Abstract
Suggesting further reading materials is an application of recommendation. Considering context, current systems usually rely on topic information and related materials to propose options for users, while users behavior is also commonly used if log information is involved. However, the users interests, which are aroused by the content of the current article they read instead of what they have had, are seldom detected from the context, and they are usually the motive that readers want to read more. This paper presents an approach to detect readers’ interest from the current article they read and the users feedback of it. TED talks are utilized as the experimental materials. InterestFinder proposes interest keywords/keyphrases for each talk, where different kind of words and phrases are provided to it to find suitable candidate terms. Experiments show that the best setting proposed achieves a NDCG@50 0.6392, and the detail results are discussed. Results conclude that considering both words and phrases in a proper selection criteria benifits, and finding conceptual keyphrases as interest terms is necessary to further improve the system performance.
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Davidson, J., Liebald, B., Liu, J., Nandy, P., Van Vleet, T., Gargi, U., Gupta, S., He, Y., Lambert, M., Livingston, B., Sampath, D.: The YouTube Video Recommendation System. In: Proceedings of the Fourth ACM Conference on Recommender Systems (RecSys 2010), pp. 293–296 (2010)
Phelan, O., McCarthy, K., Smyth, S.: Using Twitter to Recommend Real-Time Topical News. In: Proceedings of the third ACM Conference on Recommender systems (RecSys 2009), pp. 385–388 (2009)
Huang, C., Ku, L.-W.: Interest Analysis using Semantic PageRank and Social Interaction Content. In: Proceedings of the IEEE International Conference on Data Mining, SENTIRE Workshop (2013)
Ding, Z., Zhang, Q., Huang, X.: Keyphrase Extraction from Online News Using Binary Integer Programming. In: Proceedings of the 5th International Joint Conference on Natural Language Processing (IJCNLP 2011), pp. 165–173 (2011)
Manning, C.D., Schutze, H.: Foundations of statistical natural language processing. MIT Press (2000)
Li, Q., Wu, Y.-F., Bot, R., Chen, X.: Incorporating Document Keyphrases in Search Results. In: Proceedings of the Americas Conference on Information Systems (2004)
Li, Z., Zhou, G., Juan, Y.-F., Han, J.: Keyword Extraction for Social Snippets. In: Proceedings of the WWW (WWW 2010), pp. 1143–1144 (2010)
Zhao, W.X., Jiang, J., He, J., Song, Y., Achananuparp, P., Lim, E.-P., Li, X.: Topical Keyword Extraction from Twitter. In: Proceedings of the ACL (ACL 2011), pp. 379–388 (2011)
Wu, W., Zhang, B., Ostendorf, M.: Automatic Generation of Personalized Annotation Tags for Twitter Users. In: Proceedings of the NAACL, pp. 689–692 (2010)
Mihalcea, R., Tarau, P.: TextRank: Bringing Orders into Texts. In: Proceedings of the EMNLP, pp. 404–411 (2004)
Liu, Z., Huang, W., Zheng, Y., Sun, M.: Automatic Keyphrase Extraction via Topic Decomposition. In: Proceedings of the EMNLP, pp. 366–376 (2010)
Golder, S.A., Huberman, B.A.: Usage Patterns of Collaborative Tagging Systems. Information Science 32(2), 198–208 (2006)
Halpin, H., Robu, V., Shepherd, H.: The Complex Dynamics of Collaborative Tagging. In: Proceedings of the WWW, pp. 211–220 (2007)
Alfonseca, E., Pighin, D., Garrido, G.: HEADY: News Headline Abstraction Through Event Pattern Clustering. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013), pp. 1243–1253 (2013)
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Ku, LW., Lee, A., Chen, YH. (2014). Finding Keyphrases of Readers’ Interest Utilizing Writers’ Interest in Social Media. In: Nah, F.FH. (eds) HCI in Business. HCIB 2014. Lecture Notes in Computer Science, vol 8527. Springer, Cham. https://doi.org/10.1007/978-3-319-07293-7_18
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DOI: https://doi.org/10.1007/978-3-319-07293-7_18
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