Survey

Literature survey (aka. literature review) is an integral part of doing genuine research in any field of modern engineering and science. To help you gain necessary experience and training in this process, we require all graduate students to independently finish a survey paper about a research topic selected from the provided list below.

If you have never done this before, or did not read any survey papers yet, you can follow the general suggestions below; otherwise, you can directly jump to the section of listed topics and the detailed requirements.

General steps

  1. Collecting a large set of papers related to the topic you have chosen (e.g., via Google Scholar or Microsoft Academic ).
  2. Based on your knowledge about the area and a few initial readings, prepare an organization schema for the topic.
  3. Start carefully reading and organizing your collection of papers with respect to this schema. Take notes about those papers and try to cluster them and compare them.
  4. Find any missing parts in your current collections of papers, and then go back to Step 1 to find related papers to fill the hole.
  5. After several rounds iterating from Step 1 to 4, you should be able to expend your notes into a well-organized survey paper.

List of topics

You can choose from the list of topics we suggested for the paper presentations, where you can already find a small set of related papers provided under each topic. And here we provide you a few more options to consider:

  1. Conversational IR systems
    • Christakopoulou, Konstantina, Filip Radlinski, and Katja Hofmann. "Towards conversational recommender systems." In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 815-824. 2016.
    • Sun, Yueming, and Yi Zhang. "Conversational recommender system." In The 41st international acm sigir conference on research & development in information retrieval, pp. 235-244. 2018.
    • Mahmood, Tariq, and Francesco Ricci. "Improving recommender systems with adaptive conversational strategies." In Proceedings of the 20th ACM conference on Hypertext and hypermedia, pp. 73-82. 2009.
    • Radlinski, Filip, and Nick Craswell. "A theoretical framework for conversational search." In Proceedings of the 2017 conference on conference human information interaction and retrieval, pp. 117-126. 2017.
  2. Search log analysis
    • Silverstein, Craig, Hannes Marais, Monika Henzinger, and Michael Moricz. "Analysis of a very large web search engine query log." In ACm SIGIR Forum, vol. 33, no. 1, pp. 6-12. New York, NY, USA: ACM, 1999.
    • Liu, Chao, Ryen W. White, and Susan Dumais. "Understanding web browsing behaviors through Weibull analysis of dwell time." In Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, pp. 379-386. 2010.
    • Bennett, Paul N., Ryen W. White, Wei Chu, Susan T. Dumais, Peter Bailey, Fedor Borisyuk, and Xiaoyuan Cui. "Modeling the impact of short-and long-term behavior on search personalization." In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval, pp. 185-194. 2012.
    • Collins-Thompson, Kevyn, Paul N. Bennett, Ryen W. White, Sebastian De La Chica, and David Sontag. "Personalizing web search results by reading level." In Proceedings of the 20th ACM international conference on Information and knowledge management, pp. 403-412. 2011.
  3. Neural language model for IR
    • Dai, Zhuyun, and Jamie Callan. "Deeper text understanding for IR with contextual neural language modeling." In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 985-988. 2019.
    • Zamani, Hamed, and W. Bruce Croft. "Embedding-based query language models." In Proceedings of the 2016 ACM international conference on the theory of information retrieval, pp. 147-156. 2016.
    • Nogueira, Rodrigo, Zhiying Jiang, and Jimmy Lin. "Document ranking with a pretrained sequence-to-sequence model." arXiv preprint arXiv:2003.06713 (2020).
    • Nogueira, Rodrigo, and Kyunghyun Cho. "Passage Re-ranking with BERT." arXiv preprint arXiv:1901.04085 (2019).
  4. Personalized retrieval
    • Rendle, Steffen, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. "BPR: Bayesian personalized ranking from implicit feedback." arXiv preprint arXiv:1205.2618 (2012).
    • He, Xiangnan, Zhankui He, Xiaoyu Du, and Tat-Seng Chua. "Adversarial personalized ranking for recommendation." In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 355-364. 2018.
    • Chu, Wei, and Seung-Taek Park. "Personalized recommendation on dynamic content using predictive bilinear models." In Proceedings of the 18th international conference on World wide web, pp. 691-700. 2009.
    • Liu, Fang, Clement Yu, and Weiyi Meng. "Personalized web search for improving retrieval effectiveness." IEEE Transactions on knowledge and data engineering 16, no. 1 (2004): 28-40.
  5. IR for health
    • Zeng, Qing T., Jonathan Crowell, Robert M. Plovnick, Eunjung Kim, Long Ngo, and Emily Dibble. "Assisting consumer health information retrieval with query recommendations." Journal of the American Medical Informatics Association 13, no. 1 (2006): 80-90.
    • Zeng, Qing T., Sandra Kogan, Robert M. Plovnick, Jonathan Crowell, Eve-Marie Lacroix, and Robert A. Greenes. "Positive attitudes and failed queries: an exploration of the conundrums of consumer health information retrieval." International journal of medical informatics 73, no. 1 (2004): 45-55.
    • Cartright, Marc-Allen, Ryen W. White, and Eric Horvitz. "Intentions and attention in exploratory health search." In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, pp. 65-74. 2011.
    • White, Ryen W., and Eric Horvitz. "Experiences with web search on medical concerns and self diagnosis." In AMIA annual symposium proceedings, vol. 2009, p. 696. American Medical Informatics Association, 2009.
  6. Knowledge graph for IR
    • Liu, Zhenghao, Chenyan Xiong, Maosong Sun, and Zhiyuan Liu. "Entity-duet neural ranking: Understanding the role of knowledge graph semantics in neural information retrieval." arXiv preprint arXiv:1805.07591 (2018).
    • Xiong, Chenyan, Russell Power, and Jamie Callan. "Explicit semantic ranking for academic search via knowledge graph embedding." In Proceedings of the 26th international conference on world wide web, pp. 1271-1279. 2017.
    • Wang, Xiang, Xiangnan He, Yixin Cao, Meng Liu, and Tat-Seng Chua. "Kgat: Knowledge graph attention network for recommendation." In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 950-958. 2019.
    • Cao, Yixin, Xiang Wang, Xiangnan He, Zikun Hu, and Tat-Seng Chua. "Unifying knowledge graph learning and recommendation: Towards a better understanding of user preferences." In The world wide web conference, pp. 151-161. 2019.

Requirements

Every graduate student is required to finish a survey paper independently. The topic has to be chosen from the provided list, unless you have a strong reason for any topic outside the list (if so, inform the instructor by email before you proceed). Multiple students can choose the same topic, and also have overlaps in their surveyed papers. For many topics we listed, you can already find good survey papers out there. You are encouraged to refer to their organizations, but it is prohibited for you to copy any content from them. Plagiarism is a very serious misconduct in academia.

You are required to use the ACM single column large template. You need to submit your paper in PDF format. And the page limit is up to 6 pages, excluding the references. The due date of this survey is the last week of this semester, i.e., May 15th. A collab submission page will be created. If you have any questions regarding your survey paper, please feel free to contact the instructor.