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Three main techniques are widely used to build recommender systems: collaborative filtering, content-based filtering and hybrid algorithm.
The collaborative filtering algorithm is the most widely used and the most matured technique. It is a process of filtering information or pattern based on the similarity between items.
Content-based algorithm is based on the matching of user profile and some specific characteristics of an item.
But single Recommender system algorithm has its own limitation.
Collaborative filtering performs well when there is sufficient rating information, but its performance is not good when the rating is sparse and has cold-start problem for new items.
Content-based filtering is able to handle the case of new items, but the items must be encoded with meaningful feature
hybrid algorithm combines the features of two or more recommender techniques to overcome the drawbacks of one recommender technique and obtain the advantages of different recommender techniques.
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IEEE/ICACT20230130 Slide.03
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