maximum distance from that location. In some embodiments, only potential residences within the selected region will be presented to the individual.

In some embodiments, the individual also provides (or chooses, selects, etc.) “mandatory,” or at least relatively important, residence preferences (or features). Examples include, but are not limited to, move-in date (e.g., availability of potential residences), budget (e.g., maximum rent, monthly payment, etc.), residence type (e.g., stand alone house, apartment, multi-family, etc.), and number of bedrooms and/or bathrooms (i.e., perhaps multiple options for number of bedrooms/bathrooms).

The individual may also add “optional,” or at least relatively unimportant, residence preferences (or amenities). These options may include, for example, whether or not the residence has a swimming pool (e.g., private or community), fitness center, golf course, covered parking, furnished/unfurnished, deck/patio, pet policy, laundry, and elevator.

In some embodiments, the individual also adds at least some of their personal interests (or activities, etc.). For example, the individual may be able to manually enter various personal interests of theirs. However, in some embodiments, the individual may be able to select from a list including, for example, museums, nature & parks, sights/landmarks, tours, shopping, fun/games, outdoor activities, nightlife, concerts, water/amusement parks, and spas/wellness. The number of personal interests that may be entered and/or selected may be limited (e.g., a maximum of four categories) to limit the number of potential residences.

Each possible interest may be mapped to trigger words or keywords (or key phrases). For example, if the individual enters running/jogging as a personal interest, as described below, the system searches for greenways (or greenbelts), large parks, running trails, established running clubs/stores, etc. In some embodiments, the system links potential specified interests to preset trigger words that search for the locations within the selected region that are associated with the personal interest(s), such as restaurants, churches, parks, museums, gyms, music venues, etc., which may be identified using various data sources (e.g., online data sources, such as travel-related websites).

In some embodiments, the individual also indicates their dining preferences. For example, the individual may indicate how often they eat out, their preferred types of cuisine, and/or the type of restaurant(s) they frequently attend. This option may be included regardless of whether or not restaurants/food was indicated as a personal interest of the individual. These actions may group all of the desired interest locations as key search words.

In some embodiments, personal interests of the individual may (also) be determined (or identified) automatically. For example, the individual may optionally grant the system to one or more sources of data associated with the individual (or personal data sources), such as social media profiles/activity, electronic communications (e.g., emails, text messages, etc.), online accounts (e.g., related to shopping), etc. Information may be identified in such data sources that allow the system to determine the personal interests of the individual. For example, with respect to social media, personal interests may be identified or determined based on keywords identified in social media posts that the individual responded to and/or comments left by the individual or social media groups to which the individual belongs (e.g., a running/jogging group). The identified personal interests may be added to the individual's profile and used in scoring/grading potential residences as described below. In some embodiments, the individual's contacts (e.g., via social media, emails, etc.) may also be searched for people who live or work in the region in which the individual is searching for a residence.

In some embodiments, all of the information described above is stored in the individual's user profile and then used to perform a search for residences as described below. However, it should be understood that in some embodiments, the various types of information may simply be entered each time the individual desires to perform a search (i.e., no user profile is created/stored). It should also be understood that the various steps described (e.g., entering/determining the various types of information) may be performed in a different order than those described herein (e.g., the user's personal interests may be selected/determined first).

When a search is to be performed, in some embodiments, the system searches various data sources (e.g., online databases, websites, etc.) to locate potential residences for the individual as well as locations associated with the individual's personal interests. For example, various sources of structured and unstructured data may be utilized, such as websites related to locating/finding housing, travel-related websites, online mapping applications, online reviews, etc.

Referring now toFIG. 4, a map400of an exemplary region is shown. The region may correspond to a region selected by an individual for a housing search as described above. In some embodiments, the map400shown may be displayed on a display screen of a computing device (e.g., a mobile electronic device, desktop PC, etc.). However, it should be understood that the map400may be provided to illustrate the functionality of the methods and systems described herein. In the depicted embodiment, the region corresponds to an urban area (e.g., a city or town) with various roadways, landmarks, etc. However, it should be understood that the region is simply provided as an example, as any region may be used, including rural areas or regions with multiple cities/towns that are separated by rural areas.

Referring now toFIG. 5, in some embodiments, all residential listings in the region400that fit/match the mandatory preferences of the user (e.g., number of bedrooms/bathrooms, residence type, budget, etc.) are identified and displayed. In the example shown, five such residences402-410(and the locations thereof) are displayed on the map400. In some embodiments, rather than selecting all such listings, the potential residences are further filtered based on the individual's optional preferences. For example, in some embodiments, only the listings that fit all of the individual's mandatory preferences and a predetermined percentage (e.g., 60%) of the individual's optional preferences are selected (or identified) and displayed.

The individual's personal interests (or activities) are then determined/entered and/or utilized to identify locations associated with those interests (or interest locations) in the region. The interests may be linked to common location words that match the interests (through established trigger words). As described above, locations that match the interests may be identified through various data sources, such as online databases, websites, etc. As described above, in some embodiments, the individual selects a particular number of interests (e.g., his/her most important), and only locations associated with those interests are utilized. Further, in some embodiments, the available data sources are utilized so that only the interest locations associated with businesses, activities, etc. that have an average rating above a predetermined threshold (e.g., 3.5 out of 5) are selected. For example, only restaurants with at least an average rating of 3.5 out of 5 may be selected and/or utilized as described below. The appropriate interest locations within the region, which are associated with the individual's personal interests, are mapped, as shown inFIG. 6. In the example shown, six interest locations412-422are shown (though it should be noted that this number may vary significantly in other embodiments).

Still referring toFIG. 6, an “interest point score” is then determined or calculated for each of the potential residences402-410(i.e., the potential residences402-410are scored with respect to the interest locations412-422in the region). As one exemplary method for scoring (and/or ranking) the potential residences402-410, each of the potential residences may be awarded points based on the proximity thereof to the various interest locations412-422. For example, each potential residence may be awarded one or more points for each interest location within a predetermined distance/range (e.g., 4 miles).

In some embodiments, the interest locations are grouped into four different categories with respect to their distance from each of the potential residences: Category A, Category B, Category C, and Category D. In such an embodiment, each of the potential residences402-410may be awarded 8 points for each of the interest locations within 0.5 miles of that residence (i.e., Category A), 5 points for each of the interest locations between 0.51 and 1.0 mile (Category B), 3 points for each between 1.1 and 2.0 miles (Category C), and 1 point for each of the interest locations between 2.1 and 4.0 miles (Category D). In this manner, an interest point score may be determined or calculated for each of the potential residences.

In some embodiments, a final residence score for each of the potential residences is then determined or calculated. The final residence score may be determined by multiplying the interest point score for each potential residence by the percentage of the individual's optional preferences (or amenities) that is matched/met by that residence. For example, if the interest point score for a particular potential residence is 30, and that potential residence has 80% of the optional preferences indicated by the individual, the final residence score would be 24.

A signal representative of the final residence score for each of the potential residences may then be generated, resulting in, for example, a list of the potential residences, along with the final residence scores thereof, being provided to the user (e.g., being displayed on a display device, sent via electronic communication, etc.). In some embodiments, the potential residences are listed in order based on their final residence scores (e.g., listed order of descending final residence score/from highest to lowest final residence score). Additionally, each residence may be displayed with the various details or information about the residence (e.g., type, size, amenities, costs, etc.) and/or the community/surrounding area (e.g., the interest locations within a particular distance).

The individual may be provided with the option to “save” (or select) or “discard” the recommended residences, perhaps individually or the entire list. Additionally, contact details (e.g., phone number, email address, etc.) may be provided to the individual for those of the recommended residences (or at least those that the individual has saved/selected) so that the individual may contact the appropriate entity regarding additional information.

In some embodiments, the individual may also be able to provide feedback to the system related to the recommended residences. For example, if the individual thinks the list of recommended residences and/or the scoring/ranking thereof does not accurately reflect his/her personal interests (and/or residence preferences), the individual may be able to indicate such. The feedback may be utilized by the system to perform the search/scoring again and/or for future residence searches (e.g., for the same individual and/or other individuals). It should be noted that the user feedback may be received and/or retrieved in any suitable manner. For example, the user may actively/intentionally provide feedback via questionnaires, etc., such as when they are provided with a list of recommended residences. Additionally, other forms of communication may be monitored (perhaps at the option of the user). For example, emails, texts, social media posts, etc. may be scanned for keywords indicative of the user's satisfaction with the search/scoring of the residences.

Further, in some embodiments, the individual's personal data (e.g., social media, emails, contact list, etc.) is searched for contacts who live/work in the region. If any such contacts are identified, the individual may be provided with an indication of such (e.g., via pop-up window, email, text message, etc.) so that the individual may seek additional advice.

Referring now toFIG. 7, a flowchart/block diagram of a method (or apparatus logic)700for generating a user profile for an individual according to some embodiments is shown. However, it should be understood that the method700may also be applicable to the general process for collecting information related to the individual, which is utilized to perform the residence searches/scoring described herein (i.e., as opposed to the creating of a specific user profile).

At block702, the user enters the location (or region) in which he/she is searching for a residence. As described above, the user may enter (or select) a city (or town), along with nearby regions/areas, as well as be provided the option to enter a work location. At block704, the user enters the type(s) of residence he/she prefers or needs (e.g., apartment, stand-alone house, multi-family, etc.). At block706, the user enters residence preferences (e.g., important or mandatory preferences), such as the number of bedrooms/bathrooms, price, move in date, etc. At block708, the user enters desired amenities (e.g., relatively unimportant preferences), such as details related to parking, a gym, a pool, a golf course, laundry, pet policy, etc.

Still referring toFIG. 7, in some embodiments, at block710, the user links (and/or grants the system access to) personal data sources, such as social media activity, emails, etc. At block712, personal interests may be identified or determined. As described above, the personal interests of the user may be manually entered or selected by the user and/or determined from the personal data sources. In some embodiments, at block714, the user selects a predetermined number of interests. For example, the user may select four personal interests (e.g., museums, nature/parks, shopping, and concerts) which are utilized to perform (and/or limit) the residence searching described herein. In some embodiments, at block716, the user enters (or selects) details related to his/her restaurant preferences (e.g., cuisine, type of restaurant, etc.). It should be understood that the selection of restaurant preference may be utilized regardless of whether or not restaurants are determined to be one of the user's personal interests.

Referring now toFIG. 8, a flowchart/block diagram of a method (or apparatus logic)800for recommending residences according to some embodiments is shown. At block802, a user profile (e.g., fromFIG. 7) is uploaded/received/retrieved. The user profile may contain, for example, the location/region for the search, the desired residence type, preferences, and personal interests of the user. At block804, potential residences in the appropriate region are identified. That is, a list of residences in the region that (at least to some extent) match the user's preferences/amenities is created. At block806, the personal interests of the user are retrieved from the user profile and/or otherwise determined. As described above, the personal interests may include user entered interests, interests identified from personal data sources, and a selection of the interests most important to the user.

Still referring toFIG. 8, at block808, interest locations, associated with the personal interests of the user, within the region are identified and/or selected, as described above (e.g., via online databases/websites, reviews, etc.). At block810, the interest locations are mapped relative to the potential residences. As described above, each of the interest locations may be categorized (e.g., Categories A-D) relative to each potential residence based on the distance between each interest location and each potential residence. At block812, values are assigned to the categories (e.g., 8 points, 5 points, 3 points, and 1 point). At block814, interest scores (or grades) for each of the potential residences are calculated. As described above, the interest scores may be calculated by adding the values for each of the interest locations within a range of the residence and multiplying the sum by the percentage of desired amenities included.

Continuing withFIG. 8, at block816, the potential, or recommended, residences are displayed (or provided) to the user. The residences may be listed based on the calculated scores (e.g., highest to lowest) and other relevant information may be shown (e.g., nearby interest locations, amenities, etc.). At block818, the user selects one or more of the recommended residences (e.g., the user saves at least some of the recommendations). At block820, local advice may be provided by, for example, identifying contacts of the individual who live or work in the region and providing appropriate contact information.

FIG. 9illustrates a flowchart/block diagram of a system (and/or data flow)900for recommending residences according to some embodiments described herein. As described above, a user (or individual) may access the systems/methods described herein via, for example, an application902on a computing device. The application902receives user entered information904(e.g., a user profile) and various types of information from external data sources (e.g., online databases, websites, etc.), such as reviews906, residential listings908, travel-related data910, personal data (e.g., social media, emails, etc.)912, and a mapping application914, and may store any appropriate information/data (e.g., user information, saved searches, etc.) on a database (or memory)916. Recommended residence options918are identified and displayed, utilizing the residential listings908and activities/restaurants in the region (e.g., interest locations)920, as described above.

Turning toFIG. 10, a flowchart diagram of an exemplary method1000for providing residence recommendations, in accordance with various aspects of the present invention, is provided. Method1000begins (step1002) with, for example, a user (or individual) deciding he/she desires to locate a new residence (or other type of accommodations) in a particular region and/or creating a user profile as described above.

At least one interest associated with the user is determined (step1004). The determining of the at least one interest associated with the user may include at least one of receiving an indication of the at least one interest from the user, automatically searching at least one data source associated with the user, or a combination thereof.

At least one interest location associated with the at least one interest is identified (step1006). As described above, the at least one interest location may be identified utilizing external data sources, such as online databases, websites, etc.

A score for each of a plurality of potential residences for the user is calculated at least based on a distance between the respective potential residence and each of the at least one interest locations (step1008). The plurality of potential residences may be selected based on a plurality of received residence preferences. The received plurality of residence preferences may include at least one residence amenity preference. The calculating of the score for each of the plurality of potential residences may include comparing amenities for each of the plurality of potential residences to the at least one amenity preference. Each of the selected plurality of potential residences may match at least a predetermined percentage of the received plurality of residence preferences.

A signal representative of the calculated score for each of the plurality of potential residences is generated (step1010). The generating of the signal representative of the calculated score for each of the plurality of potential residences may include causing an indication of the calculated scores for each of the plurality of potential residences to be displayed on a display device.

Method1000ends (step1012) with, for example, the user selecting at least one of the potential residences based on, for example, the calculated scores. The user may provide feedback (e.g., related to the identified interests, etc.), which may be utilized in subsequent processes/searches.