ABSTRACT
In contrast to traditional Web search, where topical relevance is often the main selection criterion, news search is characterized by the increased importance of freshness. However, the estimation of relevance and freshness, and especially the relative importance of these two aspects, are highly specific to the query and the time when the query was issued. In this work, we propose a unified framework for modeling the topical relevance and freshness, as well as their relative importance, based on click logs. We use click statistics and content analysis techniques to define a set of temporal features, which predict the right mix of freshness and relevance for a given query. Experimental results on both historical click data and editorial judgments demonstrate the effectiveness of the proposed approach.
- D. Agarwal, B.-C. Chen, P. Elango, and X. Wang. Click shaping to optimize multiple objectives. In KDD, 2011. Google ScholarDigital Library
- E. Agichtein, E. Brill, S. Dumais, and R. Ragno. Learning user interaction models for predicting web search result preferences. In SIGIR, 2006. Google ScholarDigital Library
- R. Baeza-Yates, B. Ribeiro-Neto, et al. Modern information retrieval, volume 463. ACM press New York, 1999. Google ScholarDigital Library
- S. Brin and L. Page. The anatomy of a large-scale hypertextual web search engine. Computer networks and ISDN systems, 30(1--7):107--117, 1998. Google ScholarDigital Library
- Z. Cao, T. Qin, T. Liu, M. Tsai, and H. Li. Learning to rank: from pairwise approach to listwise approach. In ICML, pages 129--136. ACM, 2007. Google ScholarDigital Library
- H. Chernoff and E. Lehmann. The use of maximum likelihood estimates in $\chi$2 tests for goodness of fit. The Annals of Mathematical Statistics, pages 579--586, 1954.Google ScholarCross Ref
- N. Dai, M. Shokouhi, and B. D. Davison. Learning to rank for freshness and relevance. In SIGIR, pages 95--104, 2011. Google ScholarDigital Library
- A. Dong, Y. Chang, Z. Zheng, G. Mishne, J. Bai, R. Zhang, K. Buchner, C. Liao, and F. Diaz. Towards recency ranking in web search. In WSDM, pages 11--20, 2010. Google ScholarDigital Library
- A. Dong, R. Zhang, P. Kolari, J. Bai, F. Diaz, Y. Chang, Z. Zheng, and H. Zha. Time is of the essence: improving recency ranking using twitter data. In WWW, 2010. Google ScholarDigital Library
- M. Efron and G. Golovchinsky. Estimation methods for ranking recent information. In SIGIR, pages 495--504, 2011. Google ScholarDigital Library
- H. Fang, T. Tao, and C. Zhai. A formal study of information retrieval heuristics. In SIGIR, 2004. Google ScholarDigital Library
- K. J\"arvelin and J. Kek\"al\"ainen. Cumulated gain-based evaluation of ir techniques. ACM TOIS, 20(4):422--446, 2002. Google ScholarDigital Library
- T. Joachims. Optimizing search engines using clickthrough data. In KDD, pages 133--142, 2002. Google ScholarDigital Library
- T. Joachims, L. Granka, B. Pan, H. Hembrooke, and G. Gay. Accurately interpreting clickthrough data as implicit feedback. In SIGIR, pages 154--161, 2005. Google ScholarDigital Library
- K. Jones, S. Walker, and S. Robertson. A probabilistic model of information retrieval: development and comparative experiments. Information Processing and Management, 36(6):779--808, 2000. Google ScholarDigital Library
- N. Kanhabua and K. Nørvåg. Determining time of queries for re-ranking search results. Research and Advanced Technology for Digital Libraries, pages 261--272, 2010. Google ScholarDigital Library
- A. Kulkarni, J. Teevan, K. Svore, and S. Dumais. Understanding temporal query dynamics. In WSDM, 2011. Google ScholarDigital Library
- L. Li, W. Chu, J. Langford, and X. Wang. Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms. In Proceedings of the fourth ACM WSDM '11, pages 297--306, 2011. Google ScholarDigital Library
- X. Li and W. Croft. Time-based language models. In CIKM, pages 469--475, 2003. Google ScholarDigital Library
- T. Liu. Learning to rank for information retrieval. Foundations and Trends in Information Retrieval, 3(3):225--331, 2009. Google ScholarDigital Library
- T. Moon, L. Li, W. Chu, C. Liao, Z. Zheng, and Y. Chang. Online learning for recency search ranking using real-time user feedback. In CIKM, pages 1501--1504, 2010. Google ScholarDigital Library
- G. Salton and M. McGill. Introduction to modern information retrieval. McGraw-Hill, Inc., 1986. Google ScholarDigital Library
- K. M. Svore, M. N. Volkovs, and C. J. Burges. Learning to rank with multiple objective functions. In WWW, 2011. Google ScholarDigital Library
- C. Zhai and J. Lafferty. A study of smoothing methods for language models applied to ad hoc information retrieval. In SIGIR, pages 334--342, 2001. Google ScholarDigital Library
- Z. Zheng, K. Chen, G. Sun, and H. Zha. A regression framework for learning ranking functions using relative relevance judgments. In SIGIR, pages 287--294, 2007. Google ScholarDigital Library
Index Terms
- Joint relevance and freshness learning from clickthroughs for news search
Recommendations
Learning to rank for freshness and relevance
SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information RetrievalFreshness of results is important in modern web search. Failing to recognize the temporal aspect of a query can negatively affect the user experience, and make the search engine appear stale. While freshness and relevance can be closely related for some ...
Ranking Relevance in Yahoo Search
KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data MiningSearch engines play a crucial role in our daily lives. Relevance is the core problem of a commercial search engine. It has attracted thousands of researchers from both academia and industry and has been studied for decades. Relevance in a modern search ...
Learning to rank code examples for code search engines
Source code examples are used by developers to implement unfamiliar tasks by learning from existing solutions. To better support developers in finding existing solutions, code search engines are designed to locate and rank code examples relevant to user'...
Comments