Department of Computer Science

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Author: search.filters.author.Dr. Hamid Turab MirzaSubject: search.filters.subject.Information technology

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    Detection and Diversification of Genuine Online Reviews
    (Library Information Services, COMSATS University Islamabad, Lahore Campus., 2020-11-20) Mohammad Nabeel Tariq; SP16-BCS-194; Dr. Hamid Turab Mirza; LHR TP 6314
    We are grateful to the Almighty ALLAH who provides all the resources of every kind to us so that we make their proper use for the benefit of mankind. May He keep providing us with all the resources, and the guidance to keep helping humanity. We would not have contemplated this road if not for our parents, who instilled within us a love of creative pursuits, science, and language. To our parents, thank you. They have also been the best friends along this journey to encourage us to look forward to being more than just students. We would like to thank our project supervisor Dr. Hamid Turab Mirza for his guidance and encouragement to work hard and smart. We have found him very helpful while discussing the various issues that occurred during our project work. Lastly, many thanks go to the head of the project, Mr. Naveed Hussain who has invested his full effort in guiding us in achieving the goal, without him, this would not have been possible.
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    A POI Clustered Users Recommendation Method in LBSNs Considering the Weather Forecast
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Khurram Shahzad; FA19-RCS-023; Dr. Hamid Turab Mirza
    The Location-Based Social Network (LBSN) is one type of social web platform that allows users to register when they visit certain Points of Interest (POIs). The practice of recommending points of interest (POI) has become an important component of location-based social networks. However, due to the unique limits such as privacy concerns, accuracy, and reliability, and lack of comprehensive POI coverage of these networks, it remains a difficult challenge. In this thesis, data mining methods (i.e., model-based approach) are applied to handle the problem of the POI recommendation system. The approach is based on a particular method of analyzing geographic data. POI recommendations have a hard time (because of limited context information, credibility concerns, and lack of personalization) persuading users to visit the suggested destinations. The major purpose of this study is to categorize locations into different areas and to use the user group check-ins to train the model to forecast the weather using classification models. In this study, two datasets including Foursquare Check-Ins Tokyo Dataset, and Foursquare Check-Ins New York Dataset are used which contain the check-ins of different locations in New York and Tokyo from 2012-2016. The user’s check-in history is used in terms of timestamps, longitude, latitude, and user behavior to analyze the reliably forecast of the user’s location weather. After applying the data mining methods, the results demonstrate that for the New York check-in corpus, the decision tree method obtained the top performance with an accuracy of 0.74. Regarding the Tokyo check-in corpus, the overall performance indicates that the decision tree algorithm achieved the maximum accuracy of 0.84. The results concerning weather classification show that random forest achieved the best performance with an accuracy of 0.95
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    Understanding User Mobility Behaviour: A Study of Check-in Patterns in Location-Based Social Networks
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Sajeel Ur Rehman; FA17-RCS-014; LHR TP 7297; Dr. Hamid Turab Mirza
    Increasingly there is a tendency amongst social media users to ‘check-in’ i.e., an action allowing individuals to share their location at any given time, to places they physically visit through various social media applications like Facebook, Foursquare, etc. Statistics from these applications serve as a huge reservoir of user data which can be used to understand and predict the mobility behaviour of these users through formulation of a recommender system. The two basic methods for developing such a recommender system are memory-based and model-based system. Owing to pronounced limitations of the memory-based system such as the provision of a huge amount of data being essential for effective performance, this work develops a recommender system using model-based method. User mobility data of New York and Tokyo city (collected over 10 months) is obtained from Foursquare.com. This data is utilized to decipher the mobility behaviour of users: ‘checking-in’ to parks, hotels, recreational centres etc. This data only contains time stamps against the ‘checked-in’ locations. To develop a nuanced recommendation system, further parameters are added qualifying these ‘check-ins’ so that more personalized and precise recommendations are furthered. Firstly, the data is enriched by adding weather as an additional parameter. Secondly, Deep Neural Network” (DNN) is formed by embedding features which are then used to develop the DNN Model. Thirdly, model’s generalization is evaluated through Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Square Error (MSE). Based on these evaluations, after testing, it is concluded that MAE produced best result which is only 1.25 on the dataset and value for RMSE is 1.45 which is second best. Moreover, MSE results are not satisfactory being 2.11. It is anticipated that this recommender system will help local e-commerce and other non-e-commerce venues of the region to increase the visibility of their products or services sales by attracting customers through this developed model
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    Long and Short Term User Preference Learning for Next POI Recommendation
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Muhammad Umair Khalid; SP19-RCS-005; LHR TP 7299; Dr. Hamid Turab Mirza
    Recommendation systems in the latest years have play an essential part and it is becoming an emerging trend in daily life and business. The Point-of Interest (POI) - recommendation depends on the check-in histories of individual users at an explicit time. The goal is to develop a comprehensive recommendation system, which has the aptitude to learn the user long and short-term preference for the next POI recommendation and is a considerable attractive research interest. Since users check-in activities are independent and indefinable and can be viewed as long and short term sequence, which equally determine the user’s preference for the next destination. Moreover, the previous methods focuses on the geographical relationship of users among recently visited (POIs) and neglect the user's general interest. To addresses, the issue of (POI)-recommendation, the comprehensive model is given for learning user long and short term preferences. Which considers both recent successive information of users and their general taste simultaneously. The long term module utilizes fusion technique to capture the user's next (POI) preference, by computing the historical visit frequency of users to a particular venue representing their general interest. Whereas the short term preference module utilizes an attention mechanism, to learn the users’ recently visited locations at a specific time and location using spatio temporal based attention model. The given model in this thesis has shown prominent results with an accuracy of 57.7% at N=30 for cold-start-users and 60.7% for Neural network and Random forest respectively. However these results is improved using on non-cold-start users to 79.1% and 77.9% at N=30. Besides the existing methods which neglect the user’s long-term behavior. This work targets to achieve improvement by using fusion model on real-word check-in information and access the model performance in terms of non-cold start and cold-start users which will overcome data sparseness problem in user check-ins, to achieve effective results, and will help in different applications of recommender system
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    Group Spam identification in Online Product Reviews
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) GHULAM MEHMOOD; SP17-RCS-017; LHR TP 6400; Dr. Hamid Turab Mirza
    In this era of e-commerce, user’s opinion about a product on the online review system is of great importance, as it provides guidance for people to decide. Being very important, people used to write fake reviews about the products, called opinion spamming. Detecting opinion spams in online review platforms is a challenging task drawing attention from research communities. It is a persistent campaign between the spammers and platforms. Grouped opinion spamming is the main type of opinion spamming in the online review system these days. For that purpose, usually people make multiple accounts to write fake reviews or they pay to crowdsourcing platforms to write fake reviews for promotion of their product or demotion of their competitor’s product. Group spam reviews are more damaging for online review systems as reviews from many peoples about a product either it is positive, or negative can easily deceive peoples as compared to a single spam review. These spam review groups should be detected so that reviews about that product reflect genuine user opinion. Many researchers try to resolve this problem using behavioral and linguistic features of the users and reviews. Many machine learning models are being adapted to solve this problem but could not resolve this problem completely. The purpose of this research work is to design a framework that detect group spammer who targets online review systems. This framework has used linguistic, behavioral, and structural features to dig out all such spammer groups who write fake reviews. First, constructed a reviewer-product network between reviewers and products. Then written an algorithm to find out collusiveness score between reviewers who have commonly reviewed a product and constructed a network between reviewers. Then all those edges (reviewer pairs) whose score was below a threshold valued was removed from the network. After that extracted high quality candidate spammer groups by using a novel algorithm. Five best group spam indicators are used to calculate spamicity score of candidate spammer groups. The candidate groups whose spamicity score was greater than a threshold values (says 0.6) was considered as spammer groups. For the evaluation of proposed algorithm, experiments performed on 3 labeled datasets from Yelp. The system extracted spammer groups from online reviews with precision of 0.89 @ top 50 spammer groups.