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    FeedbackTrust: Using feedback effects in trust-based recommendation systems

    , Article 3rd ACM Conference on Recommender Systems, RecSys'09, New York, NY, 23 October 2009 through 25 October 2009 ; 2009 , Pages 269-272 ; 9781605584355 (ISBN) Moghaddam, S ; Jamali, M ; Ester, M ; Habibi, J ; Sharif University of Technology
    Abstract
    With the advent of online social networks, the trust-based approach to recommendation has emerged which exploits the trust network among users and makes recommendations based on the ratings of trusted users in the network. In this paper, we introduce a two dimensional trust model which dynamically gets updated based on users's feedbacks, in contrast to static trust values in current trust models. Explorability measures the extent to which a user can rely on recommendations returned by the social network of a trusted user. Dependability represents the extent to which a user's own ratings can be trusted by users trusting him directly and indirectly. We propose a method to learn the values of... 

    Hybrid learning approach toward situation recognition and handling

    , Article Computer Journal ; Volume 65, Issue 5 , 2022 , Pages 1293-1305 ; 00104620 (ISSN) Rajaby Faghihi, H ; Fazli, M ; Habibi, J ; Sharif University of Technology
    Oxford University Press  2022
    Abstract
    We propose a novel hybrid learning approach to gain situation awareness in smart environments by introducing a new situation identifier that combines an expert system and a machine learning approach. Traditionally, expert systems and machine learning approaches have been widely used independently to detect ongoing situations as the main functionality in smart environments in various domains. Expert systems lack the functionality to adapt the system to each user and are expensive to design based on each setting. On the other hand, machine learning approaches fail in the challenge of cold start and making explainable decisions. Using both of these approaches enables the system to use user's... 

    User adaptive clustering for large image databases

    , Article Proceedings - International Conference on Pattern Recognition, 23 August 2010 through 26 August 2010, Istanbul ; 2010 , Pages 4271-4274 ; 10514651 (ISSN) ; 9780769541099 (ISBN) Saboorian, M. M ; Jamzad, M ; Rabiee, H. R ; Sharif University of Technology
    2010
    Abstract
    Searching large image databases is a time consuming process when done manually. Current CBIR methods mostly rely on training data in specific domains. When source and domain of images are unknown, unsupervised methods provide better solutions. In this work, we use a hierarchical clustering scheme to group images in an unknown and large image database. In addition, the user should provide the current class assignment of a small number of images as a feedback to the system. The proposed method uses this feedback to guess the number of required clusters, and optimizes the weight vector in an iterative manner. In each step, after modification of the weight vector, the images are reclustered. We...