Abstract
In recent years, Twitter has become one of the most popular microblogging avenues. Today it has more than 300 million monthly active users generating more than 500 million tweets everyday. Twitter users both post messages as well as search for messages. Current search results given by Twitter are chronologically ordered and often users have to manually scan through an overwhelming number of the tweets to find content of interest. This process can quickly become infeasible. Personalization techniques address this problem by learning the user interests and tailoring search results by matching them with the user’s interests. Recent research on neural word embedding models, which represents each word in the vocabulary as a vector of real values, has gained much attention. These models learn word embeddings in such a way that contextually similar words have similar vectors. In this paper we propose a novel approach, PWEBA, for personalizing Twitter search, which uses neural word embeddings to model user interests. Our experimental results show that PWEBA outperforms existing approaches for all the evaluation metrics we have considered in this paper.
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References
Bouadjenek, M.R., Hacid, H., Bouzeghoub, M.: Social networks and information retrieval, how are they converging? a survey, a taxonomy and an analysis of social information retrieval approaches and platforms. Inf. Syst. 56, 1–18 (2016)
Carman, M.J., Baillie, M., Crestani, F.: Tag data and personalized information retrieval. In: Proceedings of the 2008 ACM Workshop on Search in Social Media, pp. 27–34. ACM (2008)
Culotta, A.: Training a text classifier with a single word using twitter lists and domain adaptation. Soc. Netw. Anal. Min. 6(1), 1–15 (2016)
Dou, Z., Song, R., Wen, J.-R., Yuan, X.: Evaluating the effectiveness of personalized web search. TKDE 21(8), 1178–1190 (2009)
Efron, M.: Information search and retrieval in microblogs. J. Am. Soc. Inf. Sci. Technol. 62(6), 996–1008 (2011)
Java, A., Song, X., Finin, T., Tseng, B.: Why we twitter: understanding microblogging usage and communities. In: WebKDD/SNA-KDD, pp. 56–65. ACM (2007)
Kang, J.H., Lerman, K.: Using lists to measure homophily on Twitter. In: AAAI Workshop on Intelligent Techniques for Web Personalization and Recommendation, pp. 26–32 (2012)
Lau, C.H., Li, Y., Tjondronegoro, D.: Microblog retrieval using topical features and query expansion. In: TREC (2011)
Leung, K.-T., Lee, D.L., Lee, W.-C.: Personalized web search with location preferences. In: ICDE, pp. 701–712. IEEE (2010)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS, pp. 3111–3119 (2013)
O’Connor, B., Krieger, M., Ahn, D.: Tweetmotif: exploratory search and topic summarization for twitter. In: ICWSM, pp. 384–385 (2010)
Rakesh, V., Singh, D., Vinzamuri, B., Reddy, C.K.: Personalized recommendation of twitter lists using content and network information. In: ICWSM, pp. 416–425 (2014)
Rzeszotarski, J.M., Spiro, E.S., Matias, J.N., Monroy-Hernández, A., Morris, M.R.: Is anyone out there? unpacking Q&A hashtags on twitter. In: CHI, pp. 2755–2758. ACM (2014)
Sontag, D., Collins-Thompson, K., Bennett, P.N., White, R.W., Dumais, S., Billerbeck, B.: Probabilistic models for personalizing web search. In: WSDM, pp. 433–442. ACM (2012)
Tao, K., Abel, F., Hauff, C., Houben, G.-J.: Twinder: a search engine for twitter streams. In: Brambilla, M., Tokuda, T., Tolksdorf, R. (eds.) ICWE 2012. LNCS, vol. 7387, pp. 153–168. Springer, Heidelberg (2012). doi:10.1007/978-3-642-31753-8_11
Teevan, J., Ramage, D., Morris, M.R.: # twittersearch: a comparison of microblog search and web search. In: WSDM, pp. 35–44. ACM (2011)
Ushiama, T., Tominaga, K.: A method for personalized ranking of items based on similarity between twitter users. In: ICUIMC, pp. 44:1–44:4. ACM (2014)
Vallet, D., Cantador, I., Jose, J.M.: Personalizing web search with folksonomy-based user and document profiles. In: Gurrin, C., He, Y., Kazai, G., Kruschwitz, U., Little, S., Roelleke, T., Rüger, S., Rijsbergen, K. (eds.) ECIR 2010. LNCS, vol. 5993, pp. 420–431. Springer, Heidelberg (2010). doi:10.1007/978-3-642-12275-0_37
Vosecky, J., Leung, K.W.-T., Ng, W.: Collaborative personalized twitter search with topic-language models. In: SIGIR, pp. 53–62. ACM (2014)
Wang, H., He, X., Chang, M.-W., Song, Y., White, R.W., Chu, W.: Personalized ranking model adaptation for web search. In: SIGIR, pp. 323–332. ACM (2013)
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Samarawickrama, S., Karunasekera, S., Harwood, A., Kotagiri, R. (2017). Search Result Personalization in Twitter Using Neural Word Embeddings. In: Bellatreche, L., Chakravarthy, S. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2017. Lecture Notes in Computer Science(), vol 10440. Springer, Cham. https://doi.org/10.1007/978-3-319-64283-3_18
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