Relational Collaborative Topic Regression for Recommender Systems

Relational Collaborative Topic Regression for Recommender Systems

Abstract:

Due to its successful application in recommender systems, collaborative filtering (CF) has become a hot research topic in data mining and information retrieval. In traditional CF methods, only the feedback matrix, which contains either explicit feedback (also called ratings) or implicit feedback on the items given by users, is used for training and prediction. Typically, the feedback matrix is sparse, which means that most users interact with few items. Due to this sparsity problem, traditional CF with only feedback information will suffer from unsatisfactory performance. Recently, many researchers have proposed to utilize auxiliary information, such as item content (attributes), to alleviate the data sparsity problem in CF. Collaborative topic regression (CTR) is one of these methods which has achieved promising performance by successfully integrating both feedback information and item content information. In many real applications, besides the feedback and item content information, there may exist

relations (also known as networks) among the items which can be helpful

for recommendation. In this paper, we develop a novel hierarchical Bayesian model called Relational Collaborative Topic Regression (RCTR), which extends CTR by seamlessly integrating the user-item feedback information, item content information, and network structure among items

into the same model. Experiments on real-world datasets show that our

model can achieve better prediction accuracy than the state-of-the-art methods with lower empirical training time. Moreover, RCTR can learn good interpretable latent structures which are useful for recommendation.

Existing System:

In most traditional CF methods, only the feedback matrix, which contains either explicit feedback (also called ratings) or implicit feedback on the items given by users, is used for training and prediction. Typically, the feedback matrix is sparse, which means that most items are given feedback by few users or most users only give feedback to few items. Due to this sparsity problem, traditional CF with only feedback information will suffer from unsatisfactory performance.

Proposed System:

We develop a novel hierarchical Bayesian model, called Relational Collaborative Topic Regression (RCTR), to incorporate item relations for recommendation.

By extending CTR, RCTR seamlessly integrates the user-item feedback information, item content information and relational (network) structure among items into a principled hierarchical Bayesian model.

Even if a new item has been given feedback only by one or two users, RCTR can make effective use of the information from the item network to alleviate the data sparsity problem in CF, which will consequently improve the recommendation accuracy.

Hardware Requirements:

•       System              : Pentium IV 2.4 GHz.

•       Hard Disk        : 40 GB.

•       Floppy Drive   : 1.44 Mb.

•       Monitor            : 15 VGA Colour.

•       Mouse               : Logitech.

•       RAM                 : 256 Mb.

Software Requirements:

•       Operating system    : - Windows XP.

•       Front End                  : - JSP

•       Back End                   : - SQL Server

Software Requirements:

•       Operating system    : - Windows XP.

•       Front End                  : - .Net

•       Back End                   : - SQL Server