Improved FC-LFM Algorithm Integrating Time Decay Factor

摘要

Different from the search engine system, the recommendation system can help users quickly find their interesting items from the massive data in a personalized way. Traditional collaborative filtering algorithms based on users and items need to define the granularity, dimension, and weight of classification subjectively when calculating similarity based on user behavior. So the accuracy and calculation efficiency of prediction scoring results are not high enough. LFM (latent factor model) based on data itself, adopts automatic clustering according to user behavior and uses a machine-learning method to mine hidden features from the user’s historical scoring data. But when the amount of data is large, there exists data sparsity in the user rating matrix. Also, the user’s interest is always changing with time, and the items themselves have a certain life cycle. Based on the above problems, this paper first proves that the accuracy of LFM model is influenced by the popularity and diversity of negative samples through comparative experiments. Then, a new algorithm called FC-LFM (forgetting curve-latent factor model) is proposed. In this algorithm, the Ebbinghaus forgetting curve function is introduced to improve LFM model and the time decay factor is integrated into the iterative operation of negative sample popularity,matrix filling, user featurematrix, and item feature matrix. In the end, the improved FC-LFM collaborative filtering algorithm is proved to be superior to the traditional UserCF, UserCF-IIF, ItemCF, ItemCF-IUF, and LFM algorithm in accuracy and recall rate by comparing experiments on the MovieLens data set.