BPRS: Belief Propagation Based Iterative Recommender System

In this paper we introduce the first application of the Belief Propagation (BP) algorithm in the design of recommender systems. We formulate the recommendation problem as an inference problem and aim to compute the marginal probability distributions of the variables which represent the ratings to be predicted. However, computing these marginal probability functions is computationally prohibitive for large-scale systems. Therefore, we utilize the BP algorithm to efficiently compute these functions. Recommendations for each active user are then iteratively computed by probabilistic message passing. As opposed to the previous recommender algorithms, BPRS does not require solving the recommendation problem for all the users if it wishes to update the recommendations for only a single active. Further, BPRS computes the recommendations for each user with linear complexity and without requiring a training period. Via computer simulations (using the 100K MovieLens dataset), we verify that BPRS iteratively reduces the error in the predicted ratings of the users until it converges. Finally, we confirm that BPRS is comparable to the state of art methods such as Correlation-based neighborhood model (CorNgbr) and Singular Value Decomposition (SVD) in terms of rating and precision accuracy. Therefore, we believe that the BP-based recommendation algorithm is a new promising approach which offers a significant advantage on scalability while providing competitive accuracy for the recommender systems

A Fuzzy-Based Inference Mechanism of Trust for Improved Social Recommenders

This paper presents a stochastic model based on Monte Carlo simulation techniques for measuring the performance of recommenders. A general procedure to assess the accuracy of recommendation predictions is presented and implemented in a typical case study where input parameters are treated as random values and recommender errors are estimated using sensitive analysis. The results obtained are presented and a new perspective to the evaluation and assessment of recommender systems is discussed

Trust-Networks in Recommender Systems

Similarity-based recommender systems suffer from significant limitations, such as data sparseness and scalability. The goal of this research is to improve recommender systems by incorporating the social concepts of trust and reputation. By introducing a trust model we can improve the quality and accuracy of the recommended items. Three trust-based recommendation strategies are presented and evaluated against the popular MovieLens [8] dataset

Evaluation and Assessment of Recommenders Using Monte Carlo Simulation

There have been various definitions, representations and derivations of trust in the context of recommender systems. This article presents a recommender predictive model based on collaborative filtering techniques that incorporate a fuzzy-driven quantifier, which includes two upmost relevant social phenomena parameters to address the vagueness inherent in the assessment of trust in social networks relationships. An experimental evaluation procedure utilizing a case study is conducted to analyze the overall predictive accuracy. These results show that the proposed methodology improves the performance of classical recommender approaches. Possible extensions are then outlined

Preference Learning

This report documents the program and the outcomes of Dagstuhl Seminar 14101 “Preference Learning”. Preferences have recently received considerable attention in disciplines such as machine learning, knowledge discovery, information retrieval, statistics, social choice theory, multiple criteria decision making, decision under risk and uncertainty, operations research, and others. The motivation for this seminar was to showcase recent progress in these different areas with the goal of working towards a common basis of understanding, which should help to facilitate future synergies

Online Shopping Decisions Enhancement with Fuzzy Expert System

Purpose Nowadays, due to the rapid development of the Internet and the rapid growth of web pages, many electronic websites are using product recommendation systems to guide users to the products that they need. Such systems usually provide a list of suggested items that the user may prefer. These systems are provided as a support tool to help users obtain information that best meets their needs. These systems can actually improve user decisions, resulting in increased sales and mutual customer satisfaction. The purpose of the paper is to improve user decisions in online shopping using fuzzy expert system. Methodology: The statistical population of this study consists of 30 experts in the field of e-commerce who were selected by combining two methods of deliberate sampling and snowball sampling. To analyze the status of improvement of users' decisions, a fuzzy expert system was created using input variables business reputation status, environmental factors status in e-commerce, online store features; product specifications; user/customer characteristics. Findings: The final results showed that there is no significant difference between the results of the created expert system and the mean of expert opinions. Originality/Value: In this paper, a conceptual Model to improve user decisions in online shopping using a fuzzy expert system is designed

A system of serial computation for classified rules prediction in non-regular ontology trees

Objects or structures that are regular take uniform dimensions. Based on the concepts of regular models, our previous research work has developed a system of a regular ontology that models learning structures in a multiagent system for uniform pre-assessments in a learning environment. This regular ontology has led to the modelling of a classified rules learning algorithm that predicts the actual number of rules needed for inductive learning processes and decision making in a multiagent system. But not all processes or models are regular. Thus this paper presents a system of polynomial equation that can estimate and predict the required number of rules of a non-regular ontology model given some defined parameters