scholarly journals Preference Mining Using Neighborhood Rough Set Model on Two Universes

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Kai Zeng
Keyword(s):  
Rough Set ◽  
User Satisfaction ◽  
Traditional Method ◽  
Cold Start ◽  
Effective Solution ◽  
Neighborhood Rough Set ◽  
Recommendation Accuracy ◽  
Item Data ◽  
Two Universes ◽  
Cold Start Problem

Preference mining plays an important role in e-commerce and video websites for enhancing user satisfaction and loyalty. Some classical methods are not available for the cold-start problem when the user or the item is new. In this paper, we propose a new model, called parametric neighborhood rough set on two universes (NRSTU), to describe the user and item data structures. Furthermore, the neighborhood lower approximation operator is used for defining the preference rules. Then, we provide the means for recommending items to users by using these rules. Finally, we give an experimental example to show the details of NRSTU-based preference mining for cold-start problem. The parameters of the model are also discussed. The experimental results show that the proposed method presents an effective solution for preference mining. In particular, NRSTU improves the recommendation accuracy by about 19% compared to the traditional method.

scholarly journals Sequence-Based Explainable Hybrid Song Recommendation

2021 ◽  
Vol 4 ◽  
Author(s):  
Khalil Damak ◽  
Olfa Nasraoui ◽  
William Scott Sanders
Keyword(s):  
Deep Learning ◽  
Cold Start ◽  
Musical Instrument ◽  
Learning Sequence ◽  
Digital Interface ◽  
Top Performers ◽  
Recommendation Accuracy ◽  
Deep Learning Model ◽  
Cold Start Problem ◽  
Validation Experiments

Despite advances in deep learning methods for song recommendation, most existing methods do not take advantage of the sequential nature of song content. In addition, there is a lack of methods that can explain their predictions using the content of recommended songs and only a few approaches can handle the item cold start problem. In this work, we propose a hybrid deep learning model that uses collaborative filtering (CF) and deep learning sequence models on the Musical Instrument Digital Interface (MIDI) content of songs to provide accurate recommendations, while also being able to generate a relevant, personalized explanation for each recommended song. Compared to state-of-the-art methods, our validation experiments showed that in addition to generating explainable recommendations, our model stood out among the top performers in terms of recommendation accuracy and the ability to handle the item cold start problem. Moreover, validation shows that our personalized explanations capture properties that are in accordance with the user’s preferences.

scholarly journals EXPRESS: Overcoming the Cold Start Problem of CRM using a Probabilistic Machine Learning Approach

2021 ◽  
pp. 002224372110329
Author(s):  
Nicolas Padilla ◽  
Eva Ascarza
Keyword(s):  
Machine Learning ◽  
Cold Start ◽  
Customer Relationship ◽  
Exponential Families ◽  
Modeling Framework ◽  
Wide Range ◽  
The Moment ◽  
Probabilistic Machine Learning ◽  
Marketing Actions ◽  
Cold Start Problem

The success of Customer Relationship Management (CRM) programs ultimately depends on the firm's ability to identify and leverage differences across customers — a very diffcult task when firms attempt to manage new customers, for whom only the first purchase has been observed. For those customers, the lack of repeated observations poses a structural challenge to inferring unobserved differences across them. This is what we call the “cold start” problem of CRM, whereby companies have difficulties leveraging existing data when they attempt to make inferences about customers at the beginning of their relationship. We propose a solution to the cold start problem by developing a probabilistic machine learning modeling framework that leverages the information collected at the moment of acquisition. The main aspect of the model is that it exibly captures latent dimensions that govern the behaviors observed at acquisition as well as future propensities to buy and to respond to marketing actions using deep exponential families. The model can be integrated with a variety of demand specifications and is exible enough to capture a wide range of heterogeneity structures. We validate our approach in a retail context and empirically demonstrate the model's ability at identifying high-value customers as well as those most sensitive to marketing actions, right after their first purchase.

A New Rough Set Model and its Application

2014 ◽  
Vol 599-601 ◽  
pp. 1350-1356
Author(s):  
Ming Ming Jia ◽  
Hai Qin Qin ◽  
Yong Qi Wang ◽  
Ke Jun Xu
Keyword(s):  
Rough Set ◽  
Discrete Variables ◽  
Water Pump ◽  
Variable Precision ◽  
Engineering Problems ◽  
Neighborhood Rough Set ◽  
Variable Precision Rough Set ◽  
Robust To Noise

A new neighborhood variable precision rough set modal is presented in this paper. The modal possesses the characteristics of neighborhood rough set and variable precision rough set, so it can overcome shortcomings of classic rough set which only be fit for discrete variables and sensitive to noise. Based on giving the definitions of approximate reduction, lower and upper approximate reduction, lower and upper distribution reduction, two kinds of algorithms to confirm lower and upper distribution reduction were advanced. The modal was applied to diagnose one frequency modulated water pump vibration faults. The result shows the modal is more suitable to engineering problems, because it can not only deal with continues variables but also be robust to noise.

Mitigation of Cold Start Problem in Experience-Based Adaptive Streaming over NDN

2021 ◽  
Author(s):  
Suphakit Awiphan ◽  
Jakramate Bootkrajang ◽  
Kanin Poobai ◽  
Jiro Katto
Keyword(s):  
Cold Start ◽  
Adaptive Streaming ◽  
Cold Start Problem
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