A recommender system is a program developed using AI and ML algorithms that is used to provide personalized recommendations to users. These recommendations can be used in E-commerce to recommend products based on user behavior so that the E-commerce store can maximize its profits. The recommender system is trained using the user’s and products data so that it can identify the pattern and provide personalized recommendations.
Why recommender systems:
- To provide a personalized user experience
- To have better selling techniques which help in better profits
- To increase click-through and conversion rates
Types of recommender systems:
- Content-Based Filtering Recommender
- Collaborative Filtering Recommender
- Hybrid recommender system
Content-Based Filtering Recommender:
This type of recommender system is used to provide recommendations only based on the items and characteristics associated with them. It occurs when a product is suggested based on the search history or historical data. The main objective is to classify products based on keywords, check what the customer likes, search those terms in the database, and then provide the recommended similar things. This type of technique is dependent on the user’s input.
For example, when a user searches for electronics products in an E-commerce store, it displays all the items consisting of those tags and keywords.
Collaborative Filtering Recommender:
This type of recommender system uses a collaborative approach that focuses on identifying the relationship between all users and the items. A similar pattern between one item and another is defined based on the user’s opinion. The key logic is: if two users who have liked the same item at some point will potentially like another similar item in the future. The program collects data, such as similarity matrix and previous purchases, and combines these results with other users who have similar interests. Thus, they assume which products are most likely to be relevant to them.
For example: if a user searches a keyword in an E-commerce store, it will identify the similar users belongs to the group and based on the
Interests it will recommend products.
Hybrid recommender system:
A Hybrid recommendation system is a mixture of both content-based and collaborative recommender systems. It works effectively in many cases because it has the positive functions of both filters.
The cold start problem for recommender systems:
When there is not sufficient data to train recommender systems it is called the cold start problem. In eCommerce, there are two types of cold starts: product cold start and user cold start.
Example: when a new user or a new product is introduced to your E-commerce store.
Approaches to overcome the cold start problem:
To overcome the cold start problem little personalized recommendations are used. We can use demographic filtering, used demographic attributes, such as age, gender, and location. Another solution is Content-based filtering is the method that answers this question. It uses the metadata of new products when creating recommendations.
Conclusion:
In the Present day, recommender systems are must-have solutions to all online businesses. One can provide a rich personalized user experience and help to generate good profits.
