Abstract
The goal of search result diversification is to retrieve diverse documents to meet as many different information needs as possible. Graph neural networks provide a feasible way to capture the sophisticated relationship between candidate documents, while existing graph-based diversification methods require an extra model to construct the graph, which will bring about the problem of error accumulation. In this paper, we propose a novel model to address this problem. Specifically, we maintain a document interaction graph for the candidate documents of each query to model the diverse information interactions between them. To extract latent diversity features, we adopt graph attention networks (GATs) to update the representation of each document by aggregating its neighbors with learnable weights, which enables our model not dependent on knowing the graph structure in advance. Finally, we simultaneously compute the ranking score of each candidate document with the extracted latent diversity features and the traditional relevance features, and the ranking can be acquired by sorting the scores. Experimental results on TREC Web Track benchmark datasets show that the proposed model outperforms existing state-of-the-art models.
X. Xu and K. Ouyang—Equal contribution.
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Agrawal, R., Gollapudi, S., Halverson, A., Ieong, S.: Diversifying search results. In: WSDM, pp. 5–14 (2009)
Carbonell, J., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: SIGIR, pp. 335–336 (1998)
Chapelle, O., Metlzer, D., Zhang, Y., Grinspan, P.: Expected reciprocal rank for graded relevance. In: CIKM, pp. 621–630 (2009)
Clarke, C.L., et al.: Novelty and diversity in information retrieval evaluation. In: SIGIR, pp. 659–666 (2008)
Clarke, C.L.A., Kolla, M., Vechtomova, O.: An effectiveness measure for ambiguous and underspecified queries. In: Azzopardi, L., et al. (eds.) ICTIR 2009. LNCS, vol. 5766, pp. 188–199. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04417-5_17
Dang, V., Croft, B.W.: Term level search result diversification. In: SIGIR, pp. 603–612 (2013)
Dang, V., Croft, W.B.: Diversity by proportionality: an election-based approach to search result diversification. In: SIGIR, pp. 65–74 (2012)
Dang, V., Xue, X., Croft, W.B.: Inferring query aspects from reformulations using clustering. In: CIKM, pp. 2117–2120 (2011)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL, pp. 4171–4186 (2019)
Goswami, A., Zhai, C., Mohapatra, P.: Learning to diversify for e-commerce search with multi-armed bandit. In: SIGIR Workshop (2019)
Hu, S., Dou, Z., Wang, X., Sakai, T., Wen, J.R.: Search result diversification based on hierarchical intents. In: CIKM, pp. 63–72 (2015)
Jansen, B.J., Spink, A., Saracevic, T.: Real life, real users, and real needs: a study and analysis of user queries on the web. Inf. Process. Manag. 36(2), 207–227 (2000)
Jiang, Z., Wen, J.R., Dou, Z., Zhao, W.X., Nie, J.Y., Yue, M.: Learning to diversify search results via subtopic attention. In: SIGIR, pp. 545–554 (2017)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7–9 May 2015, Conference Track Proceedings (2015)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (2017)
Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference on Machine Learning, pp. 1188–1196. PMLR (2014)
Liu, J., Dou, Z., Wang, X., Lu, S., Wen, J.R.: DVGAN: a minimax game for search result diversification combining explicit and implicit features. In: SIGIR, pp. 479–488 (2020)
Nguyen, T.N., Kanhabua, N.: Leveraging dynamic query subtopics for time-aware search result diversification. In: de Rijke, M., Kenter, T., de Vries, A.P., Zhai, C.X., de Jong, F., Radinsky, K., Hofmann, K. (eds.) ECIR 2014. LNCS, vol. 8416, pp. 222–234. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-06028-6_19
Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the web. Technical report, Stanford InfoLab (1999)
Qin, X., Dou, Z., Wen, J.R.: Diversifying search results using self-attention network. In: CIKM, pp. 1265–1274 (2020)
Rafiei, D., Bharat, K., Shukla, A.: Diversifying web search results. In: WWW, pp. 781–790 (2010)
Santos, R.L., Macdonald, C., Ounis, I.: Exploiting query reformulations for web search result diversification. In: WWW, pp. 881–890 (2010)
Silverstein, C., Marais, H., Henzinger, M., Moricz, M.: Analysis of a very large web search engine query log. In: ACM SIGIR Forum, vol. 33, pp. 6–12. ACM New York (1999)
Song, R., Luo, Z., Wen, J.R., Yu, Y., Hon, H.W.: Identifying ambiguous queries in web search. In: WWW, pp. 1169–1170 (2007)
Su, Z., Dou, Z., Zhu, Y., Qin, X., Wen, J.R.: Modeling intent graph for search result diversification. In: SIGIR (2021)
Vaswani, A., et al.: Attention is all you need. In: NeurIPS, pp. 5998–6008 (2017)
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks. In: ICLR (2018)
Wang, C.J., Lin, Y.W., Tsai, M.F., Chen, H.H.: Mining subtopics from different aspects for diversifying search results. Inf. Retrieval 16(4), 452–483 (2013)
Xia, F., Liu, T.Y., Wang, J., Zhang, W., Li, H.: Listwise approach to learning to rank: theory and algorithm. In: ICML, pp. 1192–1199 (2008)
Xia, L., Xu, J., Lan, Y., Guo, J., Cheng, X.: Learning maximal marginal relevance model via directly optimizing diversity evaluation measures. In: SIGIR, pp. 113–122 (2015)
Xia, L., Xu, J., Lan, Y., Guo, J., Cheng, X.: Modeling document novelty with neural tensor network for search result diversification. In: SIGIR, pp. 395–404 (2016)
Yue, Y., Joachims, T.: Predicting diverse subsets using structural SVMs. In: ICML, pp. 1224–1231 (2008)
Zheng, W., Fang, H., Yao, C.: Exploiting concept hierarchy for result diversification. In: CIKM, pp. 1844–1848 (2012)
Zhu, Y., Lan, Y., Guo, J., Cheng, X., Niu, S.: Learning for search result diversification. In: SIGIR, pp. 293–302 (2014)
Acknowledgement
This research is supported by National Natural Science Foundation of China (Grant No. 6201101015), Beijing Academy of Artificial Intelligence (BAAI), Natural Science Foundation of Guangdong Province (Grant No. 2021A1515012640), the Basic Research Fund of Shenzhen City (Grand No. JCYJ20210324120012033 and JCYJ20190813165003837), Overseas Cooperation Research Fund of Tsinghua Shenzhen International Graduate School (Grant No. HW2021008), and research fund of Tsinghua University - Tencent Joint Laboratory for Internet Innovation Technology.
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Xu, X., Ouyang, K., Zheng, Y., Lu, Y., Zheng, HT., Kim, HG. (2022). Diversify Search Results Through Graph Attentive Document Interaction. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13245. Springer, Cham. https://doi.org/10.1007/978-3-031-00123-9_51
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