I need an explanation or materials on this:
How is O(n) considered extremely big or inefficient for collaborative filtering when the datasets is extremely huge?
Dear Folasade O. Isinkaye
In general, the whole ratings database is searched in collaborative filtering and thus it suffers from poor scalability when more and more users and items are added into the database.
Kind Regards
Qamar Ul Islam
Collaborative filtering relies on calculating the similarities among users or items to find the appropriate neighbors and the number of users and items in a system grows rapidly. For example, the behavior of such a user per day may result in his stored data reaching the size of TBs in some popular websites. Furthermore, the RS should respond in less than a second to keep users satisfied and to enable them to continuously engaged with the RS . As a result, both large-scale datasets and responding time create a challenge in designing efficient RS and as a result, it demands colossal computing resources. please refer to:
Article Multi-Criteria Review-Based Recommender System – The State of the Art
Since a collaborative filtering algorithm is mainly based on similarity measures computed over the co-rated set of items, the large levels of sparsity can lead to less accuracy and can challenge the predictions or recommendations of the collaborative filtering (CF)systems. Lets assume example Commercial recommender systems in general are used to evaluate very large product sets. In a user – item rating database, though users are very active, there are a few rating of the total number of items available. The user-item matrix is thus extremely sparse. Further, a CF algorithm is assumed to be efficient if it is able to filter items that are interesting to users. But, they require computations that are very expensive and grow non-linearly with the number of users and items in a database. In general, the whole ratings database is searched in collaborative filtering and thus it suffers from poor scalability when more and more users and items are added into the database. Instigated by these challenges, two collaborative filtering algorithms, firstly an algorithm based on weighted slope one scheme and item clustering & secondly an algorithm based on item classification & item clustering were studied, which dealt with the sparsity and scalability issues simultaneously.
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