content based recommendation
Data Mining

Content based Recommendation System

The recommendation system can be developed based on Content-Based filtering, and collaborative filtering models. in this article, we are discussing a content-based Recommendation system. The content-based Recommendation system utilizes the data taken from the user which may be captured directly or indirectly from the user. For example by using a rating, review, or clicking on a link. The collected data will be used to prepare a user profile, which will be used to suggest the product or service to users. This type of recommendation system is adaptive in nature and improves its performance with user inputs,  and becomes more accurate. You can also watch the video in Hindi for the same content using this link

content based recommendation

Figure 1 Content-based recommendation system

User Profile: The content-based recommendation systems prepare a User profile. this process of making a user profile is termed Profiling. using this process the algorithms create vectors that describe the user’s preference. The User profile utilizes a utility matrix to describe the relationship between user and item.

Item Profile: we also need to build a profile for each item. this item or product profile represents the characteristics of that item. For example, if we consider a movie as an item then its actors, director, and the release year are the features or characteristics of the movie. We can also add its rating as a profile attribute of the product.

Utility Matrix: The Utility Matrix describes the user’s preference for certain items. During data collection from users, we have to consider the items which are liked and disliked by users. the preferences using this matrix are represented in terms of a score that is assigned to each user-item pair, sometimes this score is called the degree of preference.


Implementation techniques

there are two kinds of concepts by which we can implement the content-based recommendation system.

Method 1: in this technique, we can use a distance or similarity-based function to measure the likelihood between the item and the user’s preferences. the most similar products with similar characteristics based on the item are predicted as recommendations. For example: Let a user will have like a number of actors that tend to appear or not appear based on dislike. Consider a movie with actors that the user likes and only a few actors that the user doesn’t like, then the distance between the user and the movie will be measured and the number of actors liked by the user then be recommended to view. The distance indicates the user tends to like the movie.

Method 2: In this method of implementation, we can use a machine learning classifier in the recommendation systems, like Decision Tree or other. The classifier tries to find out whether a user wants to watch a movie or not based on the characteristics. At each level of prediction, we can apply a certain condition to refine our predictions.

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