Publications

Predicting Users’ Future Interests on Twitter

Fattane Zarrinkalam and Hossein Fani and Ebrahim Bagheri and Mohsen Kahani
Reference:
Links to Publication: [doi][www][pdf]
Abstract:
In this paper, we address the problem of predicting future interests of users with regards to a set of unobserved topics in microblogging services which enables forward planning based on potential future interests. Existing works in the literature that operate based on a known interest space cannot be directly applied to solve this problem. Such methods require at least a minimum user interaction with the topic to perform prediction. To tackle this problem, we integrate the semantic information derived from the Wikipedia category structure and the temporal evolution of user’s interests into our prediction model. More specifically, to capture the temporal behaviour of the topics and user’s interests, we consider discrete intervals and build user’s topic profile in each time interval separately. Then, we generalize users’ interests that have been observed over several time intervals by transferring them over the Wikipedia category structure. Our approach not only allows us to generalize users’ interests but also enables us to transfer users’ interests across different time intervals that do not necessarily have the same set of topics. Our experiments illustrate the superiority of our model compared to the state of the art.
Bibtex Entry:
@inproceedings{DBLP:conf/ecir/ZarrinkalamFBK17, author = {Fattane Zarrinkalam and Hossein Fani and Ebrahim Bagheri and Mohsen Kahani}, title = {Predicting Users’ Future Interests on Twitter}, booktitle = {Advances in Information Retrieval: 39th European Conference on IR Research, ECIR 2017, Aberdeen, UK, April 8-13, 2017, Proceedings}, pages = {464--476}, year = {2017}, crossref = {DBLP:conf/ecir/2017}, url = {https://link.springer.com/chapter/10.1007%2F978-3-319-56608-5_36}, doi = {10.1007/978-3-319-56608-5_36}, webpdf = {http://ls3.rnet.ryerson.ca/papers/Predicting_Users'_Future_Interests_on_Twitter-ecir2017.pdf}, abstract = {In this paper, we address the problem of predicting future interests of users with regards to a set of unobserved topics in microblogging services which enables forward planning based on potential future interests. Existing works in the literature that operate based on a known interest space cannot be directly applied to solve this problem. Such methods require at least a minimum user interaction with the topic to perform prediction. To tackle this problem, we integrate the semantic information derived from the Wikipedia category structure and the temporal evolution of user’s interests into our prediction model. More specifically, to capture the temporal behaviour of the topics and user’s interests, we consider discrete intervals and build user’s topic profile in each time interval separately. Then, we generalize users’ interests that have been observed over several time intervals by transferring them over the Wikipedia category structure. Our approach not only allows us to generalize users’ interests but also enables us to transfer users’ interests across different time intervals that do not necessarily have the same set of topics. Our experiments illustrate the superiority of our model compared to the state of the art.} }




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