Bayesian Networks for Collaborative Filtering

Proceedings of The 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR (IC3K-2015), pages 475-480

Collaborative filtering (CF) is one of the most popular algorithms, for recommendation in cases, the items which are recommended to users, have been determined by relying on the outcomes done on surveying their communities. There are two main CF-approaches, which are memory-based and model-based. The model-based approach is more dominant by real-time response when it takes advantage of inference mechanism in recommendation task. However the problem of incomplete data is still an open research and the inference engine is being improved more and more so as to gain high accuracy and high speed. I propose a new model-based CF based on applying Bayesian network (BN) into reference engine with assertion that BN is an optimal inference model because BN is user’s purchase pattern and Bayesian inference is evidence-based inferring mechanism which is appropriate to rating database. Because the quality of BN relies on the completion of training data, it gets low if training data have a lot of missing values. So I also suggest an average technique to fill in missing values. Keywords: Collaborative Filtering, Bayesian Network.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008

This paper presents a model designed under the formalism of Bayesian Networks to deal with the problem of collaborative recommendation. It has been designed to perform efficient and effective recommendations. We also consider the fact that the user can usually use vague ratings for the products, which might be represented as fuzzy labels. The complete proposal is evaluated with MovieLens.

International Journal on Artificial Intelligence Tools, 2008

As one of the most successful recommender systems, collaborative filtering (CF) algorithms are required to deal with high sparsity and high requirement of scalability amongst other challenges. Bayesian networks (BNs), one of the most frequently used classifiers, can be used for CF tasks. Previous works on applying BNs to CF tasks were mainly focused on binary-class data, and used simple or basic Bayesian classifiers.1,2 In this work, we apply advanced BNs models to CF tasks instead of simple ones, and work on real-world multi-class CF data instead of synthetic binary-class data. Empirical results show that with their ability to deal with incomplete data, the extended logistic regression on tree augmented naïve Bayes (TAN-ELR)3 CF model consistently performs better than the traditional Pearson correlation-based CF algorithm for the rating data that have few items or high missing rates. In addition, the ELR-optimized BNs CF models are robust in terms of the ability to make predictions...

2007

Recommender systems are important to help users select relevant and personalised information over massive amounts of data available. We propose an unified framework called Preference Network (PN) that jointly models various types of domain knowledge for the task of recommendation. The PN is a probabilistic model that systematically combines both content-based filtering and collaborative filtering into a single conditional Markov random field. Once estimated, it serves as a probabilistic database that supports various useful queries such as rating prediction and top-N recommendation. To handle the challenging problem of learning large networks of users and items, we employ a simple but effective pseudo-likelihood with regularisation. Experiments on the movie rating data demonstrate the merits of the PN.

2007 IEEE 23rd International Conference on Data Engineering Workshop, 2007

The problem of building Recommender Systems has attracted considerable attention in recent years, but most recommender systems are designed for recommending items for individuals. The aim of this paper is to automatically recommend and rank a list of new items to a group of users. The proposed model can be considered as a collaborative Bayesian network-based group recommender system, where the group's rates are computed from past voting patterns of other users with similar tastes. The use of Bayesian networks allows us to obtain an intuitive representation of the mechanisms that govern the relationships between the group members.

1999

Abstract Recent projects in collaborative filtering and information filtering address the task of inferring user preference relationships for products or information. The data on which these inferences are based typically consists of pairs of people and items. The items may be information sources (such as web pages or newspaper articles) or products (such as books, software, movies or CDs). We are interested in making recommendations or predictions.

International Journal of Advance Engineering and Research Development, 2015

Recently, it has become more and more difficult for the existing web based systems to locate or retrieve any kind of relevant information, due to the rapid growth of the World Wide Web in terms of the information space and the amount of the users in that space. However, in today's world, many systems and approaches make it possible for the users to be guided by the recommendations that they provide about new items such as articles, news, books, music, and movies. However, a lot of traditional recommender systems result in failure when the data to be used throughout the recommendation process is sparse. This Paper focuses on the development and evaluation of a web based movie recommendation system.

2010 World Congress …, 2010

The World Wide Web has created a new media for mass marketing that can also be highly customized to online customers’ needs and expectations. Recommender Systems (RS) play an important role in this area. Here, we aim to establish a genre-based collaborative RS to automatically suggest and rank a list of appropriate items (movies) to a user based on the user profile and the past voting patterns of other users with similar tastes. The contribution of this paper is using genre based information in a hybrid fuzzy-Bayesian network collaborative RS. The interest to the different genres is computed based on a hybrid user model. The similarity of likeminded users according to the fuzzy distance and also Pearson correlation coefficient is involved in a Bayesian network.

Communications in Statistics: Case Studies, Data Analysis and Applications, 2017

In this article, we provide a formulation of empirical Bayes Atchadé to tune the hyperparameters of priors used in Bayesian set-up of collaborative filter. We implement the same in a MovieLens small dataset. We see that it can be used to get a good initial choice for the parameters. It can also be used to guess an initial choice for hyperparameters in grid search procedure even for the datasets where MCMC oscillates around the true value or takes a long time to converge.

2004

This paper applies Probabilistic Relational Models (PRMs) to the Collaborative Filtering task, focussing on the EachMovie data set. We first learn a standard PRM, and show that its performance is competitive with the best known techniques. We then define hierarchical PRMs, which extend standard PRMs by dynamically refining classes into hierarchies. This represnetation is more expressive that standard PRMs, and allows greater context sensitivity. Finally, we show that hierarchical PRMs achieve stateof-the-art results on this dataset.