A Cluster-based Recommender System

Introduction: E-commerce is growing rapidly offering a vast number of products and services to the users. Facing with a wide range of options, users cannot decide which one would be the most suitable option. Recommender systems help users to find the most suitable item easier and faster. To do this, recommender systems apply machine learning algorithms to user’s data to build sophisticated models to predict the user’s behavior in the future. There are many recommender systems employed by companies to increase their profitability. Some examples include Amazon, Movielens, Youtube, Facebook, and Linkedin. Objectives: The aim of this project is to provide a cluster-based recommender system which cluster users based on their history (previous interactions with the system) to increase the accuracy of recommendations. Method: The proposed approach consists of two phases: offline and online. In the offline phase, users are clustered using genetic algorithm. In the online phase, the appropriate cluster or clusters and neighborhood are selected for the target user. Then, his/her interesting items (not chosen yet) are determined using interesting items of his/her neighbors. Results: After implementing the proposed approach for the recommender system, it was evaluated in terms of accuracy (the portion of recommended items which have been interesting for the users) and compared it with several existing recommender systems. The results show that our approach outperforms other approaches. Conclusions: Having a good recommender system encourages users to buy new products, find new friends, or watch new videos. On the contrary, an inaccurate recommender system may discourage the users and motivates them to sign out of the system or ignore all recommendations. The approach we proposed for recommendation achieved promising results. We hope by completing the project we can use this approach in developing commercial recommender systems

Proposing a hybrid approach for emotion classification using audio and video data

Emotion recognition has been a research topic in the field of Human-Computer Interaction (HCI) during recent years. Computers have become an inseparable part of human life. Users need human-like interaction to better communicate with computers. Many researchers have become interested in emotion recognition and classification using different sources. A hybrid approach of audio and text has been recently introduced. All such approaches have been done to raise the accuracy and appropriateness of emotion classification. In this study, a hybrid approach of audio and video has been applied for emotion recognition. The innovation of this approach is selecting the characteristics of audio and video and their features as a unique specification for classification. In this research, the SVM method has been used for classifying the data in the SAVEE database. The experimental results show the maximum classification accuracy for audio data is 91.63% while by applying the hybrid approach the accuracy achieved is 99.26%

Minimizing Synchronization in Parallel Nested Loops

Although, computer system architecture and the throughput enhances continuously, the need for high computational speed and power in many scientific applications grows every day. As a result, implementation of parallel applications has gained more attention. Since nested loops are the most time-consuming parts of most programs, we propose a method for scheduling uniform nested loops to processors based on the equation of a straight line which includes the maximum possible number of dependence vectors. Experimental results show that the proposed method imposes a lower communication between processors compared with similar methods

Recommender Systems in ECommerce

E-commerce is growing rapidly offering a vast number of products and services to the users. Facing with a wide range of options, users cannot decide which one would be the most suitable option. Recommender systems help users to find the most suitable item easier and faster. To do this, recommender systems apply machine learning algorithms to user’s data to build sophisticated models to predict the user’s behavior in the future. There are many recommender systems employed by companies to increase their profitability. Some examples include Amazon, Movielens, Youtube, Facebook, and Linkedin. This presentation details the implementation of a cluster based recommender system that can accurately recommend items to users

A New Framework for Tackling Combinatorial Optimization Problems

A difficult class of problems is the class of combinatorial optimization problems. This is because the search space of such problems is often grown exponentially when the size of the problem grows. There are some known solving techniques to tackle such problems of which the most popular ones are: mathematical methods, constraint programming and local search. Each technique has its own advantages and disadvantages and for a given problem it is unclear at the beginning which technique gives us the best result. In this paper, we explain our experiments in designing the modeling language Zinc. Zinc is a high-level modeling language which supports a methodology in which each model can be automatically mapped into corresponding low level model suitable for one of the afore mentioned solving techniques

Detecting False Positive Interactions in Protein-Protein Interaction Data

High-throughput experimental techniques, such as mass spectrometry and yeast two-hybrid assay, provided an extensive list of protein-protein interactions (PPIs) that control the biological processes in for various organisms. Although these experimental approaches produce crucially valuable data, they sufferfrom high false positives results.In this paper, we proposed a new method to detect false positive interactions. Our method usesinteraction generality andK-path ratio as two topological features of the protein-protein interaction networks, which extracted from high quality data, to detect false positive interactions. The results reveal that proposed method can effectively detect false positive interactions in highthroughput PPI data

Considering Faults in Service-Oriented Architecture: A Graph Transformation-Based Approach

Nowadays, using Service-Oriented Architectures (SOA) is spreading as a flexible architecture for developing dynamic enterprise systems. Due to the increasing need of high quality services in SOA, it is desirable to consider different Quality of Service (QoS) aspects in this architecture as security, availability, reliability, fault tolerance, etc. In this paper we investigate fault tolerance mechanisms for modeling services in service-oriented architecture. We propose a metamodel (formalized by a type graph) and some graph rules for monitoring services and their communications to detect faults. By defining additional graph rules as reconfiguration mechanisms, service requesters can be dynamically switched to a new service (with similar descriptions). To validate our proposal, we use our previous approach to model checking graph transformation using the Bogor model checker

Deadline-constrained workflow scheduling using imperialist competitive algorithm on infrastructure as a service clouds

Cloud computing is internet based computing paradigm that opens new opportunities for researchers to investigate its benefits and disadvantages on executing scientific applications such as workflows. Workflow scheduling on distributed systems has been widely studied over the years. Most of the proposed scheduling algorithms attempt to minimize the execution time without considering the cost of accessing resources and mostly target environments similar or equal to community Grids. But, in case of Cloud computing, usually, faster resources are more expensive than the slower one such that execution time as well as cost incurred by using a set of heterogeneous resources over cloud should be minimized. The proposed approach in this paper is based on Imperialist Competitive Algorithm (ICA). The ICA is a new evolutionary algorithm which is inspired by human's socio-political evolution. Generally this algorithm mathematically models the imperialism as a level of human's social evolution and uses this model for optimization Problems. In this paper, we develop a static cost-minimization, deadline-constrained heuristic for scheduling a scientific workflow application in a Cloud environment. Our approach considers fundamental features of IaaS providers such as on-demand resource provisioning and unlimited computing resources. The results show that our approach performs better than the PSO algorithm in terms of cost minimization and percent of meeting deadline

Proposing a New Search Template for Modelling Languages

The major problem in solving combinatorial optimization problems is the huge size of the search space. To explore the search space in a reasonable time, using smart search algorithms is inevitable. One of the main difficulties in implementing search methods is the lack of a uniform, high-level template for all search paradigms. In this paper, we propose a high-level, parametric template suitable for modeling languages which covers both tree search and local search

An adaptive approach to dealing with unstable behaviour of users in collaborative filtering systems

Recommendation systems manage information overload in order to present personalized content to users based on their interests. One of the most efficient recommendation approaches is collaborative filtering, through which recommendation is based on previously rated data. Collaborative filtering techniques feature impressive solutions for suggesting favourite items to certain users. However, recommendation methods fail to reflect fluctuations in users’ behaviour over time. In this article, we propose an adaptive collaborative filtering algorithm which takes time into account when predicting users’ behaviour. The transitive relationship from one user to another is considered when computing the similarity of two different users. We predict variations of users’ preferences using their profiles. Our experimental results show that the proposed algorithm is more accurate than the classical collaborative filtering technique