ABSTRACT A recommendation system provides personalized recommendations on products and services to users. In the traditional recommendation system, the user interest is regarded as constant over time, while in fact, the user interest changes over time. Hence, tracking the user interest drift becomes key in designing the dynamic recommendation system. However, it is a challenge to find an accurate and effective method that can predict the user interest drift. To solve the prediction problem of the user interest drift, this paper adopts clustering and time impact factor matrix to monitor the degree of user interest drift in the class and more accurately predict an item's rating. We add a time impact factor to the original baseline estimates and use the linear regression to predict the user interest drift. Our comparative experiments are conducted on three big data sets: MovieLens100K, MovieLens1M, and MovieLens10M. The experimental results show that our proposed approach can efficiently improve the prediction accuracy. INDEX TERMS Collaborative filtering, recommender systems, concept drift, time weight, MovieLens. With the development of information technology and Internet, people gradually entered an information-explosion era from the past information-scarce one. Everyday, people will get a lot of information from different ways, but most are useless noise and valuable information is mixed in the information noise. In order to improve the efficiency, people need to filter out useless information by some filtering techniques. When user has clear demands, classified directory and search engine can be a good way to solve the problem of information overload [1] . However, most user demands are not clear in real life, recommendation system came into being. Recommendation system uses user modeling and user historical data to predict the user's favorite information. This method speeds up transmission efficiency of useful information and highlights the individuation, comparing with friend recommendation. At the same time, recommendation system is advantageous to the information producer to carry on the user population localization. Recommendation system is often divided into several categories: Content-based [2] : the user will be recommended the items that are similar to his favorite items in the past. Collaborative filtering [3] : the user will be recommended the items that are liked by people who have the same interests and hobbies with the user. Hybrid: it combines two recommendation models to make recommendations. Collaborative filtering recommendation system has received more attention in the past ten years, because it doesn't require too much professional knowledge and has the ability to discover models which are more complex and difficult to be discovered. Collaborative filtering system has two classical models: nearest neighbor model and latent factor model. Nearest neighbor model uses neighbor relationships between people or between items. In user-based neighbor model, the user's nearest neighbors are used to predict the item's rating that the user will give. In item-based neighbor model, the user's rating for the item is predicted by the item's nearest neighbor ratings. Nearest neighbor model pays more attention to the relationships between people or between items, instead of the relationships between people and items, thereby it reduces a lot of complex calculations. But nearest neighbor model has its own shortcoming: it doesn't carefully analyze the users' rating, so that this model is good at recommending item and not good at predicting rating. Because use nearest neighbors are generated by clustering. If user u doesn't rate item i, most neighbors of user u don't rate item i. We can't get more accurate rating prediction by nearest neighbor method.
Sun and Dong REF adopt clustering and time impact factor matrix to predict user interest drifts through a linear regression.
Dynamic Model Adaptive to User Interest Drift Based on Cluster and Nearest Neighbors
IEEE Access
IEEE Access
Computer Science