Abstract
Relevance estimation is among the most important tasks in the ranking of search results. Current methodologies mainly concentrate on text matching, link analysis, and user behavior models. However, users judge the relevance of search results directly from Search Engine Result Pages (SERPs), which provide valuable signals for reranking. In this article, we propose two different approaches to aggregate the visual, structure, as well as textual information sources of search results in relevance estimation. The first one is a late-fusion framework named Joint Relevance Estimation model (JRE). JRE estimates the relevance independently from screenshots, textual contents, and HTML source codes of search results and jointly makes the final decision through an inter-modality attention mechanism. The second one is an early-fusion framework named Tree-based Deep Neural Network (TreeNN), which embeds the texts and images into the HTML parse tree through a recursive process. To evaluate the performance of the proposed models, we construct a large-scale practical Search Result Relevance (SRR) dataset that consists of multiple information sources and relevance labels of over 60,000 search results. Experimental results show that the proposed two models achieve better performance than state-of-the-art ranking solutions as well as the original rankings of commercial search engines.
- Javad Azimi, Ruofei Zhang, Zhou Yang, Vidhya Navalpakkam, Jianchang Mao, and Xiaoli Fern. 2012. The impact of visual appearance on user response in online display advertising. In Proceedings of the 21st International Conference on World Wide Web. ACM, 457--458. Google ScholarDigital Library
- Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.Google Scholar
- Deng Cai, Shipeng Yu, Ji-Rong Wen, and Wei-Ying Ma. 2003. Extracting content structure for web pages based on visual representation. In Proceedings of the Asia-Pacific Web Conference. Springer, 406--417. Google ScholarDigital Library
- Olivier Chapelle and Ya Zhang. 2009. A dynamic bayesian network click model for web search ranking. In Proceedings of the 18th International Conference on World Wide Web. ACM, 1--10. Google ScholarDigital Library
- Danqi Chen, Weizhu Chen, Haixun Wang, Zheng Chen, and Qiang Yang. 2012. Beyond ten blue links: Enabling user click modeling in federated web search. In Proceedings of the 5th ACM International Conference on Web Search and Data Mining. ACM, 463--472. Google ScholarDigital Library
- Kan Chen, Trung Bui, Chen Fang, Zhaowen Wang, and Ram Nevatia. 2017. AMC: Attention guided multi-modal correlation learning for image search. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2644--2652.Google ScholarCross Ref
- Haibin Cheng, Roelof Van Zwol, Javad Azimi, Eren Manavoglu, Ruofei Zhang, Yang Zhou, and Vidhya Navalpakkam. 2012. Multimedia features for click prediction of new ads in display advertising. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 777--785.Google ScholarDigital Library
- Kyunghyun Cho, Bart Van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014. On the properties of neural machine translation: Encoder-decoder approaches. arXiv preprint arXiv:1409.1259.Google Scholar
- Dan C. Ciresan, Ueli Meier, Jonathan Masci, Luca Maria Gambardella, and Jürgen Schmidhuber. 2011. Flexible, high performance convolutional neural networks for image classification. In Proceedings of the Proceedings-International Joint Conference on Artificial Intelligence (IJCAI’11), Vol. 22, 1237.Google Scholar
- Jacob Cohen. 1968. Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. Psychol. Bull. 70, 4 (1968), 213.Google ScholarCross Ref
- Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. Imagenet: A large-scale hierarchical image database. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’09). IEEE, 248--255.Google ScholarCross Ref
- Georges E. Dupret and Benjamin Piwowarski. 2008. A user browsing model to predict search engine click data from past observations. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 331--338. Google ScholarDigital Library
- Mark Everingham, S. M. Ali Eslami, Luc Van Gool, Christopher K. I. Williams, John Winn, and Andrew Zisserman. 2015. The pascal visual object classes challenge: A retrospective. Int. J. Comput. Vis. 111, 1 (2015), 98--136. Google ScholarDigital Library
- Yixing Fan, Jiafeng Guo, Yanyan Lan, Jun Xu, Liang Pang, and Xueqi Cheng. 2017. Learning visual features from snapshots for web search. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. ACM, 247--256. Google ScholarDigital Library
- Paolo Frasconi, Marco Gori, and Alessandro Sperduti. 1998. A general framework for adaptive processing of data structures. IEEE Trans. Neur. Netw. 9, 5 (1998), 768--786. Google ScholarDigital Library
- Yoav Freund, Raj D. Iyer, Robert E. Schapire, and Yoram Singer. 1998. An efficient boosting algorithm for combining preferences. In Proceedings of the 15th International Conference on Machine Learning. 170--178. Google ScholarDigital Library
- Jerome H. Friedman. 2001. Greedy function approximation: A gradient boosting machine. Ann. Stat. 29, 5 (2001), 1189--1232.Google ScholarCross Ref
- Christoph Goller and Andreas Kuchler. 1996. Learning task-dependent distributed representations by backpropagation through structure. In Proceedings of International Conference on Neural Networks (ICNN'96), Vol. 1. IEEE, 347--352.Google ScholarCross Ref
- Fan Guo, Chao Liu, and Yi Min Wang. 2009. Efficient multiple-click models in web search. In Proceedings of the 2nd ACM International Conference on Web Search and Data Mining. ACM, 124--131. Google ScholarDigital Library
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 770--778.Google ScholarCross Ref
- Geoffrey E. Hinton. 1990. Mapping part-whole hierarchies into connectionist networks. Artif. Intell. 46, 1--2 (1990), 47--75. Google ScholarDigital Library
- Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Comput. 9, 8 (1997), 1735--1780. Google ScholarDigital Library
- Baotian Hu, Zhengdong Lu, Hang Li, and Qingcai Chen. 2014. Convolutional neural network architectures for matching natural language sentences. In Proceedings of the International Conference on Neural Information Processing Systems. 2042--2050. Google ScholarDigital Library
- Thorsten Joachims. 2002. Optimizing search engines using clickthrough data. In Proceedings of the ACM Conference on Knowledge Discovery and Data Mining. 133--142. Google ScholarDigital Library
- Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A Shamma, et al. 2017. Visual genome: Connecting language and vision using crowdsourced dense image annotations. Int. J. Comput. Vis. 123, 1 (2017), 32--73. Google ScholarDigital Library
- Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems. 1097--1105. Google ScholarDigital Library
- Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. 2014. Microsoft coco: Common objects in context. In Proceedings of the European Conference on Computer Vision. Springer, 740--755.Google ScholarCross Ref
- Jiasen Lu, Caiming Xiong, Devi Parikh, and Richard Socher. 2016. Knowing when to look: Adaptive attention via A visual sentinel for image captioning. arXiv preprint arXiv:1612.01887 (2016).Google Scholar
- Cheng Luo, Yiqun Liu, Tetsuya Sakai, Fan Zhang, Min Zhang, and Shaoping Ma. 2017. Evaluating mobile search with height-biased gain. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 435--444. Google ScholarDigital Library
- Jie Luo, Sudarshan Lamkhede, Rochit Sapra, Evans Hsu, Helen Song, and Chang Yi. 2013. A unified search federation system based on online user feedback. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Google ScholarDigital Library
- Minh Thang Luong, Hieu Pham, and Christopher D. Manning. 2015. Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025.Google Scholar
- Thomas Mandl. 2006. Implementation and evaluation of a quality-based search engine. In Proceedings of the 17th Conference on Hypertext and Hypermedia. ACM, 73--84. Google ScholarDigital Library
- Schutze Manning Raghavan. 2008. Introduction to information retrieval. J. Am. Soc. Inf. Sci. Technol. 43, 3 (2008), 824--825.Google Scholar
- Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Distributed representations of words and phrases and their compositionality. Adv. Neural Inf. Process. Syst. 26 (2013), 3111--3119. Google ScholarDigital Library
- L. Page. 1999. The PageRank citation ranking: Bringing order to the web. Stanf. Dig. Libr. Work. Pap. 9, 1 (1999), 1--14.Google Scholar
- Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, Shengxian Wan, and Xueqi Cheng. 2016. Text matching as image recognition. In Proceedings of the Association for the Advancement of Artificial Intelligence Conference (AAAI’16). 2793--2799.Google Scholar
- Jordan B. Pollack. 1990. Recursive distributed representations. Artif. Intell. 46, 1--2 (1990), 77--105. Google ScholarDigital Library
- Tao Qin, Tie Yan Liu, Jun Xu, and Hang Li. 2010. LETOR: A benchmark collection for research on learning to rank for information retrieval. Inf. Retriev. 13, 4 (2010), 346--374. Google ScholarDigital Library
- Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2017. Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39, 6 (2017), 1137--1149. Google ScholarDigital Library
- Stephen Robertson and Hugo Zaragoza. 2009. The probabilistic relevance framework: BM25 and beyond. Found. Trends Inf. Retriev. 3, 4 (2009), 333--389. Google ScholarDigital Library
- Kalervo Rvelin, Kek, and Jaana Inen. 2002. Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. 20, 4 (2002), 422--446.Google ScholarDigital Library
- Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).Google Scholar
- Richard Socher, Jeffrey Pennington, Eric H. Huang, Andrew Y. Ng, and Christopher D. Manning. 2011. Semi-supervised recursive autoencoders for predicting sentiment distributions. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 151--161. Google ScholarDigital Library
- Ruihua Song, Haifeng Liu, Ji-Rong Wen, and Wei-Ying Ma. 2004. Learning block importance models for web pages. In Proceedings of the 13th International Conference on World Wide Web. ACM, 203--211. Google ScholarDigital Library
- Ruihua Song, Haifeng Liu, Ji-Rong Wen, and Wei-Ying Ma. 2004. Learning important models for web page blocks based on layout and content analysis. ACM SIGKDD Explor. Newslett. 6, 2 (2004), 14--23. Google ScholarDigital Library
- Alessandro Sperduti and Antonina Starita. 1997. Supervised neural networks for the classification of structures. IEEE Trans. Neural Netw. 8, 3 (1997), 714--735. Google ScholarDigital Library
- Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1--9.Google ScholarCross Ref
- Kai Sheng Tai, Richard Socher, and Christopher D. Manning. 2015. Improved semantic representations from tree-structured long short-term memory networks. arXiv preprint arXiv:1503.00075 (2015).Google Scholar
- Shengxian Wan, Yanyan Lan, Jiafeng Guo, Jun Xu, Liang Pang, and Xueqi Cheng. 2015. A deep architecture for semantic matching with multiple positional sentence representations. In Proceedings of theThirtieth AAAI Conference on Artificial Intelligence. 2835--2841. Google ScholarDigital Library
- Chao Wang, Yiqun Liu, Meng Wang, Ke Zhou, Jian-yun Nie, and Shaoping Ma. 2015. Incorporating non-sequential behavior into click models. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 283--292. Google ScholarDigital Library
- Chao Wang, Yiqun Liu, Min Zhang, Shaoping Ma, Meihong Zheng, Jing Qian, and Kuo Zhang. 2013. Incorporating vertical results into search click models. In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 503--512. Google ScholarDigital Library
- Qiang Wu, Christopher J. C. Burges, Krysta M. Svore, and Jianfeng Gao. 2010. Adapting boosting for information retrieval measures. Inf. Retriev. 13, 3 (2010), 254--270. Google ScholarDigital Library
- Jun Xu and Hang Li. 2007. AdaRank: A boosting algorithm for information retrieval. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 391--398.Google ScholarDigital Library
- Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard Zemel, and Yoshua Bengio. 2015. Show, attend and tell: Neural image caption generation with visual attention. In Proceedings of the International Conference on Machine Learning. 2048--2057. Google ScholarDigital Library
- Dawei Yin, Yuening Hu, Jiliang Tang, Tim Daly, Mianwei Zhou, Hua Ouyang, Jianhui Chen, Changsung Kang, Hongbo Deng, and Chikashi Nobata. 2016. Ranking relevance in Yahoo search. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 323--332. Google ScholarDigital Library
- Junqi Zhang, Yiqun Liu, Shaoping Ma, and Qi Tian. 2018. Relevance estimation with multiple information sources on search engine result pages. In Proceedings of the 2018 ACM on Conference on Information and Knowledge Management. ACM, 10. Google ScholarDigital Library
- Masrour Zoghi, Tomáš Tunys, Lihong Li, Damien Jose, Junyan Chen, Chun Ming Chin, and Maarten de Rijke. 2016. Click-based hot fixes for underperforming torso queries. In Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 195--204. Google ScholarDigital Library
Index Terms
- Search Result Reranking with Visual and Structure Information Sources
Recommendations
Relevance Estimation with Multiple Information Sources on Search Engine Result Pages
CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge ManagementRelevance estimation is among the most important tasks in the ranking of search results because most search engines follow the Probability Ranking Principle. Current relevance estimation methodologies mainly concentrate on text matching between the ...
Intent-based diversification of web search results: metrics and algorithms
We study the problem of web search result diversification in the case where intent based relevance scores are available. A diversified search result will hopefully satisfy the information need of user-L.s who may have different intents. In this context, ...
Re-ranking search results using query logs
CIKM '06: Proceedings of the 15th ACM international conference on Information and knowledge managementThis work addresses two common problems in search, frequently occurring with underspecified user queries: the top-ranked results for such queries may not contain documents relevant to the user's search intent, and fresh and relevant pages may not get ...
Comments