With the convergence of businesses and the use of the internet as a marketing tool, if not the market place itself, more and more people are going online to do things that they would usually do outside the home. Recommendation engines are used by websites in order to better facilitate product browsing and purchase online.
These engines computes the rating that a person gives a particular product and from that rating rates other articles that could be or are actually related to their interest. It predicts other things that the person might be interested in and offers this to the person whenever he or she goes online. They produce the desired search item of the user while at the same time suggesting related items that might be of interest to the user. With the expansion of online e-commerce and even regular businesses that are hooked up online, recommendation engines have become a means to reach out to the people and share what the company or organization has to offer.
What We Need
Product recommendation engines usually require user registration and permission for data gathering as well as the means through which these data can be stored and computed in order to provide the desired search result of the user each time he or she goes online. The main reasons for the user registration and permission is to protect the website operator from legal technicalities and also to make the user profile unique and exclusive.
According to Thomas Hess, there are four techniques in using recommendation engines. And by first choosing which approach can work best for you, you can optimize the use of these engines for your benefit. These are the non personalized recommendation, demographic recommendation, content based recommendation and the collaborative filtering. Each takes a different approach in making a recommendation engine work.
Non Personalized Recommendation
The use of non personalized recommendation allows the engine to provide search items based on a given set of information that can be used for all users. These are either manually selected by a person in charge or may be automated based on the popularity of an item. This type of recommendation is usually used by companies who have no need for a personalized approach in terms of marketing strategy. In the case demographic recommendation, it uses the ratings of a particular group of persons with the same demographics in order to determine what might pique the interest of new users coming from the same demographic. This particular technique is dependent on the computation of the ratings of the entire demographic and is thus quite tedious and raises the question of how accurate search items are in matching a person personal preference and the amount of flexibility that this can offer to a person.
Content Based on Recommendation
On the other hand, Content based recommendation uses items that have been rated or chosen by the user and from this recommends the items with the highest rating of similarity to the item that the person is searching for. This works best for items that can be categorized or divided into particular groups in order to make the search easier and more efficient. It only takes into consideration the ratings of that particular individual and thus makes it less flexible in terms of offering items that may slightly differ from the searched items but could be of better quality or value.
Lastly, the collaborative filtering technique is the most sophisticated technique that can be used by recommendation engines as it can filter items based on user, item and model ratings; i.e. by correlating information used between users and by the users themselves. In this case, it takes a more community based but still personalized approach in their recommendation.
How to Successfully Set it up
The first step to successfully set up a recommendation setting is to get acquainted with the way that it can produce the offerings of the website and from there, decide how these offerings will be shown on the website. People who are yet to get acquainted with the use of recommendation engines have to familiarize themselves with the different techniques that can be used in order to provide a better search output and to minimize the work required but with maximum results. Recommender system works best when the data input can express the desired output of both the user and the operator.
If you need more information about increasing sales on your website, Maxymiser.com has great information about Product Recommendation Engines. By taking into consideration how these engines should work for the website, it can assure optimized results that is continuous and steadily growing.
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