A Survey Paper on Recommender Systems <s> Efficiency <s> Collaborative filtering (CF) has become an important data mining technique to make personalized recommendations for books, web pages or movies, etc. One popular algorithm is the memory-based collaborative filtering, which predicts a user’s preference based on his or her similarity to other users (instances) in the database. However, the tremendous growth of users and the large number of products, memory-based CF algorithms results in the problem of deciding the right instances to use during prediction, in order to reduce executive cost and excessive storage, and possibly to improve the generalization accuracy by avoiding noise and overfitting. In this paper, we focus our work on a typical user preference database that contains many missing values, and propose four novel instance reduction techniques called TURF1-TURF4 as a preprocessing step to improve the efficiency and accuracy of the memory-based CF algorithm. The key idea is to generate prediction from a carefully selected set of relevant instances. We evaluate the techniques on the well-known EachMovie data set. Our experiments showed that the proposed algorithms not just dramatically speed up the prediction, but also improved the accuracy. <s> BIB001 </s> A Survey Paper on Recommender Systems <s> Efficiency <s> Matrix factorization is a successful technique for building collaborative filtering systems. While it works well on a large range of problems, it is also known for requiring significant amounts of storage for each user or item to be added to the database. This is a problem whenever the collaborative filtering task is larger than the medium-sized Netflix Prize data. In this paper, we propose a new model for representing and compressing matrix factors via hashing. This allows for essentially unbounded storage (at a graceful storage / performance trade-off) for users and items to be represented in a pre-defined memory footprint. It allows us to scale recommender systems to very large numbers of users or conversely, obtain very good performance even for tiny models (e.g. 400kB of data suffice for a representation of the EachMovie problem). We provide both experimental results and approximation bounds for our compressed representation and we show how this approach can be extended to multipartite problems. <s> BIB002
In order for a recommender system to be reliable, not only it must be accurate, but also it must process within a reasonable time, make good use of the available resources, and handle hundred requests per second BIB001 . Memory and Computation time are two important metrics that evaluate the efficiency of a recommender system. Algorithms that work with item sets that has a static nature tend to pre-compute item similarity and stores a matrix of similarities. The more the items, the bigger the matrix will grow. Therefore, we will end up with a quick look up table that speeds up the recommendation process; however, an O(n 2 ) space is needed for n items  . Because of the space problem we may not consider all the n items of a system. Instead, we only consider a small fraction of the most similar items k where (k < n). This attempt will reduce the size of the lookup table but we will have a trade-off: smaller model size means a reduced quality  . Another approach to efficiently allocate space needed is to give each item a space according to the amount of rating. In other words, the more an item has ratings, the more space I allocate BIB002 . In some situation, the knowledge of customer preferences changes, memory consumption reduces and the time used for computation increases, therefore the efficiency of the recommender system in dynamic datasets depends on the amount of calculation required in an algorithm BIB001 . In this situation, two calculations must be performed: learning time and running time. In some cases, running time was fast but learning time was 8 hours. To speed up the calculation, we consider a relevant dataset rather than the whole database; again, a trade-off between the accuracy and efficiency. Another approach to speed up the calculation is to use data structure or other data mining techniques such as hierarchical clustering since searching for neighbors is faster than scanning the whole tree BIB001 .