%0 Conference Paper %1 citeulike:14389365 %A Piao, Guangyuan %A Breslin, John G. %B Proceedings of the 28th ACM Conference on Hypertext and Social Media %C New York, NY, USA %D 2017 %I ACM %K dbpedia hypertext2017 interest user-modeling wikipedia %P 155--164 %R 10.1145/3078714.3078730 %T Leveraging Followee List Memberships for Inferring User Interests for Passive Users on Twitter %U http://dx.doi.org/10.1145/3078714.3078730 %X User modeling for inferring user interests from Online Social Networks (OSNs) such as Twitter has received great attention in the user modeling community with the growing popularity of OSNs. The focus of previous works has been on analyzing user-generated content such as tweets to infer user interests. Therefore, these previous studies were limited to active users who have been actively generating content. On the other hand, with the percentage of passive use of OSNs on the rise, some researchers investigated different types of information about followees (i.e., people that a user is following) such as tweets, usernames, and biographies to infer user interests for passive users who use OSNs for consuming information from followees but who do not produce any content. Although different types of information about followees have been exploited, list memberships (a topical list which other Twitter users can freely add a user into) of followees have not yet been investigated extensively for inferring user interests. In this paper, we investigate list memberships of followees, to infer interest profiles for passive users. To this end, we propose user modeling strategies with two different weighting schemes as well as a refined interest propagation strategy based on previous work. In addition, we investigate whether the information from biographies and list memberships of followees can complement each other, and thus improve the quality of inferred interest profiles for passive users. Results show that leveraging list memberships of followees is useful for inferring user interests when the number of followees is relatively small compared to using biographies of followees. In addition, we found that combining the two different types of information (list memberships and biographies) of followees can improve the quality of user interest profiles significantly compared to a state-of-art method in the context of link recommendations on Twitter. %@ 978-1-4503-4708-2

@inproceedings{citeulike:14389365, abstract = {{User modeling for inferring user interests from Online Social Networks (OSNs) such as Twitter has received great attention in the user modeling community with the growing popularity of OSNs. The focus of previous works has been on analyzing user-generated content such as tweets to infer user interests. Therefore, these previous studies were limited to active users who have been actively generating content. On the other hand, with the percentage of passive use of OSNs on the rise, some researchers investigated different types of information about followees (i.e., people that a user is following) such as tweets, usernames, and biographies to infer user interests for passive users who use OSNs for consuming information from followees but who do not produce any content. Although different types of information about followees have been exploited, list memberships (a topical list which other Twitter users can freely add a user into) of followees have not yet been investigated extensively for inferring user interests. In this paper, we investigate list memberships of followees, to infer interest profiles for passive users. To this end, we propose user modeling strategies with two different weighting schemes as well as a refined interest propagation strategy based on previous work. In addition, we investigate whether the information from biographies and list memberships of followees can complement each other, and thus improve the quality of inferred interest profiles for passive users. Results show that leveraging list memberships of followees is useful for inferring user interests when the number of followees is relatively small compared to using biographies of followees. In addition, we found that combining the two different types of information (list memberships and biographies) of followees can improve the quality of user interest profiles significantly compared to a state-of-art method in the context of link recommendations on Twitter.}}, added-at = {2017-11-15T17:02:25.000+0100}, address = {New York, NY, USA}, author = {Piao, Guangyuan and Breslin, John G.}, biburl = {https://www.bibsonomy.org/bibtex/22c75cffd2464ab983b50aa327d628966/brusilovsky}, booktitle = {Proceedings of the 28th ACM Conference on Hypertext and Social Media}, citeulike-article-id = {14389365}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=3078714.3078730}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/3078714.3078730}, doi = {10.1145/3078714.3078730}, interhash = {240eaf6b63ffce5ddfa35cce3fa44491}, intrahash = {2c75cffd2464ab983b50aa327d628966}, isbn = {978-1-4503-4708-2}, keywords = {dbpedia hypertext2017 interest user-modeling wikipedia}, location = {Prague, Czech Republic}, pages = {155--164}, posted-at = {2017-07-07 11:18:04}, priority = {2}, publisher = {ACM}, series = {HT '17}, timestamp = {2019-11-19T02:52:00.000+0100}, title = {{Leveraging Followee List Memberships for Inferring User Interests for Passive Users on Twitter}}, url = {http://dx.doi.org/10.1145/3078714.3078730}, year = 2017 }