Pros and Strengths.
Cons and weakness.
Recommender system is a system which can recommend product using pre-labeled data and customer behavior from the large categories of the data under given context or scenario. In most cases there is billions record on which a recommendation runs and gives recommendations. This process is often slow to speed up this recommendation algorithm different techniques are proposed which includes reduce time complexity of algorithm. Most recent approaches are reducing the size of input data from millions to thousands records. To do so sampling techniques are used which takes samples from the original data and the algorithm run on that sample data instead of original waste amount of data. This paper shows the sampling techniques and discusses the improvement factor in those techniques. In this paper is also suggestion about how to improve sampling techniques by using low bias and mean squared errors.
Improving recommendation system, by applying matric average on recommendation function which gives the how good a recommendation system perform. Area under the curve which shows the probability of the recommended item for relevance is greater than the any random chosen recommended item.
While specifying the quality of a recommendation system, if assumes that all the items (relevant items) are equally preferred by the user, instead of ranking of those Items as most favorable to least favorable. Instead of using random item to recommended item comparison, a well-known algorithm should be used to calculate likelihood by calculating area under the curve.
Evaluate the assumption that a customer has equal presidency for items recommended by the recommendation engine while evaluating the performance of a recommendation system. Use a well-known recommendation algorithm to show that an item recommended by the Recommendation system is better or worse than that well known recommendation system instead of comparing it with randomly recommended items.
This paper discusses about recommendation system and sampling techniques to train recommendation system instead of training on whole data which is expensive and slow. It shows the techniques to improve sampling techniques. Also provides a way to evaluate the performance of a recommender system that how good a recommendation system performs.
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