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A recommendation engine (or system) is an algorithm that analyzes the user behavior to suggest items which they are likely to prefer. A recommendation system uses data analysis techniques to figure out the items that match the users' taste & preferences. The ultimate aim of any recommendation engine is to stimulate demand and engage users. Recommendation engines can have many use cases like in entertainment, e-commerce, mobile apps, education, etc. In general, a recommendation engine can come in handy wherever there is a need to give personalized suggestions and advice to users.
Why Should I Care?
By using a recommendation engine you can provide personalized suggestions to buyers and improve catalog visibility. One of the reasons why in-store retail is still relevant is because in a physical store the salesman understands the buyer and provides personalized suggestions. If you like a particular style of t-shirt then the salesperson will showcase more of the similar kind. The reason why we trust our friends and family for advice is that they understand us. We don’t have to tell them every time what we need, they just know it. By using a recommendation engine you can earn a similar level of trust. Thus making long-term loyal customers which are essential for every business.
Read More: How to Optimize Pricing using Big Data?
3 Ways to Create a Recommendation Engine:
A recommendation engine can work in various ways. Depending upon your business model and needs you can create one in either of these 3 ways:
1. Collaborative Filtering:
In case of collaborative filtering, the recommendations are placed on the basis of buyer’s history. For example, in case of Facebook and LinkedIn, the person would get suggestions based on mutual friends and connections. If you like something then you will get recommendations based on the behavior of people with similar demographics.
While this is a very effective approach when applied to scale, there are some minor limitations to it. To implement a collaborative filtering recommendation engine you need a good amount of customer data (i.e. many users) so that you can identify trends and find people who show similar behavior.
2. Content-based Filtering:
In content-based filtering, certain attributes and keywords are attached to each item. Based on what people like or dislike the attributes are given weights. A profile is created for every user and his likes and dislikes for different attributes is recorded. The items are recommended if their attributes match the profile of the user.
This approach is less effective since it difficult to attach attributes to items and the recommendations turn out to be vague. Another issue with this approach is the inability to map products which are not similar to each other. For instance, the system would be effective for recommending books based on reading history but it won’t be efficient in recommending other content types like movies, games, and news on the basis of reading history.
3. Hybrid Model:
As the name suggests a hybrid model uses both the approaches to deliver more precise suggestions. A hybrid model can be created using various means like by adding collaborative capabilities to a content-based model (or vice-versa), by designing two separate models and then combining them or by designing a custom model. One company that uses this model is Netflix, which has earned many loyal with this approach.
By combining the two approaches you can enjoy the benefit of both without handling their disadvantages. Though all of this sounds easy, it requires good data science skills to create a custom model. You will need expert guidance in order to create impactful solutions. Overall spending on a good recommendation engine pays off so it is good to create an effective one.
Read More: How to predict buyer's purchases with the help of Big Data?
Why do many recommendations engines fail?
There are plenty of plugins and tools out there that claim to be effective recommendation engines. But a prebuilt solution is not something that can add value to your business. There are many failed implementations out there. If you are creating a recommendation engine make sure your’s is not on the list. Here are some reasons why many of these algorithms fail:
- Lack of Surprise Element: You need to amaze your users with your suggestions. If you showcase only one kind of content or something that won’t generate curiosity then your system is doomed. Showcase suggestions that your users are not likely to search for themselves to surprise them.
- Breach of Privacy: While collecting data it is essential that you anonymise it. While everyone likes to be treated personally, no one likes to share too much information on the internet. You need to anonymise the user information so that it cannot be accessed or misused by hackers.
- Diversity: If your recommendation engine keeps on recommending only one kind of content like the same genre of movie than soon your users will get bored. You need to ensure that you provide a diverse variety of content to keep your users hooked.
Read More: 5 Uncommon ways of using Big Data in retail
Recommendation Engines in Wordpress and Magento:
28.7% of the web is powered by Wordpress while Magento has always been the preferred choice for e-commerce. If you are using a content management system then you would have probably heard about the prebuilt recommendation engine plugins. That sounds great! No need to code. No need to invest huge money. Simply install a plugin and enjoy. As good as it sounds it extremely unrealistic. For any company of good size, going with a prebuilt solution is not a good option. The need, product, model and customers of every business are different. You need a recommendation system that is designed to help your business. With the ease of building custom Magento and Wordpress, creating a recommendation engine is not something challenging. Tech giants like Microsoft, have made customized recommendation engines available to a company of any size. All you need is a capable data scientist by your side.
At NewGenApps, we have created many successful recommendation engines. One of our client is UNBXD, for whom we created a recommendation system in the mobile app. (Check Case Study Here). We created an auto-suggest algorithm to support auto-completion of the search query. It uses artificial intelligence to store the product feed in the Unbxd cloud infrastructure from where the data is retrieved and displayed on the website. If you are looking for skilled developers who can create recommendation engines for your business then you search ends here. Feel free to contact us for or consultation or project.