M.Phil / MS

Permanent URI for this collectionhttps://repository.cuilahore.edu.pk/handle/123456789/36

This collection archives the complete set of theses produced by students of the COMSATS University Islamabad, Lahore Campus.

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Author: search.filters.author.Dr. Hamid Turab MirzaSubject: search.filters.subject.fa17

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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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    A Study on Diversification of Online Product Reviews
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) Abid Ali; FA17-­RCS-­034; LHR TP 5982; Dr. Hamid Turab Mirza
    Recent studies have boosted the e¬commerce industry which has resulted in increased significance of online product reviews. However, this usefulness of product reviews has also attracted the people who try to manipulate overall product perception by generating fake reviews. Another challenge due to boost in e¬commerce is the information overload which is caused by generation of huge reviews data. This study paves a complete pathway by presenting techniques for removal of spam reviews and by proposing a novel algorithm to retrieve a diversified subset of reviews to reduce the burden of information overload. A diversified set of reviews attempts to cover maximum features of the selected product within a limited number of reviews that ultimately leads to reduction in decision time as well as enhances the credibility and reliability for the user. Spam detection techniques were formulated based on deep learning models whereas novel SENTIMENT AND FEATURE ORIENTED DIVERSIFICATION (SeFOD) algorithm was constructed on the features addressed in each review and the sentiments of the review separately. The proposed models showed prominent results and achieved a maximum spam accuracy of 95.78%, 96.38% and 96.18% for LSTM, GRU and CNN models re spectively. The same results were validated on Yelp hotel reviews dataset. Whereas a new measure for calculating the diversity of the reviews set was adopted named as DivScore. The score nearer to 0 means there is no diversity in the set and hence all the retrieved reviews contain similar features. The far this score goes from 0, the more diversity exists in the diversified set. A DivScore of 7.14 was achieved for the selected product from Daraz reviews dataset while 10.88 was the score when a product was diversified from Yelp reviews dataset. This study can be used by e¬commerce industry to maximize their profits as well as is equally relevant for the general users to better choose relevant product for them
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    User Interest Base Product Ranking Using Opinions’ Aspects
    (Library Information Services COMSATS University Islamaabad Lahore Campus, 2020) Ahtsham Shoukat; FA17-RCS-011; LHR TP 6401; Dr. Hamid Turab Mirza
    Due to Online shopping trend, a vast number of reviews are generated on daily basis. These reviews help users in selection of products or services. Most of the existing works utilize these reviews in different research areas. Ranking products using these reviews is one of them. Recent studies in the area of research did not focus ranking products on the basis of user specification needs. Previous studies did not focus on ranking products on the basis of user specification. Most of the times the negative polarity of aspects given by users in their reviews is neglected while ranking the products. The main contribution of this study is to take negative polarity of the aspects into consideration, to improve the ranking task. To rank products on the basis of user specification, this research considered both polarities (either positive or negative) of the aspects. In which firstly review is broken down into the sentences to cover the cases in which a review contains multiple senti mentsandvariousaspects. Thereafter aspects are extracted from the reviews of products given in the dataset and polarity of products is calculated on sentence level. Then the polarity is mapped with the aspects extracted from the specific sentence of the review. By using the weightage of the positive and negative polarities products are ranked ac cording to user specification needs. Experimental results show that users now possess the power change the weight of polarities and the resultant ranked list will be according to the user. It means if a user doesnotintendtoincludeaspecificpolarity, hemayignorethesamebeassigningweight 0 to that. This study helps users in selection of good products or services. The instant work is performed for English language reviews. This research opens a gate to the natives of different countries to enhance the work for their native languages
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