Abstract:
The present invention provides a method and system for recommending one or more products and recommending one or more sales channel to a user. The method and apparatus disclosed provides a mechanism for generating a personalized and customized catalog of relevant products to be presented to the user along with a plurality of sales channels showing availability of the recommended products. Various embodiments of the present invention teaches a mechanism for generating the product recommendation based on at least the user shopping context information, user preference information, and the body profile information. The one or more products are correlated with the user information and the most relevant products are recommended to the user. The invention also teaches a method for virtually trying a product recommended by the recommendation engine of the present invention before purchasing through the one or more recommended sales channels.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application claims the benefit of U.S. Provisional Patent Application Ser. No. 61/732, 398 filed Dec. 2, 2012, entitled “RECOMMENDATION SYSTEM TO PROVIDE RIGHT PRODUCT AND SALES CHANNEL FOR THEIR SHOPPING CONTEXT,” the entirety of which is herein incorporated by reference. 
     
    
     FIELD OF INVENTION 
       [0002]    The present invention relates to data acquisition, information search and recommendation systems and in particular, this invention relates to method and system for recommending relevant products to an individual user. 
       BACKGROUND 
       [0003]    It is often necessary to make choices without sufficient personal experience of alternatives. In everyday life, people rely on recommendations from other people either by word of mouth, reviews printed on newspapers, online recommendation based on the rating and reviews from experts, users and the like. 
         [0004]    Online recommendation of a product has gained popularity due to the growth of internet, advancement in technologies, and the need for retailers to reach consumers directly. Individuals browsing through a given collection can have a difficult time for efficiently locating products that are ideal choices for their shopping context. Such difficulty increases with the increase in the collection of products. Accordingly, an online or an in-store digital recommendation system is desirable to assist the individual in locating product of interest. Existing online recommendation systems predict the products of interest for a user by collecting preferences from many users with similar interest. Though the prediction is specific to the individual user, the information is retrieved based on aggregated inputs from many users present in the database of the online recommendation system. 
         [0005]    The online recommendation system is used in variety of applications such as electronic products, books, accessories, apparels, footwear and the like. However, it is difficult to recommend lifestyle product that are wearable such as apparels and footwear using only user preferences and fitting records of the user. As every model and brand has it&#39;s own interpretation of size, it is hard to recommend a product size without physical trial of the product and without having to take measurements of foot or body every time. 
         [0006]    The conventional online recommendation systems for wearable items recommend products based on traditional sizing scheme. For example, if an individual enters his size as 8.5 in men&#39;s category, the online recommendation system displays all the items listed in the database with the size 8.5 in men&#39;s category. One problem with the conventional sizing scheme is that it is a single dimensional sizing system to represent a 3-dimensional body parts such as a foot and therefore does not take into account several other parameters key to fitting a product on the user. Further, the product manufacturers use this single numeral sizing system to design footwear or apparels that has more than 10-20 measurements. These measurements are designer&#39;s interpretation based on the product&#39;s usage, target segment, demographic data, fashion etc and therefore differ for every manufacturer, from brand to brand and model to model with in the same brand. Additionally, in the conventional methods, the individual user is not provided with any provisions to know the way the recommended item looks on her or how the item fits on her 
         [0007]    In light of the above discussion, there is a need for a system and method that overcomes the above disadvantages. 
     
    
     
       BRIEF DESCRIPTION OF FIGURES 
         [0008]      FIG. 1  illustrates environment in which the product and sales channel recommendation is made, in accordance with various embodiments of the present invention; 
           [0009]      FIG. 2  illustrates a block diagram of a recommendation system, in accordance with various embodiments of the present invention; 
           [0010]      FIG. 3  illustrates components of recommendation engine, in accordance with various embodiments of the present invention; 
           [0011]      FIG. 4  illustrates a flowchart for recommending products and sales channel to a user, in accordance with various embodiments of the present invention; and 
           [0012]      FIG. 5  illustrates a block diagram of a computer server system, in accordance with various embodiments of the present invention. 
       
    
    
     BRIEF DESCRIPTION OF THE INVENTION 
       [0013]    The above-mentioned shortcomings, disadvantages and problems are addressed herein which will be understood by reading and understanding the following specification. 
         [0014]    The present invention provides a method and system for recommending one or more products and recommending one or more sales channels to the user. The method comprises steps of making the user verbalize the shopping context by providing one or more personal preferences and shopping needs, creating a customized catalog of products correlating with the shopping context and user preferences identified in the previous step. Further, the method comprises the steps of providing an interface to the user for comparing a plurality of suggested products, providing an interface to virtually try the product and suggesting one or more sales channel that carry the inventory in real time for the selected products. The user preferences include but are not limited to the digitized body image of the user, preference for or against a make, design, material &amp; construction, preference on usage, preference on features, and geo-location of the user. The method and system further includes presenting a trial room for users to try one or more recommended products. 
         [0015]    The system and apparatus for recommending relevant products to the user comprises a recommendation engine communicatively connected to a user database. The recommendation engine is configured to collect user preferences, identify and record the user shopping context. The recommendation engine is further in communication with a product database and a retailer database. The recommendation engine of the present invention is also configured to receive inputs from a social media inputs module and feedback collection module. In addition to the products, the system and method provides the recommendation of retail channel/store to complete the purchase. 
         [0016]    Systems and methods of varying scope are described herein. In addition to the aspects and advantages described in this summary, further aspects and advantages will become apparent by reference to the drawings and with reference to the detailed description that follows. 
       DETAILED DESCRIPTION OF THE INVENTION 
       [0017]    In the following detailed description, reference is made to the accompanying drawings that form a part hereof, and in which is shown by way of illustration specific embodiments, which may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the embodiments, and it is to be understood that other embodiments may be utilized and that logical, mechanical, electrical and other changes may be made without departing from the scope of the embodiments. The following detailed description is, therefore, not to be taken in a limiting sense. 
         [0018]      FIG. 1  illustrates environment  100  in which the product and sales channel recommendation is made, in accordance with various embodiments of the present invention. The environment  100  includes a user  105  who wants to get her body profile digitized for using recommendation service. System  100  also includes a kiosk  110  from where the user  110  can get her body profile digitized. The user  105  gets her body profile digitized in the nearest available kiosk  110 . A kiosk  110  is a small open-fronted but or cubicle from where the user can get her body profile digitized. In an embodiment, the user  105  gets her feet profile digitized in the kiosk. In another embodiment, the user  105  gets her hand profile digitized in the kiosk  110 . In yet another embodiment, the user  105  gets any other body party or the whole body profile digitized in the kiosk  110 . The kiosk  110  generates a unique identity for each digitized profile. In an embodiment, the unique identity generated by the kiosk  110  is a set of numerical characters. In another embodiment, the unique identity generated by the kiosk  110  is a set of alphabets. In yet another embodiment, the unique identity generated by the kiosk  110  is a combination of alphabets and numerical characters. The kiosk  110  generates unique identity based on various algorithms. In an embodiment, the kiosk  110  generates unique identity randomly under given set of conditions. In another embodiment, the kiosk  110  generates unique identity based on different encryption algorithms. 
         [0019]    In one embodiment, the system  100  further includes a profiler application  111  installed on a computing device  120  associated with the user  110 . The profiler application  111  is configured to create digital profile of the user  110 . The computing device  120  is any internet connectable device such as laptop, mobile phone or tablet computer. The computing device  120  may further have any special hardware device attached for the purpose of this invention. In another embodiment, the profiler application  111  is configured to run as a plugin on the computing device  120 . In yet another embodiment, the profiler application is configured to run as a web application on the computing device  120 . 
         [0020]    In one embodiment, the user  105  uses the profiler application  111  to click photographs in a specified manner of any or all body parts and which will be used to create a digital profile. The profiler application  111  generates a unique identity for each digitized profile. In an embodiment, the unique identity generated by the profiler application  111  is a set of numerical characters. In another embodiment, the unique identity generated by the profiler application  111  is a set of alphabets. In yet another embodiment, the unique identity generated by the profiler application  111  is a combination of alphabets and numerical characters. The profiler application  111  generates unique identity based on various algorithms. In an embodiment, the profiler application  111  generates unique identity randomly under given set of conditions. In another embodiment, the profiler application  111  generates unique identity based on different encryption algorithms. 
         [0021]    In yet another embodiment, the kiosk  110  or the profiler application  111  are configured to receive the digital profile of the user by providing a product proxy ( 112 ) as shown in  FIG. 1 . The kiosk  110  and/or, the profiler application  111  receive the proxy product( 112 ) information such as for e.g. details of a footwear that the user is comfortable in, size, color of a pair of jeans and the like. Based on the information received on the proxy product( 112 ), a unique identity number is assigned to the user and stored in the storage means. 
         [0022]    In yet another embodiment of the present invention, the kiosk  110  and the profiler application  111  are further configured to create digital profile of the user based on the physical dimensions provided by user manually such as by using a measuring tape etc. The kiosk  110  and the profiler application  111  are configured to convert the measures input by users into standard units and create a unique code. The user digital profile along with the unique code is further stored in the storage means. 
         [0023]    Further, the system  100  includes a user  115  who is using the recommendation service through a computing device  120 . In the context of present invention the user  105  and the user  110  are the same. In an embodiment, the computing device  120  is a desktop computer. In another embodiment, the computing device  120  is a laptop computer. In yet another embodiment, the computing device  120  is a tablet computer. In yet another embodiment, the computing device  120  is a smart-phone. In yet another embodiment, the computing device  120  is a phone capable of connecting to internet. 
         [0024]    Further, the system  100  shows the user  115  entering her unique identity along with user preferences &amp; shopping context in the computing device  120 . The user  115  can enter detailed information based on personal preferences, current shopping and product attributes. In an embodiment, the user  115  enters her unique identity and user preferences through a web application installed in the computing device  120 . In another embodiment, the user  115  enters her unique identity and user preferences through a plug-in installed in the computing device  120 . In yet another embodiment, the user  115  enters her unique identity and user preferences by directly logging on to the recommendation service website. 
         [0025]    Furthermore, the system  100  shows the user  115  getting recommendation of the product. The user  115  selects a category from which one or more products can be recommended. For example, the user  115  may select the category of footwear from the available list of category along with her unique identity. Furthermore, the recommendation service shows the entire possible sales channel through which the user  115  can buy the recommended product. The recommendation service recommends the product that are available both online and offline. In the system  100 , it can be seen that the online store  125  and an offline store  130  is recommended as sales channel for one or more recommended product. In an embodiment, there can be only a single online store  125  and a single offline store  130 . In another embodiment, there can be multiple online stores  125  and multiple offline stores  130 . 
         [0026]      FIG. 2  illustrates a block diagram of a recommendation system  200 , in accordance with various embodiments of the present invention. In an embodiment, the recommendation system  200  is computer server capable of recommending one or more products and one or more sales channel to the user  115 . In another embodiment, the recommendation system  200  is plurality of computer servers capable of recommending one or more products and one or more sales channel to the user  115 . In an embodiment, the recommendation system  200  is capable of handling large amount of traffic. In another embodiment, the recommendation system  200  is having a good response time. In yet another embodiment, the recommendation system  200  is able to scale large amount of data using various methods. Examples of data scaling methods are logging and measuring, horizontal scaling, load balancing, caching and the like. 
         [0027]    Further, the recommendation system  200  includes a user database  210 . The user database  210  is a collection of user data. The user database  210  contains the unique identity of each user and the preference information of the user as entered by the user. The user database  210  stores the user preference information and the unique identity of the user in a specific schema. The user database  201  furthers stores the shopping behavior history of the user  115 . The user  115  can modify the user preference information to get a different recommendation. In an embodiment, the user preference information includes preference towards a particular brand, the context of usage, a preference for fit characteristics such as snug, good or loose fit. In another embodiment, the user preference information includes preference for product attributes such as high heels, laces or ankle height. In yet another embodiment, the user preference information includes product material such as leather or cloth and its related snugness of fit. In yet another embodiment, the user preference information includes preference towards trading off longevity of the product over price of the product. In yet another embodiment, the user can store existing products in the digital closet to provide information on preferences on style, design, construction and make. 
         [0028]    The system  200  is configured to provide user  115  with provisions to change the preferences for every shopping experience. In yet another embodiment, the user preference could change as she may be buying the product for some one else. In yet another embodiment, the user preference information includes the location from which the user is willing to buy the product. In yet another embodiment, the user preference information includes combination of one or more features listed above. For example, the user preference information includes the user can wear a low or medium heel, needs arch support for her sports shoes and ASICS and BROOKS are her preferred running shoe brands . The user preference information is stored in the user database  210 . 
         [0029]    Furthermore, the recommendation system  200  includes a retailer database  220 . The retailer database  220  is a collection of data of retailers. The retailer database  220  contains a list of products available with the plurality of retailers. The retailer database is configured to receive inputs for operations such as modifying the list of products in the retailer database  220 , deleting or adding one or more products in the list. In an embodiment, the retailer database  220  is updated when a new retailer is added to the list. In another embodiment, the retailer database  220  is updated when an existing retailer withdraws his service from the recommendation system. In yet another embodiment, the retailer database is configured to update automatically at regular intervals. The retailer database  220  is further configured to update when multiple channels such as online or physical store options are created for the same retailer. 
         [0030]    Furthermore, the recommendation system  200  includes a product database  215  which contains a master data base of all the products available in the industry. All products sourced through different manufacturers, brands and retailers are stored in the product database  215 . Each product listed in the product database contains detailed information regarding the attributes, physical dimensions, photographs and usage model of the product. The retailer database  220  and product database  215  are configured to communicate with each other. Product database  215  is usually the super set of the data in the retailer database. 
         [0031]    Furthermore, the recommendation system  200  includes a recommendation engine  230 . The recommendation engine  230  recommends one or more products to the user  115  for every shopping event based on the information received from the user database  210 , the shopping context information received from the user computing device  120  for the current shopping event and the product list obtained from the product database  215 . The recommendation engine  230  is configured to correlate the products in the product database  215  with the user information based on the at least context information, preference information, user location information, expert opinion information and the dimension information associated with the user. 
         [0032]    Based on the correlation of the available products with the shopping context mentioned by the user and her preferences, the recommendation engine  230  is configured to generate a personalized &amp; customized catalog of most relevant products suggested to the user for one event of shopping. The shopping context as described herein refers to identifying the purpose of shopping by the user based on one or more parameters. The shopping context information is collected for every event of shopping by the kiosk  110  or the profiler application)  11  installed on the user computing device  120  and communicated to the recommendation engine  200  along with user preferences. 
         [0033]    Furthermore, the recommendation system  200  includes a virtual trial room  240 . The user  115  can try one or more recommended products in the virtual trial room  240 . The virtual trial room  240  is a computer generated two or three-dimensional representation of the user  115  with an appearance with the recommended product and/or contains qualitative and quantitative fitting information of the product in a visual form. The virtual trial room  240  has a database where it captures the defining features of the person, defining features of the recommended product and a means to integrate and display the picture representing the user  115  wearing the recommended product for cosmetic, health and fitting feedback. 
         [0034]    Further, the virtual trail room  240  is configured to generate at least a two-dimensional or a three-dimensional image of the user based on the digital profile stored in the user database  210 . The virtual trail room  240  is further configured to collect opinion from live social media inputs module  270  to modify the final recommendation from recommendation engine  230 . In another embodiment the virtual trial room  240  graphically overlay the two products to show case comparison. In another embodiment the virtual trial room is enabled by creating an “avatar” of the person&#39;s body or body features as well as creating a virtual digital replica of the product that is kept in the shopping catalog. The virtual trial room  240  then matches the product to the users avatar by personal body and product dimensions, wherein the ‘avatar’ is stored as the digital profile in the user database  210 . 
         [0035]    Furthermore, the recommendation system  200  includes a retailer listing database  250 , where one or more sales channel is recommended for the recommended one or more products. The sales channel can be an online store or an offline store. For example, for the recommended product, the recommendation system shows one or more online store and one or more offline store. The user  115  can select the convenient recommended product and the recommended sales channel. 
         [0036]    Furthermore, the recommendation system  200  includes a surveillance database  260  (not shown in the Figure). The surveillance database  260  monitors the recommendation system  200 . The surveillance database  260  monitors the shopping behavior of the user  115 , preferences, as well as factors of shopping such as conversion rate, efficiency of the recommendation system, highest selling retailers and the like. 
         [0037]    Furthermore, the recommendation system  200  includes a feedback collection module  280  that is configured to collect errors and changes reported from the virtual trial room  240  to be reported back to the recommendation engine  230  for a new iteration of the recommendation. 
         [0038]      FIG. 3  illustrates components of recommendation engine, in accordance with various embodiments of the present invention. The recommendation engine  360  is the enlarged view of the recommendation engine  230 . The recommendation engine  360  includes a data-mining module  310 . The data-mining module  310  mines shopping behavior, product information and consumer information from the user, shopping history, and products databases. In an embodiment, the data-mining module  310  mines the data from the previous buying history of the user to find a pattern of buying. In another embodiment, the data-mining module  130  mines the data from social networking sites, blogs, news sites to collect the trend and popularity of the product. 
         [0039]    Furthermore, the recommendation engine  360  includes an expert opinion module  315 , the expert opinion module  315  generates expert opinions. The expert opinions are generated for increasing the quality of the recommendation. The expert opinion module  315  contains opinions from experts in the particular field. For example, podiatrists, shoe design/technologists for footwear retail vertical. The expert opinion module  315  also contains objective calculation and prediction of the product&#39;s usefulness for particular set of user needs 
         [0040]    Furthermore, the recommendation engine  360  includes a user preference module  320 . The user preference module  320  contains user preference information. The user preference information can be preference for a particular brand, user preference against a particular brand and the like. In an embodiment, the user preference module  320  and the user database  210  are the same. In another embodiment, the user preference module  320  and the user database  210  are different. 
         [0041]    Furthermore, the recommendation engine  360  includes a product search engine  325 . The product search engine  325  searches for the most relevant product based on the given user shopping context and preferences information. In an embodiment, the product search engine  325  rates, ranks and displays the top products based on the user shopping context and preferences information. In another embodiment, the product search engine  325  maps one or more products to user needs by tagging each product to different needs. In yet another embodiment, the product search engine  325  quantitatively positions the product on different consumer purchase decision variables. In another embodiment, the product search engine  325  maps the products based on usage model provided by the expert module  315   
         [0042]    Furthermore, the recommendation engine  360  includes a consumption-tracking module  330 . The consumption-tracking module  330  tracks the consumption of the recommended products using a data recorder. The data recorder collects information on every product purchase made by the user based on the recommendations made by recommendation engine  360 . The said information is collected for every attempt of shopping and is communicated to the retailers in the retailer database  220 . The consumption-tracking module  330  uses the information provided by data recorder to manage the product inventory. Consumption behavior captured by consumption-tracking module  330  is also communicated to a targeted promotion module  350  as described in the later sections. 
         [0043]    Furthermore, the recommendation engine  360  includes a stock keeping unit (SKU) availability-checking module  335 . The SKU availability checking module  335  checks if the recommended one or more products are available with one or more vendors. The sales channel is recommended to the user  115  after checking with SKU availability checking module  335 . The features of SKU availability-checking module  335  use point-of-sale and enterprise-resources-planning software plug-ins to transfer the inventory knowledge at the retailer to the recommendation engine platform. In an embodiment, the availability check is done in real time. In yet another embodiment, the availability check is done on a scheduled time regularly. 
         [0044]    Furthermore, the recommendation engine  360  includes a demand projection module  340 . The demand projection module  360  projects the existing trend and the demand for the recommended one or more products. The demand-projecting module  360  also projects the demand and the trend of the recommended sales channel. In one embodiment, this information could be used by the product brands or the retailers for inventory/manufacturing planning In another embodiment, this information could be used by the user for consumption. 
         [0045]    Furthermore, the recommendation engine  360  includes a 3D business analytics module  345 . The 3D business analytics module  345  analyzes the product consumption and shopping pattern in time-domain. The 3D business analytics module  345  allows the shopping pattern to be tracked in the three-dimensional space where the user identity and preference can be tracked on a time-domain. 
         [0046]    Furthermore, the recommendation engine  360  includes a targeted promotion module  350 . The targeted promotion module  350  helps the recommendation engine  360  to target small sets of users with most relevant products using the information provided by 3D analytics data  345  and consumption tracking module  330 . For example, the targeted promotion module  350  can target the shoe with a trendy pattern to a youth when his/her shoes are due for a change 
         [0047]    Additionally, the components in the recommendation engine  360  are coupled to each other for generating effective and efficient product and sales channel recommendation. For example, the recommendation unit  360  recommends one or more sports shoe based on the user need by using ,product search engine, data mining, expert opinion module, and SKU availability engine to help the shopper buy the right sport shoes at right retailer. 
         [0048]    Furthermore all these modules( 310 - 360 ) are configured to be accessed through a unique user interface  355 . In one embodiment a unique implementation of a tag cloud is provided to capture user inputs by the user interface  355 . In another embodiment a special voice assist is provided to collect data in the expert opinion module  315 . In yet another embodiment a map based interface is provided to the user  115  to view information provided by the SKU availability checking module  335 . 
         [0049]      FIG. 4  illustrates a flowchart for recommending products and sales channel to a user, in accordance with various embodiments of the present invention. The flowchart  400  initiates at step  410 . At step  420 , the recommendation system  200  asks user to verbalize shopping context in terms of needs, product attributes, preferences, and usage model for the current shopping event. The combination of inputs is captured as information about the shopping context of the user for the current shopping event. 
         [0050]    Further, at step  430 , the recommendation system  200  receives the shopping context from step  420  and searches for the right product(s) for the given shopping context and user needs. The matching products are rated &amp; ranked. The top products are listed as a custom catalog for the user. 
         [0051]    Furthermore, at step  440 , the recommendation system  200  receives the custom catalog for the user&#39;s shopping trip and presents to the user for product selection. Furthermore, at step  450 , the recommendation system  450  allows the user to trial and tests one or more recommended item virtually using various techniques as described previously. Furthermore, at step  460 , the recommendation system  460  recommends user one or more sales channel for one or more recommended products and record the sales channel and product that was bought by the customer. At step  460 , the flowchart terminates. 
         [0052]      FIG. 5  illustrates a block diagram of a computer node  500  of the recommendation engine  230 , in accordance with various embodiments of the present invention. The computer node  500  of the recommendation engine  230  includes a computer server  505  that is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with computer server  505  include, but are not limited to, personal computer systems, server computer systems, cloud computing services, thin clients, thick clients, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and the like. 
         [0053]    In  FIG. 5 , the computer server  505  in the computer node  500  of the recommendation engine  230  is shown in the form of a general-purpose computing device. The components of computer server  505  include, but are not limited to, processing unit  530 , a system memory  555 , a network adapter  520 , an input-output (I/O) interface  540  and one or more buses that couples various system components to processing unit  530 . 
         [0054]    The one or more buses represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnects (PCI) bus. 
         [0055]    Computer server  505  typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer server  505 , and includes both volatile and non-volatile media, removable and non-removable media. In an embodiment, the system memory  555  includes computer system readable media in the form of volatile memory, such as random access memory (RAM)  560  and cache memory  570 . Computer server  505  may further include other removable/non-removable, non-volatile computer system storage media. In an embodiment, the system memory  555  includes a storage system  580 . 
         [0056]    Computer server  505  can communicate with one or more external devices  550  and a display  510 , via input-output (I/O) interfaces  540 . In addition, computer server  505  can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (for example, the Internet) via the network adapter  520 . 
         [0057]    It can be understood by one skilled in the art that although not shown, other hardware and/or software components can be used in conjunction with the computer server  505 . Examples, include, but are not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data archival storage systems, and the like. 
         [0058]    The advantage of using the recommendation system  200  is that the user  115  will have a quality shopping experience. Quality of the experience is defined as expectations and needs that are catered during the shopping process. When the recommendation system is used the user will get all the information he or she wants before the product purchase to make the right purchase decision. Satisfaction of buying the right product from right place increases the quality of shopping experience. 
         [0059]    Another advantage of using recommendation system  200  is that the efficiency of the shopping is enriched. Shopping is an activity that needs resources to be spent to procure and consume a good/service. The resources used by shoppers can be classified mainly into time, energy and money. The other advantage of using recommendation system  200  is that the shopping context is enriched. For example, the recommendation system  200  recommends right formal shoe model to wear for occasion as well as to use with a pair of jeans in the shortest turnaround time while the same shoe model search may take five physical trials in three different store in two commercial districts taking the user more than three to four hours along with commute expense and energy to try fifteen different pair of shoes. 
         [0060]    This written description uses examples to describe the subject matter herein, including the best mode, and also to enable any person skilled in the art to make and use the subject matter. The patentable scope of the subject matter is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal language of the claims.