Table of Contents
Which technique is used for product recommendation?
Product recommendation techniques are being used widely to reduce this extra overload and recommend the scrutinized product to the customers. Collaborative filtering, Association rules and web mining are on top amongst the techniques that is being used for recommendation technology.
What are the main types of recommendation systems?
There are majorly six types of recommender systems which work primarily in the Media and Entertainment industry: Collaborative Recommender system, Content-based recommender system, Demographic based recommender system, Utility based recommender system, Knowledge based recommender system and Hybrid recommender system.
What are the two types of recommendation system?
There are two main types of recommender systems – personalized and non-personalized. Non-personalized recommendation systems like popularity based recommenders recommend the most popular items to the users, for instance top-10 movies, top selling books, the most frequently purchased products.
What is the easiest recommender in Elasticsearch?
The easiest recommender is a summary statistic without any personalisation. Elasticsearch Aggregations are useful for this task. But instead of demonstrating this fact with some explicit knowledge about the personal preferences of a user, we can improve our recommendation.
What are the downsides of Elasticsearch collaborative filtering?
The downside of using Elasticsearch in that way is that every query has a significant CPU cost. If collaborative filtering works well for your use-cases then it’s probably best to choose a better collaborative filtering algorithm and compute the recommendation signals more efficiently in batch rather than on the fly.
How do users interact with Elasticsearch products?
Users interact with a series of products and content on your site. Adding a state memory to your model allows you to query Elasticsearch with an interaction series for a user which expands the possibilities of matches to work with.
Is your Elasticsearch cluster getting out of control?
As you add more and more metrics to your model you will notice that not only the cost of your Elasticsearch cluster spirals out of control, the time and CPU resources to process signals in your rules engine are equally on a steep increase. Your rules engine translating signals into final recommendations starts to get significantly complex.