A recommendation engine or a recommender system is a tool used by developers to foresee the users' choices in a huge list of suggested items.
A vivid example of recommendation engine usage is by Amazon; with its "Customer who bought this item also bought ...". Generally, the content recommendation engine is like a clever and experienced salesperson who grasps the needs, tastes, and requirements of the user and is able to make knowledgeable decisions about recommendations beneficial and relevant to the client's wants, meanwhile increasing the conversion rate.
The statistics show that 35% of Amazon's revenue comes from the usage of recommendation engines.
1)Content Based Recommendation System
2)Collaborative Filtering Based Recommendation System
3)Hybrid Recommendation System
Recommendation systems have several different uses. The most common use for a recommendation system is ranking products by how much a user would like them.
Shopping has been, is and will remain a necessity for humanity. It's not a long time since we asked our friends to recommend this or that product. Hence, it's the essence of human beings to buy items recommended by our friends, whom we trust more. The digital age has taken into consideration this ancient habit. Therefore, any online shop you visit today, you may see some recommendation engine used.
With the usage of algorithms and data, recommendation engines filter and recommend the most relevant products to a specific user. As they say, it's like an automated shop assistant. When asking for something, it also suggests another one that you may be interested in. Developing product recommendation algorithm models is a research area that grows hour by hour.
Data gathering is the first phase of creating a recommendation engine. In reality, data is classified into explicit and implicit ones. Data provided by users, like ratings and comments are explicit. Whereas, implicit data may consist of a search log, order and return history, clicks, page views, and cart events. This kind of data is collected from any users who visit the given website.
Collecting behavioral data is not difficult, since you can keep user activities logged on your website. As each user likes or dislikes various items, their datasets are different. During some time, when the recommender engine is feed with more data, it becomes more clever And the recommendations become more relevant too, so the visitors are more inclined to click and buy.
To have better recommendations, you should create more data for the algorithms you use. It means that you can turn any recommender project into a great data project quickly. You can decide what type of storage you need to use with the help of the data you use for creating recommendations. It is up to you whether to use a NoSQL database or a standard SQL database or even some sort of object storage. All of these variants are practical and conditioned with whether you capture user behavior or input. A scalable and managed database decreases the number of required tasks to minimal and focuses on the recommendation itself.
In order to find items with similar user engagement data, it is necessary to filter it with the use of various analyzing methods. Sometimes it is necessary to provide recommendations immediately when the user is viewing the item, so the type of analysis is required. Some of the ways to analyze this kind of data are as follows:
· Real-time system In case you need to provide fast and split-second recommendations you should use the real-time system. It is able to process data as soon as it is created. The real-time system generally includes tools being able to process and analyze event streams.
· Near-real-time analysis The best analyzing method of recommendations during the same browsing session is the near-real-time system. It is capable of gathering quick data and refreshing the analytics for few minutes or seconds.
· Batch analysis This method is more convenient for sending an e-mail at a later date since it processes the date periodically. This kind of approach suggests that you need to create a considerable amount of data to make the proper analysis like daily sales volume.
The next phase is filtering the data to provide relevant recommendations to the users. For implementing this method, you should choose an algorithm suitable for the engine you use. There are a few types of filtering, such as:
· Content-based The focus of content-based filtering is a specific shopper. The algorithms follow actions like visited pages, spent time in various categories, items clicked on and etc. And the software is developed based on the description of the products the user likes. Afterwards, the recommendations are created based on the comparison of user profiles and product catalogs.
· Cluster Cluster analysis is intended for smaller groups of cases. It tries to group more similar to one another in contrast to other types of cases. In this respect, recommended items fit each other regardless of what other users have watched or liked.
· Collaborative It makes predictions conditioned with the tastes and preferences of the customer and allows you to make product attributes. The essence of collaborative filtering is the following; two users who have liked the same item before will like the one in the future.
