Abstract
Recommender Systems are tools to understand the huge amount of data available in the internet world. Collaborative filtering (CF) is one of the most knowledge discovery methods used positively in recommendation system. Memory collaborative filtering emphasizes on using facts about present users to predict new things for the target user. Similarity measures are the core operations in collaborative filtering and the prediction accuracy is mostly dependent on similarity calculations. In this study, a combination of weighted parameters and traditional similarity measures are conducted to calculate relationship among users over Movie Lens data set rating matrix. The advantages and disadvantages of each measure are spotted. From the study, a new measure is proposed from the combination of measures to cope with the global meaning of data set ratings. After conducting the experimental results, it is shown that the proposed measure achieves major objectives that maximize the accuracy Predictions.
Highlights
Recommender systems are tools that utilize the beliefs of a group of users to assist entities in that group to effectively explore new things of interest from a possibly tremendous set of choices
Collaborative Filtering (CF) can be categorized into two main algorithms: memory-based and model-based
Challenges of Collaborative Filtering Techniques A brief introduction to the challenges that are considered important for the development of the research on recommender systems is introduced: 1- Cold-start problem: This refers to a situation where a recommender does not have adequate information about a user or an item in order to make relevant predictions
Summary
Recommender systems are tools that utilize the beliefs of a group of users to assist entities in that group to effectively explore new things of interest from a possibly tremendous set of choices. Challenges of Collaborative Filtering Techniques A brief introduction to the challenges that are considered important for the development of the research on recommender systems is introduced: 1- Cold-start problem: This refers to a situation where a recommender does not have adequate information about a user or an item in order to make relevant predictions. This is one of the major problems that reduce the performance of recommendation system.(2) 2- Data sparsity problem: This problem occurs as a result of lack of enough information, that is, when only a few of the total number of items available in a database are rated by users.
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