| Summary: | 碩士 === 國立臺北大學 === 資訊工程學系 === 104 === Social activities are the important parts of life for connecting and developing friendship. As online social platforms such as Facebook and Twitter get popular, more and more social applications have also been developed, which greatly facilitate making new friends and organizing activities online. Nevertheless, organizing activities is still time-consuming, since a host needs to search through all his or her friends to figure out whom to invite and suffers if he or she has hundreds or even thousands of friends online. Thus, in this thesis, we address a recommend problem name Guest Invitation list for Hosts (GIH). The object of GIH problem is to rank the friends of a host under the consideration of an activity and to recommend a proper guest invitation list for the host.
For the GIH problem, we develop a system consisting of training and recommendation phases. At the training phase, we collect activity of similar types to learn the associations between the activities and invited guest. To identify reliable associations, the collected activities will first be clustered according to the titles and descriptions of activities. After that, we establish a matrix to consider the relationship between the host and invited guests of an activity, and propose five matrix factorization methods, i.e., Naïve, NMF, Density, TFP and NMF+TFP, to derive the associations from the matrix. At the recommendation phase, a new activity is first categorized to the most similar activity group according to its title and descriptions. Then, the guest recommendation is performed based on the learned associations of that activity group. Experimental results show that NMF+TFP has the best performance. We also explore the effects of various parameters, investigate the performances of simple rules and complex rules, and perform case studies for three types of activities.
Key-word(s)：Activity, Host, Guest, Matrix factorization, Recommendation
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