Recommendation For Using Smartphone

Recommendation For Using Smartphone – In this blog I will discuss various things about referral system like what is referral system? What are its use cases? How many types of recommendation systems and metrics are used for this.

In the image above of Amazon, you’ve probably seen this page many times when trying to buy something from Amazon. It’s a recommendation for the product you’re trying to buy, and you’ll be surprised to know that 35% of Amazon’s revenue comes from these recommendation engines. By now, you’ve probably noticed the power of a recommendation engine. These days, every small and large business uses a recommendation engine. Now let me discuss this.

Recommendation For Using Smartphone

A recommender system is a subclass of information filtering systems that attempt to predict the rating or preference a user might give an item. Simply put, it’s an algorithm that suggests relevant items to users. For example: what movie to watch in the case of Netflix, what product to buy in the case of e-commerce, or what book to read in the case of Kindle, etc.

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A. Personalized content: Helps improve the experience on a site by creating dynamic recommendations for different types of audiences, like Netflix.

B. Improve Product Search Experience: Helps categorize products based on their characteristics. For example: Material, season, etc.

In this type of recommender system, relevant items are displayed using the content of previously searched items by users. Here, content refers to the attribute/tag of the product that the user likes. In this type of system, products are tagged using certain keywords, then the system tries to understand what the user wants and it searches its database and finally tries to offer different products that the user needs.

Let’s take the example of a movie recommendation system where each movie is associated with a genre called a tag/attribute in the above case. Now suppose user A arrives and initially there is no data about user A. So initially the system tries to recommend popular movies to the users or the system tries to get information about the user by getting the form filled by the user. After some time, users may have rated some movies because it gives good rating to movies based on action genre and bad rating to movies based on anime genre. This is how the system recommends action movies to users. But here you can’t say that the user doesn’t like animated movies, because the user might not like this movie for other reasons like acting or story, but actually likes animated movies, and in this case, more data is needed.

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Recommending new items to users based on the interests and preferences of other similar users is essentially collaborative filtering. For example: – When we buy from Amazon, it offers new products

This overcomes the disadvantage of content-based filtering, because it uses user interaction instead of content in objects used by users. It only needs historical performance of users. Based on historical data, users who consented in the past are expected to consent in the future.

The rating of the object is made using the rating of neighboring users. Simply put, it is based on the concept of user affinity.

Let’s see an example. On the left you will see a picture where 3 children have named A, B, C and 4 fruits i.e. grape, strawberry, watermelon and orange.

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Based on the picture, we can assume that A bought all four fruits, B bought only strawberries and C bought watermelon along with strawberries. Here, A and C are similar types of users, as this C is recommended Grape and Orange are indicated by the dotted line.

The object’s rating is estimated using the user’s own rating of neighboring objects. Simply put, it is based on the concept of object similarity.

Let’s see about users and objects with the same example as above. The only difference here is that we see dissimilar users, for example, if you see grapes and watermelon, you understand that watermelon is bought by all of them, and grapes are bought by children A and B. Therefore, C grapes are recommended for children.

Now that you understand both, you may be wondering which one to use when. If the number of objects is more than the number of users, then use user-based collaborative filtering, this is the solution, because it reduces computing power, and if the number of users is more than the number of objects, go for object-based collaborative filtering. For example, Amazon has a few thousand items for sale, but has billions of customers. Therefore, Amazon uses object-based collaborative filtering because there is no reason. products compared to customers.

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As we have discussed different types of recommendation systems, their pros and cons, but how to evaluate whether a given model is recommending the right things and how much the system predicts is important, and here comes the evaluation metrics. There are several metrics to evaluate a model, but here we discuss 4 key metrics.

It shows how relevant the list of recommended articles is. Here, K represents the Recommended items in the top k matching set.

This is the percentage of objects in the training data sample that can be represented in the test set. Or simply, the percentage that any advisory system can predict.

It’s basically how many of the same elements the model offers to different users. Or the difference between user lists and offers.

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This blog has covered many topics related to recommendation engines such as what it is and its use cases. In addition to different types of recommendation systems such as content-based filtering and collaborative filtering and collaborative filtering, user-based as well as object-based with its examples, advantages and disadvantages and finally evaluation metrics for model evaluation.

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