GroupRank: Ranking Online Social Groups Based on User Membership Records | ||
| Journal of AI and Data Mining | ||
| دوره 9، شماره 1، فروردین 2021، صفحه 45-57 اصل مقاله (1.44 M) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2020.8337.1973 | ||
| نویسندگان | ||
| A. Hashemi؛ M. A. Zare Chahooki* | ||
| Software Engineering Department, Yazd University, Daneshgah Street, Yazd, Yazd, Iran | ||
| چکیده | ||
| Social networks are valuable sources for marketers. Marketers can publish campaigns to reach target audiences according to their interest. Although Telegram was primarily designed as an instant messenger, it is used as a social network in Iran due to censorship of Facebook, Twitter, etc. Telegram neither provides a marketing platform nor the possibility to search among groups. It is difficult for marketers to find target audience groups in Telegram, hence we developed a system to fill the gap. Marketers use our system to find target audience groups by keyword search. Our system has to search and rank groups as relevant as possible to the search query. This paper proposes a method called GroupRank to improve the ranking of group searching. GroupRank elicits associative connections among groups based on membership records they have in common. After detailed analysis, five-group quality factors have been introduced and used in the ranking. Our proposed method combines TF-IDF scoring with group quality scores and associative connections among groups. Experimental results show improvement in many different queries. | ||
| کلیدواژهها | ||
| social networks؛ instant messenger؛ search engine؛ ranking؛ Telegram | ||
| مراجع | ||
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[1] A. Eugene, E. Brill, and S. Dumais, "Improving web search ranking by incorporating user behavior information." in Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, ACM, 2006, pp. 19-26. [2] B. Michael, X. Wang, D. Metzler, and M. Najork, "Learning from user interactions in personal search via attribute parameterization." in Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, ACM, 2017, pp. 791-799. [3] E. Fredrik, P. Bródka, A. Borg, and H. Johnson, "Finding influential users in social media using association rule learning." Entropy vol. 18, no. 5, pp. 164-179, 2016. [4] M. Sandy, E. Aksehirli, and B. Goethals, "Frequent itemset mining for big data." in Big Data international conference, 2013, pp. 111-118. [5] J. Thorsten, L. Granka, B. Pan, H. Hembrooke, and G. Gay, "Accurately interpreting clickthrough data as implicit feedback." in ACM SIGIR Forum, ACM, 2017, pp. 4-11. [6] R. Stephen, and H. Zaragoza, "The probabilistic relevance framework: BM25 and beyond." Foundations and Trends® in Information Retrieval vol. 3, no. 4, pp. 333-389, 2009. [7] S. Gerard, A. Wong, and C. Yang, "A vector space model for automatic indexing." Communications of the ACM vol. 18, no. 11, pp. 613-620, 1975. [8] W. Jianshu, E. Lim, J. Jiang, and Q. He, "Twitterrank: finding topic-sensitive influential twitterers." in Proceedings of the third ACM international conference on Web search and data mining, ACM, 2010, pp. 261-270. [9] Y. Yuto, T. Takahashi, T. Amagasa, and H. Kitagawa, "Turank: Twitter user ranking based on user-tweet graph analysis." in International Conference on Web Information Systems Engineering, Springer, Berlin, Heidelberg, 2010, pp. 240-253. [10] Y. Ming, J. Sang, and C. Xu, "Unified youtube video recommendation via cross-network collaboration." in Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, ACM, 2015, pp. 19-26. [11] X. Chonghuan, "A novel recommendation method based on social network using matrix factorization technique." Information Processing & Management, vol. 54, no. 3, pp. 463-474, 2018. [12] A. Sajad, M. Meghdadi, and M, Afsharchi. "A social recommendation method based on an adaptive neighbor selection mechanism." Information Processing & Management vol. 54, no. 4, pp. 707-725, 2018. [13] G. D. Feltoni et al. "Temporal people-to-people recommendation on social networks with sentiment-based matrix factorization." Future Generation Computer Systems vol. 78, pp. 430-439, 2018. [14] Z. Hong, D. Ge, and S. Zhang, "Hybrid recommendation system based on semantic interest community and trusted neighbors." Multimedia Tools and Applications vol. 77, no. 4, pp. 4187-4202, 2018. [15] B. Thirunavukarasu, R. Nayak, and C. Yuen, "People to people recommendation using coupled nonnegative Boolean matrix factorization." in IEEE International Conference on Soft-Computing and Network Security, Coimbatore, India, 2018, pp. 14-16. [16] Z. Fattane, M. Kahani, and E. Bagheri, "Mining user interests over active topics on social networks." Information Processing & Management vol. 54, no. 2, pp. 339-357, 2018. [17] C. L. A. Gonzalez, and S. N. Alves-Souza, "Social network data to alleviate cold-start in recommender system: A systematic review." Information Processing & Management vol. 54, no. 4, pp. 529-544, 2018. [18] L. Preethi, G. D. F. Morales, and A. Gionis, "Finding topical experts in Twitter via query-dependent personalized PageRank." in Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ACM, 2017, pp. 1-19. [19] Q. Z. Tony, C. Zhuo, W. Tan, J. Xie, and J. Ye, "Large-Scale Targeted Marketing by Supervised PageRank with Seeds." in International Conference on Machine Learning and Data Mining in Pattern Recognition, Springer, Cham, 2018, pp. 409-424. [20] H. Richang, C. He, Y. Ge, M. Wang, and X. Wu, "User vitality ranking and prediction in social networking services: A dynamic network perspective." IEEE Transactions on Knowledge and Data Engineering vol. 29, no. 6, pp. 1343-1356, 2017. [21] A. Majed, M. AlQurishi, M. AlRakhami, M. M. Hassan, and A. Alamri. "Reputation-based credibility analysis of Twitter social network users." Concurrency and Computation: Practice and Experience vol. 29, no. 7, pp. 4676–4681, 2017. [22] L. S. Ling, R. Chiong, and D. Cornforth, "Ranking of high-value social audiences on Twitter." Decision Support Systems vol. 85, pp. 34-48, 2016. [23] K. Miltiadis, L. Mitrou, V. Stavrou, and D. Gritzalis, "Profiling online social networks users: an omniopticon tool." International Journal of Social Network Mining vol. 2, no. 4, pp. 293-313, 2017. [24] B. Ricardo, "Predicting user behavior in electronic markets based on personality-mining in large online social networks." Electronic Markets vol. 27, no. 3, pp. 247-265, 2017. [25] M. S. Ahmad, M. Jalali, N. Misaghian, S. Shamshirband, and M. H. Anisi. "Community detection in social networks using user frequent pattern mining." Knowledge and Information Systems vol. 51, no. 1, pp. 159-186, 2017. [26] T. A. Kumar, F. Zarrinkalam, and E. Bagheri, "Topic-Association Mining for User Interest Detection." in European Conference on Information Retrieval, Springer, Cham, 2018, pp. 665-671. [27] H. Jiangning, H. Liu, R. Y. K. Lau, and J. He, "Relationship identification across heterogeneous online social networks." Computational Intelligence vol. 33, no. 3, pp. 448-477, 2017. [28] V. Nobahari, M. Jalali, and S. J. S. Mahdavi, "ISoTrustSeq: a social recommender system based on implicit interest, trust and sequential behaviors of users using matrix factorization." Journal of Intelligent Information Systems vol. 1, pp. 1-30, 2018. [29] V. Faridani, M. Jalali, and M. V. Jahan, "Collaborative filtering-based recommender systems by effective trust." International Journal of Data Science and Analytics vol. 3, no. 4, pp. 297-307, 2017. | ||
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