In the immortal words of Steve Jobs: “A lot of times, people don’t know what they want until you show it to them.” You might believe that customers will love your movie, your product, your job opening- but they may not even know it exists. The job of a Recommendation Engine is to open the customer/user up to a whole new world of products and possibilities, which they would not even think of directly searching for themselves.
A Recommendation Engine plays a vital role in increasing the chances of a user buying a product. In today’s world, with tons of data from searched products available (thanks to the digital data explosion), it is very easy to find what people are likely to buy – just by looking at their ‘intent’ data. Needless to say, right analysis of such BIG data is absolutely necessary. This is where the Recommendation Engine comes into the picture.
Recommendation Engines are best known for their use in e-commerce web sites. Simply put, they use information about a customer’s interests as input to generate a list of recommended items. Some engines only use the items customers purchase explicitly to predict the likelihood of purchase of a product in similar category. Others use attributes such as items viewed, demographic data, and interests etc. to recommend. It all depends on the need of the company. For example:
- A large retailer might have huge amount of data, tens of millions of customers and millions of distinct catalog items over decades of data and can bake in detailed historical analysis into its recommendation engine
- An e-commerce company may require recommendation results to be available in real time, while still producing high-quality recommendations and may rely more on digital footprint as against historical purchase data analysis as input to its recommendations. Each customer interaction provides valuable data, and the recommendation engine must be tailored to respond immediately to new information
- For new customers typically, there is limited information available, based on only a few purchases or product ratings while older customers can provide a glut of information, based on thousands of purchases and ratings
There are three common approaches to solve the recommendation problem:
- Traditional collaborative filtering
- Cluster models and
- Item-based methods
Collaborative filtering is the process of filtering for information or patterns using techniques involving collaboration among multiple agents, viewpoints, data sources, etc. Applications of collaborative filtering typically involve very large data sets. Collaborative filtering methods have been applied to many different kinds of data including: sensing and monitoring data, such as in mineral exploration, environmental sensing over large areas or multiple sensors; financial data.
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics.
Item based collaborative filtering:
Item-based collaborative filtering algorithm uses the similarity between items- items and then select the most similar items. The basic idea in similarity computation between two items is to first isolate the users who have rated both of these items and then to apply a similarity computation technique to determine the similarity.
For large retailers , a good recommendation algorithm is scalable over very large customer bases and product catalogs, requires only sub second processing time to generate online recommendations, and is able to react immediately to changes in a user’s data, and makes compelling recommendations for all users regardless of the number of purchases and ratings.
In the future, we expect the retail industry to more broadly apply recommendation algorithms for targeted marketing, both online and offline. While e-commerce businesses have the easiest vehicles for personalization, the technology’s increased conversion rates as compared with traditional broad-scale approaches will also make it compelling to offline retailers for use in postal mailings, coupons, and other forms of customer communication.
But now recommendation engine is not limited to only e-commerce industry, it has wide spread to other industry as well, whether it is hospitality sector, or property sector or supply chain everywhere it has shown its presence and also its impact in the industry.
This blog is written by Vivek Kumar, Analytics Consultant at BRIDGEi2i
About BRIDGEi2i: BRIDGEi2i provides Business Analytics Solutions to enterprises globally, enabling them to achieve accelerated business impact harnessing the power of data. BRIDGEi2i combines domain expertise, advanced analytics and technology accelerators to embed analytics in the DNA of the organization to deliver improved return on investment. To know more visit www.bridgei2i.com
The views and opinions expressed in this article are those of the author and do not necessarily reflect the official position or viewpoint of BRIDGEi2i.