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On Enhancing the User Experience in Web Search Engines Franco Maria Nardini Slide 2 About Me I joined the HPC Lab in 2006 Master Thesis Ph.D. in 2011, University of Pisa Thesis: Query Log Mining to Enhance User Experience in Search Engines mail: francomaria.nardini@isti.cnr.it web: http://hpc.isti.cnr.it/~nardini skype: francomaria.nardini Slide 3 Query Suggestion with Daniele Broccolo, Lorenzo Marcon Raffaele Perego, Fabrizio Silvestri Slide 4 Our Contribution: Search Shortcuts Slide 5 Slide 6 Search Shortcuts: It uses the happy ending stories in the query log to help new users; Efficient: All the stuff is stored on a inverted index: retrieval problem; Effective: (head, torso, tail) New evaluation methodology confirming this evidencies: TREC Diversity Track. Daniele Broccolo, Lorenzo Marcon, Franco Maria Nardini, Fabrizio Silvestri, Raffaele Perego, Generating Suggestions for Queries in the Long Tail with an Inverted Index, IP&M, 2011. Slide 7 Some Results Slide 8 Whats Next?! Why not to use Machine Learning? Machine learning is helping a lot in the IR community; Better and fine-graned ranking as it could take into account important signals that are not fully- exploited nowadays; It may helps in filtering redundant suggestions and choosing the best expressive ones (for each intent). under exploration with Marcin Sydow (PJIIT), Raffaele Perego, Fabrizio Silvestri Slide 9 Signals Which signals we would like to capture? Relevance to the given query; Diversity with respect to a subtopic list; Serendipity of suggestions; Novelty with respect to news/trends on Twitter; How do we catch them? How do we combine them? The training set is a problem. Slide 10 Query Suggestion: Ranking A two-step architecture First step to produce a list of candidates; Second step as a ML architecture composed of two different (cascade) stages of ranking: First round to rank suggestions w.r.t. the query; Second round to understand diversity. Slide 11 Diversification of Web Search Engine Results with Gabriele Capannini, Raffaele Perego, Fabrizio Silvestri Slide 12 Our Contribution We design a method for efficiently diversify results from Web search engines. Same effectiveness of other state-of-the-art approaches; Extremely fast in doing the hard work; Intents behind ambiguous queries are mined from query logs; Capannini G., Nardini F.M., Silvestri F., Perego R., A Search Architecture Enabling Efficient Diversification of Search Results, Proc. DDR Workshop 2011. Capannini G., Nardini F.M., Silvestri F., Perego R., Efficient Diversification of Web Search Results. Proceedings of VLDB 2011 (PVLDB), Volume 4, Issue 7. Slide 13 Our Contribution Slide 14 Slide 15 Some Results Slide 16 Whats Next? A modern ranking architecture: Effective: Users should be happy of the results they receive; Efficient: Low response times (< 0.1 s); Easy to adapt: Continuous crawling from the Web; Continuous users feedback; with Berkant Barla Cambazoglu (Yahoo! Barcelona), Gabriele Capannini, Raffaele Perego, Fabrizio Silvestri Slide 17 Lets Plug All Together BM25 Scorer 1 Scorer n Query Index Second Phase First Phase Results Scorer div SS A way for efficiently diversifying ambiguous queries; SS teaches how to diversify the current user query; Scorer div computes the diversity signal of each document and rerank the final results list; Possible intents behind the query Slide 18 Retrieval over Query Sessions with M-Dyaa AlBakour (University of Glasgow) Slide 19 Main Goals Question 1) Can Web search engines improve their performance by using previous user interactions? (including previous queries, clicks on ranked results, dwell times, etc.) Question 2) How do we evaluate system performance over an entire query session instead of a single query? Slide 20 TREC Session Track Two editions of the challenge: 2010, 2011 query, previous queries; urls + docs, urls + docs + dwell time; Two different evaluations: last subtop., all subtop. Query expansion with Search Shortcuts: weighted by means of user interaction data; history-based recommendation; Follow-up with tuning of the parameters. Ibrahim Adeyanju, Franco Maria Nardini, M-Dyaa Albakour, Dawei Song, Udo Kruschwitz, RGU-ISTI-Essex at TREC 2011 Session Track, TREC Conference, 2011. Franco Maria Nardini, M-Dyaa Albakour, Ibrahim Adeyanju, Udo Kruschwitz, Studying Search Shortcuts in a Query Log to Improve Retrieval Over Query Sessions, SIR 2012 in conjunction with ECIR 2012. Slide 21 Some Results Whats Next? Entity-based representation of the user session. to reduce the sparsity of the space. Slide 22 Challenges How those systems really affect (and modify) the behavior of the user? Is it possible to quantify it? (metrics?) What do we need to observe? Toward the perfect result page: accurate models for blending different sources of results. Slide 23 Little Announcement http://tf.isti.cnr.it Models and Techniques for Tourist Facilities Evaluation and Test Collections User Interaction and Interfaces Paper Deadline 06/25/2012 Slide 24 Questions!?!