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Two of the most familiar and successful recommendation techniques are probably Collaborative filtering (CF) and Content-based filtering (CBF).
- Collaborative filtering recommender systems is the most mature and the most commonly implemented in different application areas, for example Amazon since 1998. The filtering uses customer’s ratings to categorize users in groups according to their similarity. Recommendations are then inferred by taking into account ratings of users’ in the same group.
- In contrast to CF, Content-based recommender systems computes recommendations according to the features of items that the user preferred in the past.
The main difference compared to collaborative filtering is that the content based approach offers the recommendation based on similarity not only by rating, but also the information from the products i.e., the movie title, the year, the actors, the genre, story. Thus, in order to implement this CB, it is necessary to process information describing each item.
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IEEE/ICACT20200192 Slide.03
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