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Information is rising exponentially over the Internet. The World Wide Web has emerged as a treasure trove of knowledge and provide relevant information pertaining to any exclusive topic as per the individual's performance or demand. Frequently, the user gets confused while seeing such a large number of over the Internet to choose which one to buy....
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Music recommendation systems play a crucial role in addressing the challenges of information overload and personalization in the digital music landscape. This paper presents the implementation and contribution of a novel music recommendation system that aims to enhance the user experience and overcome the limitations of existing approaches. The AI-...
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... More similarity is implied by angles with lower values, and vice versa. The uncentered cosine similarity measure is so named since it does not provide for data centering or modification of preference values [ (Zubair et al., 2019)]. Cosine similarity is computed as follows: ...
... They mention that better results are obtained from a combination of similarity measures because another measure strengthens the weakness of each of the measures. Authors in (Zubair et al., 2019) analyzed the disadvantages of the existing similarity measure. They compare all correlation similarities with the best distance similarity, and they obtained that the cosine similarity models performed better than other similar models. ...
A recommendation system represents a very efficient way to propose solutions adapted to customers needs. It allows users to discover interesting items from a large amount of data according to their preferences. To do this, it uses a similarity metric, which determines how similar two users or products are. In the case of recommender systems, similarity computation is a practical step. The calculation of similarity may be used for both items and users. Following the similarity calculation, a user or item with a comparable computation value can be recommended together with the goods to a user with similar preferences. The user’s requirements influence the choice of similarity metric. This paper explores various similarity measurement methods employed in recommender systems. We compare correlation and distance techniques to determine the capabilities of different similitude calculation algorithms and synthesize which similarity measure is adapted for which type of recommendation.
