Big data applications based on web mining techniques and recommender systems: Survey

The increase in data from modern sources, the heterogeneous nature of data, ambiguous and unstructured data, and the so-called Big Data with all of its five v’s characteristics, indicate a growing need to use approaches that provide assistance in modeling and processing these data, provide additional automated data processing, and so on. The majority of all these studies involve one or more big data sets that may be used in various applications. Most of these selected papers use one or more distributed frameworks (such as MapReduce, Spark, or HDFS Hadoop distributed file systems). Furthermore, more than one strategy of Web mining is dealt with (such as Naive Bayes, Logistic Regression, and Random Forest, as examples) more than recommender system types (collaborative filtering, or/and content-based filtering). Various data sets were used (such as Movie Lens data set, LDOSCoMoDa data set, real bank loans dataset, using Facebook and Twitter for collecting data, data was taken across different companies, thousands of movies were used as exemplary data sets, or data sets used a sample data from different schools of Central Board of Secondary Education (CBSE) across India). Testing accuracy on the dataset obtained is required in the accuracy used (precision and recall). The best precision was 0.9886 and the best recall was 0.9835 in these studies. We have displayed some studies that have high precision and low recall and some other studies that have low precision and high recall. This paper introduces a literature survey about recommender systems that deal with big data through web mining techniques.