I am somewhat like many people who are unfamiliar with data mining for this type of matching.
For example, I want to provide a "similar items" for Vacation Rentals, where the "dimensions" or attributes, could be "location", "bedrooms", "price", etc. It's hard to quantitfy anything to show something which might be more relevant to someone else based on the previous properties that they have currently been viewing.
Instead I have just taken to approach of creating a bounding box based on the Geo coordinates, and then offer up similar properties within their search price range. But I would really love to eventually implement something like your original article. (Suggestions welcome).
Amazon is taking an indirect approach. They are not necessarily comparing items directly to offer suggestions, although they probably take categories into consideration and that's because they have a good stream of traffic, ratings and purchases to rely on (having more, better data gives better results than smart algorithms).
Their suggestions are like: customers that viewed this item also viewed; customers that viewed this item ended up buying; customers that bought this product, also bought these other products.
That last metric in particular is interesting, because it tells you for a product what are the complementary products that customers may be interested in. So you don't actually have to measure somehow the physical properties of the objects getting sold to discover relationships.
In your case I don't have knowledge about the problem domain to give advice, but "customers that viewed this deal also viewed ..." is always a great addition. Also add ratings and follow-up on people with emails to rate on their vacation, after coming back from the trip. I don't know how well it will work - there's no general solution, you try something and if it doesn't work, try something else.
Thanks for the tip bad_user. I hadn't actually thought about trying to figure out "customers that viewed this item also viewed".
I could very easily create something that takes every visit, MapReduce it, and then track the entropy between potential matches to provide the "best" match based on user visits of that property also.
To take the example, it would be really great to also know that people from Germany aren't interested in the slightest in our Italian properties based on trends of their national behaviour.
I am somewhat like many people who are unfamiliar with data mining for this type of matching.
For example, I want to provide a "similar items" for Vacation Rentals, where the "dimensions" or attributes, could be "location", "bedrooms", "price", etc. It's hard to quantitfy anything to show something which might be more relevant to someone else based on the previous properties that they have currently been viewing.
Instead I have just taken to approach of creating a bounding box based on the Geo coordinates, and then offer up similar properties within their search price range. But I would really love to eventually implement something like your original article. (Suggestions welcome).