The personal recommender
we want a guide, a local expert when we visit a new place. if we are doing something routine, we don't need any help. if we are in a new place or we are experiencing something new, we appreciate information. we do this today by flipping brochures, maps and ads. we use a scratch pad and a pen to put together a list. we know our tastes and we know how to find a match in the new environment. Booking a travel itinerary involves a recommender from the travel website. we get choices for our flight searches and hints to add a hotel and a car. Some such websites involve intelligent listings based on past purchases, reward program memberships and even recommendations based on a collaborative filtering technique from other travelers. We appreciate these valuable tips on the otherwise mundane listing of flights and hotels ordered by price.
Therefore information is valuable when we explore. it is similar to wearing google eyeglasses or anything that hones our senses. Maps do this in the form of layers overlaid over the geographic plot but the data is merely spatial and improved with annotations. It is not temporal as in what events are happening at a venue next to a hotel. It also does not give a time based activity recordings that we can rewind and forward to see peak and low traffic. such data would be gigantic to store statically with maps. besides they may not be relevant all the time as business change.
A recommender on the other hand can query many data sources over the web. for example, it can query credit card statements to find patterns in spending and use it to highlight related attractions in a new territory. wouldn't it be helpful to have our login pull up restaurants that match our eating habits instead of cluttering up the map with all those in the area ? similarly, wouldn't it be helpful to use our social graph to annotate venues that our friends visited ? The recommender may be uniquely positioned to tie in a breadth of resources from public and private data sources. many web applications such as deli.cio.us and kayak make data available for browsing and searching.
This recommender is empowered to add a personal touch to any map or dataset by correlating events and activities to the user's profile and history. By integrating signing in credentials across banks, emails, bills, and other agencies, the recommender gets to know more about our habits and becomes more precise in the recommendations. Moreover, the recommender can keep learning more and more about us and it accrues habits and recommendations and improves it with feedback loop.
The recommender is also enabled to query trends and plot charts to analyze data in both spatial and temporal dimensions. it could list not only the top 5 in any category but also the items that remained in the top 5 week after week. These and such other aggregation and analysis techniques indicate tremendous opportunity for the recommender to draw the most appealing and intuitive information layer over any map.
The recommender is also not limited to standard querying operators and can employ a variety of statistical models and machine learning algorithms to better judge on our behalf. By using different ways to determine correlations, a recommender becomes closer to truth.
we want a guide, a local expert when we visit a new place. if we are doing something routine, we don't need any help. if we are in a new place or we are experiencing something new, we appreciate information. we do this today by flipping brochures, maps and ads. we use a scratch pad and a pen to put together a list. we know our tastes and we know how to find a match in the new environment. Booking a travel itinerary involves a recommender from the travel website. we get choices for our flight searches and hints to add a hotel and a car. Some such websites involve intelligent listings based on past purchases, reward program memberships and even recommendations based on a collaborative filtering technique from other travelers. We appreciate these valuable tips on the otherwise mundane listing of flights and hotels ordered by price.
Therefore information is valuable when we explore. it is similar to wearing google eyeglasses or anything that hones our senses. Maps do this in the form of layers overlaid over the geographic plot but the data is merely spatial and improved with annotations. It is not temporal as in what events are happening at a venue next to a hotel. It also does not give a time based activity recordings that we can rewind and forward to see peak and low traffic. such data would be gigantic to store statically with maps. besides they may not be relevant all the time as business change.
A recommender on the other hand can query many data sources over the web. for example, it can query credit card statements to find patterns in spending and use it to highlight related attractions in a new territory. wouldn't it be helpful to have our login pull up restaurants that match our eating habits instead of cluttering up the map with all those in the area ? similarly, wouldn't it be helpful to use our social graph to annotate venues that our friends visited ? The recommender may be uniquely positioned to tie in a breadth of resources from public and private data sources. many web applications such as deli.cio.us and kayak make data available for browsing and searching.
This recommender is empowered to add a personal touch to any map or dataset by correlating events and activities to the user's profile and history. By integrating signing in credentials across banks, emails, bills, and other agencies, the recommender gets to know more about our habits and becomes more precise in the recommendations. Moreover, the recommender can keep learning more and more about us and it accrues habits and recommendations and improves it with feedback loop.
The recommender is also enabled to query trends and plot charts to analyze data in both spatial and temporal dimensions. it could list not only the top 5 in any category but also the items that remained in the top 5 week after week. These and such other aggregation and analysis techniques indicate tremendous opportunity for the recommender to draw the most appealing and intuitive information layer over any map.
The recommender is also not limited to standard querying operators and can employ a variety of statistical models and machine learning algorithms to better judge on our behalf. By using different ways to determine correlations, a recommender becomes closer to truth.
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