Deep latent factor model for collaborative filtering

Abstract

Latent factor models have been used widely in collaborative filtering based recommender systems. In recent years, deep learning has been successful in solving a wide variety of machine learning problems. Motivated by the success of deep learning, we propose a deeper version of latent factor model. Experiments on benchmark datasets shows that our proposed technique significantly outperforms all stateof-the-art collaborative filtering techniques.

Application of sample balance-based multi-perspective feature ensemble learning for prediction of user purchasing behaviors on mobile wireless network platforms

“…Memory-based CF has data sparsity problem with weak extensibility [13]. Different from memory-based CF, model-based CF trains model mainly through user ratings for commodities and then predicts userpurchasing probability [14]. At present, many model-based CF algorithms have been put forward, where matrix decomposition has gradually become the mainstream method in modelbased CF algorithms by virtue of transformation of high-dimensional sparse user rating data and excellent extensibility [12].…”

Application of sample balance-based multi-perspective feature ensemble learning for prediction of user purchasing behaviors on mobile wireless network platforms

“…Memory-based CF has data sparsity problem with weak extensibility [13]. Different from memory-based CF, model-based CF trains model mainly through user ratings for commodities and then predicts user purchasing probability [14]. At present many model-based CF algorithms have been put forward, where matrix decomposition has gradually become the mainstream method in model-based CF algorithms by virtue of transformation of highdimensional sparse user rating data and excellent extensibility [12].…”

Application of Sample Balance-Based Multi-Perspective Feature Ensemble Learning for Prediction of User Purchasing Behaviors on Mobile Wireless Network Platforms

“…Memory-based CF has data sparsity problem with weak extensibility [9] . Different from memory-based CF, model-based CF trains model mainly through user ratings for commodities and then predicts user purchasing probability [10] . At present many model-based CF algorithms have been put forward, where matrix decomposition has gradually become the mainstream method in model-based CF algorithms by virtue of transformation of high-dimensional sparse user rating data and excellent extensibility [8] .…”

“…N L l    (9) Slicing input data is the key to realizing multi-granularity deep forest. Assume "sliding window" size as   (10) Slice size is lL WW  , and slice number of the whole dataset is: (11) After multi-granularity scanning, the slices of all input data are input into the random forest If there are RF n random forests, the quantity of class vectors generated through "sliding window" sampling is: (13) The final output data size after multi-granularity scanning is: (14) Fig . 9 shows the overall process of cascaded deep forest with three "sliding windows", the sizes of which are d/16, d/8 and d/4, respectively, where d represents feature number.…”

“…As for the time involved in the features, most of the features ranking top 10 are about user purchasing behaviors 1 day before the prediction date, e.g. features (1), (2), (3), (5) and (10). In addition, time-related features (4), (7) and (9) are also about user purchasing behaviors 3 days before the prediction date, indicating that the closer the user's historical purchasing behavior to the prediction date, the greater the influence on prediction of user purchasing behavior.…”