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Monday, 4 June 2018

RAPARE: A Generic Strategy for Cold-Start Rating Prediction Problem

RAPARE: A Generic Strategy for Cold-Start Rating Prediction Problem

In recent years, recommender system is one of indispensable components in many e-commerce websites. One of the major challenges that largely remains open is the cold-start problem, which can be viewed as a barrier that keeps the cold-start users/items away from the existing ones. In this paper, we aim to break through this barrier for cold-start users/items by the assistance of existing ones. In particular, inspired by the classic Elo Rating System, which has been widely adopted in chess tournaments; we propose a novel rating comparison strategy (RAPARE) to learn the latent profiles of cold-start users/items. The center-piece of our RAPARE is to provide a fine-grained calibration on the latent profiles of cold-start users/items by exploring the differences between cold-start and existing users/items. As a generic strategy, our proposed strategy can be instantiated into existing methods in recommender systems. To reveal the capability of RAPARE strategy, we instantiate our strategy on two prevalent methods in recommender systems, i.e., the matrix factorization based and neighborhood based collaborative filtering. Experimental evaluations on five real data sets validate the superiority of our approach over the existing methods in cold-start scenario.
  • Despite the success of existing recommender systems all over the world, the cold-start problem, i.e., how to make proper recommendations for cold-start users or coldstart items, largely remains a daunting dilemma. On one hand, cold-start users (e.g., who have rated no more than 10 items) and cold-start items (e.g., which have received no more than 10 ratings) occupy a large proportion in many real applications such as Netflix.
  • On the other hand, the effectiveness of the existing recommendation approaches (e.g., collaborative filtering) largely depends on the sufficient amount of historical ratings, and hence these approaches might quickly become ineffective for cold-start users/items that only have few ratings.
  • To date, many collaborative filtering methods have been proposed to mitigate the cold-start problem, and these efforts can be divided into three classes. In the first class, a well designed interview process is introduced for cold- start users. During this interview process, a set of items are provided for the cold-start users to express their opinions
  • Methods in the second class resort to side information such as the user/item attributes and social relationships for the cold-start problem.
  • In the third class, the coldstart problem is tackled in a dynamic manner. The intuition is that, compared to existing users/items, ratings for coldstart users/items may be more valuable to improve the accuracy of recommendation for these cold-start users/items; consequently, methods in this class aim to provide fast recommendations for cold-start users/items specifically, and then dynamically and efficiently adjust their latent profiles as they give/receive new ratings.
  • The main disadvantage of methods in this class is the additional burdens incurred by the interview process.
  • They rely on the access of such side information. These methods are inapplicable when the information is not available due to some reasons (e.g., privacy issue, user’s social network structure not existing), and has a higher computational cost compared with its side information free counterpart.
  • Methods in the third class cannot serve users with no rating in the recommender system.
  • In particular, we make the following analogy, i.e., to view the cold-start problem as a barrier between the cold-start users/items and the existing ones, and such a barrier could be broken with the assistance of existing users/items. To this end, we propose a novel rating comparison strategy (RAPARE) which can calibrate the latent profiles for coldstart users/items. Take cold-start user as an example, when a cold-start user gives a rating on an item, we first compare this rating with the existing ratings (which are from existing users) on this item. Then, we adjust the profile of the coldstart user based on the outcomes of the comparisons.
  • Our rating comparison strategy (RAPARE) is inspired by the Elo Rating System which has been widely used to calculate players’ ratings in many different types of match systems
  • We propose a novel and generic rating comparison strategy RAPARE to serve for the cold-start problem. We formulate the strategy as an optimization problem. The key idea of RAPARE is to exploit the knowledge from existing users/items to help calibrate the latent profiles of cold-start users/items.
  • We instantiate the proposed generic RAPARE strategy on both matrix factorization based (RAPARE-MF) and neighborhood based (RAPARE-KNN) collaborative filtering, together with algorithms to solve them.
  • We present the algorithm analysis for RAPARE strategy and its instantiations on aspects of effectiveness and efficiency.
  • We conduct extensive experimental evaluations on five real data sets, showing that our approach (1) outperforms several benchmark collaborative filtering methods and online updating methods in terms of prediction accuracy for cold-start scenario; (2) earns better quality-speed balance while enjoying a linear scalability.
  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB
  • Operating system : Windows 7.
  • Coding Language : JAVA/J2EE
  • Tool : Netbeans 7.2.1
  • Database : MYSQL
Jingwei Xu, Yuan Yao, Hanghang Tong, Xianping Tao, Jian Lu, “RAPARE: A Generic Strategy for Cold-Start Rating Prediction Problem”, IEEE Transactions on Knowledge and Data Engineering, 2017.

Project Cost : 10000
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Mumbai Academics | Airoli 
8097636691 (Gaurav Sir)[Project Manager]
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