Theses and Dissertations at Montana State University (MSU)
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/733
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Item Exploring timeliness for accurate location recommendation on location-based social networks(Montana State University - Bozeman, College of Engineering, 2017) Xu, Yi; Chairperson, Graduate Committee: Qing YangAn individual's location history in the real world implies his or her interests and behaviors. Accordingly, people who share similar location histories are likely to have common interest and behavior. This thesis analyzes and understands the process of Collaborative Filtering (CF) approach, which mines an individual's preference from his/her geographic location histories and recommends locations based on the similarities between the user and others. We find that a CF-based recommendation process can be summarized as a sequence of multiplications between a transition matrix and visited-location matrix. The transition matrix is usually approximated by the user's interest matrix that reflect the similarity among users, regarding to their interest in visiting different locations. The visited-location matrix provides the history of visited locations of all users, which is currently available to the recommendation system. We find that recommendation results will converge if and only if the transition matrix remains unchanged; otherwise, the recommendations will be valid for only a certain period of time. Based on our analysis, a novel location-based accurate recommendation (LAR) method is proposed, which considers the semantic meaning and category information of locations, as well as the timeliness of recommending results, to make accurate recommendations. We evaluated the precision and recall rates of LAR, using a large-scale real-world data set collected from Brightkite. Evaluation results confirm that LAR offers more accurate recommendations, comparing to the state-of-art approaches.Item Trust assessment in online social networks(Montana State University - Bozeman, College of Engineering, 2017) Liu, Guangchi; Chairperson, Graduate Committee: Qing YangAssessing trust in online social networks (OSNs) is critical for many applications such as online marketing and network security. It is a challenging problem, however, due to the difficulties of handling complex social network topologies and conducting accurate assessment in these topologies. To address these challenges, we model trust by proposing the three-valued subjective logic (3VSL) model. 3VSL properly models the uncertainties that exist in trust, thus is able to compute trust in arbitrary graphs. We theoretically prove the capability of 3VSL based on the Dirichlet-Categorical (DC) distribution and its correctness in arbitrary OSN topologies. Based on the 3VSL model, we further design the AssessTrust (AT) algorithm to accurately compute the trust between any two users connected in an OSN. AT is able to accurately conduct one-to-one trustworthiness, however, it is inefficient in addressing the massive trust assessment (MTA) problem, i.e., computing one-to-many trustworthiness in OSNs. MTA plays a vital role in OSNs, e.g., identifying trustworthy opinions in a crowdsourcing system. If the AssessTrust algorithm is applied directly to solve the MTA problem, its time complexity is exponential. To efficiently address MTA, we propose the OpinionWalk algorithm that yields an polynomial-time complexity. OpinionWalk uses a matrix to represent a social network's topology and a vector to store the trustworthiness of all users in the network. The vector is iteratively updated when the algorithm 'walks' through the entire network. To validate the 3VSL model, we first conduct a numerical analysis. An online survey system is then implemented to validate the correctness and accuracy of 3VSL in the real world. Finally, we validate 3VSL against two real-world OSN datasets: Advogato and Pretty Good Privacy (PGP). Experimental results indicate that 3VSL can accurately model the trust between any pair of indirectly connected users in the Advogato and PGP. To evaluate the performance of the AssessTrust and OpinionWalk algorithms, we use the same datasets. Compared to the state-of-art solutions, e.g., EigenTrust and MoleTrust, OpinionWalk yields the same order of time complexity and a higher accuracy in trust assessment.