Product recommendation engines can be executed effectively through collaborative filtering, content-based filtering and by hybrid recommender systems.
Product recommendation engine has been utilized by prominent online stores, social networks, and entertainment service providers, music streaming sites, review sites and blogs for more exposure and to increase actual sales. If you are wondering what a product recommendation engine is and how it works, it is usually in a form of a sidebar or just a small section that looks like advertisements or announcements. It serves as a very effective sales tool because the information, featured items and recommended products are already sorted out and aimed at the current user or visitor. It provides a link or a connection to your previous activities and preferences thereby its product recommendations are mostly helpful and handy.
How Does It Work?
If you are a regular visitor of an online store like Amazon, you will notice a section in the same page that shows other items that are related to your previous purchase and other items that you might consider based on your browsing or surfing history. Amazon specifically collated your previous purchases, viewing preferences and earlier activities; and based on these data, the product recommendation engine can generate relevant and applicable suggestions for your future or succeeding needs. It provides a very good prospect of gaining your next purchase because it offers convenience to you as a targeted market.
For other sites such as music streaming sites and entertainment service providers, the use of this system gives them more media to be streamed because users are provided with more appropriate and related contents based on their interests. It also utilized by social networks as ads and it also lets users connect with their distant contacts or long lost acquaintances in their networks. Review sites, blogs and online databases also use product recommendation engines t provide wider and more comprehensive related information, data and analysis of different products and services.
How Do Product Recommendation Engines Come Up with the Most Relevant Data?
Product recommendation engines can be executed effectively through collaborative filtering, content-based filtering and by hybrid recommender systems. In the first system, collaborative filtering, all relevant information from large pool of users such as their preferences, behavior and activities are gathered. The data gathered are then segregated and fed to the assigned user profiles for each visitor. Upon visiting the site, the recommendations that will be viewed are all relevant to your interest and line of activities.
The content-based filtering system dwells on the user’s preceding activities, preferences and browsing history. The recommendations are based from the most recent purchase or the latest items that the user viewed or liked. And last but not the least, the content-based filtering platform which is in fact a combination of the first two. The three systems are in general very effective because the information is streamlined therefore more precise and fresh. Predictions and forecasts of the next activity of the user are generated based on the actual behavior and activities of the user in a certain site. The most relevant data and applicable recommendations are foreseen by carefully sorting out the information about the users and visitors.
Benefits of Product Recommendation Engines
It is always the ultimate goal of any website owners to give their visitors or users the most significant information about their products and services in the fastest and most convenient way. With the use of product recommendation engines, most of the related items or information that the users are looking for are displayed and recommended through the site where they are currently logged in. The handiness and convenience of the browsing activity significantly saves time and effort on the part of the user. For the website owners, more sales conversions are achieved because the recommendations made by the system are relevant and this usually prompts the users to make a purchase.