Product Recommendation System
Introduction to Product Recommender SystemPersonalized product recommendation engines help millions of people narrow the universe of potential products to fit their unique tastes. These services depend on a machine-learning strategy called Long Short Term Memory (LSTM), which breaks down products into long lists of attributes and matches these elements to a customer's preferences. A hybrid filtering approach is adopted later. The technique can be extended to just about any recommendation system, from Internet search engines to dating sites.
Need for Recommender SystemsWorld Wide Web continues to grow at an exponential rate, the size and complexity of many web sites grow along with it. For the users of these web sites it becomes increasingly difficult and time consuming to find the information they are looking for. User interfaces could help users find the information that is in accordance with their interests by personalizing a web site. The interests of users may change overtime which would require them to change their selection frequently. Recommender systems provide personalized information by learning the user’s interests from traces of interaction with that user.
Involves 3 Stages
Tracks and stores data on all customer behaviour and activities. Every click the user makes for the product price, a record is kept.Events are captured for all kinds of actions like:
- User liking a product
- Adding a product to the cart
- Purchasing a product
They reveal what a user feels about a product. Implicit values of rating, with a maximum value of 5 Also take into account the ratings and feedbacks a user provides for a particular product.
Filtering products based on ratings and other user data
Three types of filtering is used:
- Collaborative Filtering
If user X likes products A,B,C and D , and user Y likes products A,B,C,D and E. Then user X likes product E.
- User Based
User’s browsing history, likes, purchases and ratings are taken into account and product recommendations are made.
- Hybrid Approach
Combines Collaborative and User Based approach.
Working of Pixuate Recommender System
A typical recommendation engine processes data through the following four phases namely collection, storing, analyzing and filtering.
The first step in creating a recommendation engine is gathering data. Data can be either explicit or implicit data. Explicit data would consist of data inputted by users such as ratings and comments on products. And implicit data would be the order history/return history, Cart events, Page views, Click thru and search log. This data set will be created for every user visiting the site. Behavior data is easy to collect because you can keep a log of user activities on your site. Collecting this data is also straightforward because it doesn’t need any extra action from the user; they’re already using the application. The downside of this approach is that it’s harder to analyze the data. For example, filtering the needful logs from the less needful ones can be cumbersome. Since each user is bound to have different likes or dislikes about a product, their data sets will be distinct. Over time as you ‘feed’ the engine more data, it gets smarter and smarter with its recommendations so that your email subscribers and customers are more likely to engage, click and buy. Just like how the Amazon’s recommendation engine works with the ‘Frequently bought together’ and ‘Recommended for you’ tab.
The more data you can make available to your algorithms, better the recommendations will be. This means that any recommendations project can quickly turn into a big data project. The type of data that you use to create recommendations can help you decide the type of storage you should use. You could choose to use a NoSQL database, a standard SQL database, or even some kind of object storage. Each of these options is viable depending on whether you’re capturing user input or behavior and on factors such as ease of implementation, the amount of data that the storage can manage, integration with the rest of the environment, and portability. When saving user ratings or comments, a scalable and managed database minimizes the number of tasks required and helps to focus on the recommendation. Cloud SQL fulfills both of these needs and also makes it easy to load the data directly from Spark.
How do we find items that have similar user engagement data? In order to do so, we filter the data by using different analysis methods. If you want to provide immediate recommendations to the user as they are viewing the product then you will need a more nimble type of analysis.Some of the ways in which we can analyze the data are:
- Real-time systems can process data as it’s created. This type of system usually involves tools that can process and analyze streams of events. A real-time system would be required to give in-the-moment recommendations.
- Batch analysis demands you to process the data periodically. This approach implies that enough data needs to be created in order to make the analysis relevant, such as daily sales volume. A batch system might work fine to send an e-mail at a later date.
- Near-real-time analysis lets you gather data quickly so you can refresh the analytics every few minutes or seconds. A near-real-time system works best for providing recommendations during the same browsing session.
Pixuate uses Hybrid approach of filtering which makes the best use of Collabrative and User Based filtering
Major benefits of using a recommendation engine areRevenue
With years of research, experiments and execution primarily driven by Amazon, not only is there less of a learning curve for online customers today. Many different algorithms have also been explored, executed, and proven to drive high conversion rate vs. non-personalized product recommendations. Customer Satisfaction – Many a time customers tend to look at their product recommendation from their last browsing. Mainly because they think they will find better opportunities for good products. When they leave the site and come back later; it would help if their browsing data from the previous session was available. This could further help and guide their e-Commerce activities, similar to experienced assistants at Brick and Mortar stores. This type of customer satisfaction leads to customer retention.
We often take recommendations from friends and family because we trust their opinion. They know what we like better than anyone else. This is the sole reason they are good at recommending things and is what recommendation systems try to model. You can use the data accumulated indirectly to improve your website’s overall services and ensure that they are suitable according to a user’s preferences. In return, the user will be placed in a better mood to purchase your products or services.
For example, the “Genius Recommendations” feature of iTunes, “Frequently Bought Together” of Amazon.com makes surprising recommendations which are similar to what we already like. People generally like to be recommended things which they would like, and when they use a site which can relate to his/her choices extremely perfectly then he/she is bound to visit that site again.
Is an integral part of a personalization system. Giving the client accurate and up to the minute, reporting allows him to make solid decisions about his site and the direction of a campaign. Based on these reports clients can generate offers for slow moving products in order to create a drive in sales.
LSTM NetworksPixuate uses the state of the art LSTMs to build their recommender systems. Long Short Term Memory networks – usually just called “LSTMs” – are a special kind of RNN, capable of learning long-term dependencies. They were introduced by Hochreiter & Schmidhuber (1997), and were refined and popularized by many people in following work.1 They work tremendously well on a large variety of problems, and are now widely used. LSTMs are explicitly designed to avoid the long-term dependency problem. Remembering information for long periods of time is practically their default behavior, not something they struggle to learn!
Advantages of Pixuate Recommendation Engine
- Pixuate recommendation systems are based on actual user behavior i.e. objective reality. This is the biggest advantage - watching people in their natural environment and making movie decisions directly on the results. For example, the "Suggested Product Price" feature suggests product prices and discounts based on our activity and likes.
- Recommendation systems are effective tools for personalization. We often take recommendations from friends and family because we trust their opinion. They know what we like better than anyone else. This is the sole reason they are good at recommending things. This is what recommendation systems try to model.
- Our Recommendation systems are always up-to-date. A new product price in the list gets recommended as long as people rate it highly. The ability of our recommendation system to bubble up activity in real time is a huge advantage because the system is always on.
- Most of the organizational maintenance of a site is keeping the navigation system in line with the users' changing needs. With Pixuate recommendation systems, organizational maintenance is reduced. Based on user activity, the system recommends navigation options to the user. Pixuate Recommendation systems are intensive, database-driven applications that are easy to set up and get running.
- Pixuate Recommendation systems are intensive, database-driven applications that are easy to set up and get running
- Uses hybrid type filtering to make an effective recommendation engine.
- Having great product recommendations for your customers is really just having the courage to dive into better conversation. And remember – the only way to truly engage with customers is to communicate with each as an individual.
- Use of cognitive methods such as deep learning, social learning, and tensor factorization which are based on machine learning and neural networks