4 Ways To Supercharge Your Recommendation System

Testimonials are one of the most important tools a company can use to show potential customers how valuable their products and services are. Testimonials are a short statement that describes how a product or service worked for a customer.

Tips on Writing Personal Recommendation Letters

1. Think carefully before saying yes.
2. Follow a business letter format.
3. Focus on the job description.
4. Explain how you know the person, and for how long.
5. Focus on one or two traits.
6. Remain positive.
7. Share your contact information.
8. Follow the submission guidelines.

1. 1 — Ditch Your User-Based Collaborative Filtering Model.
2. 2 — A Gold Standard Similarity Computation Technique.
3. 3 — Boost Your Algorithm Using Model Size.
4. 4 — What Drives Your Users, Drives Your Success.

Collaborative filtering (CF) and its modifications is one of the most commonly used recommendation algorithms. Even data scientist beginners can use it to build their personal movie recommender system, for example, for a resume project.

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.

For example, recommending news articles based on browsing of news is useful, but would be much more useful when music, videos, products, discussions etc. from different services can be recommended based on news browsing. To overcome this, most content-based recommender systems now use some form of hybrid system.

Let me introduce you to three very important types of recommender systems:

• Collaborative Filtering.
• Content-Based Filtering.
• Hybrid Recommendation Systems.

Collaborative filtering systems have many forms, but many common systems can be reduced to two steps:

1. Look for users who share the same rating patterns with the active user (the user whom the prediction is for).
2. Use the ratings from those like-minded users found in step 1 to calculate a prediction for the active user.

Content-based recommendation systems uses their knowledge about each product to recommend new ones. Recommendations are based on attributes of the item. Content-based recommender systems work well when descriptive data on the content is provided beforehand. “Similarity” is measured against product attributes.

Hybrid recommender systems combine two or more recommendation strategies in different ways to benefit from their complementary advantages. We address the most relevant problems considered and present the associated data mining and recommendation techniques used to overcome them.

Netflix, YouTube, Tinder, and Amazon are all examples of recommender systems in use. The systems entice users with relevant suggestions based on the choices they make. Recommender systems can also enhance experiences for: News Websites.