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On Enhancing the User Experience in Web Search Engines Franco Maria Nardini.

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Presentation on theme: "On Enhancing the User Experience in Web Search Engines Franco Maria Nardini."— Presentation transcript:

1 On Enhancing the User Experience in Web Search Engines Franco Maria Nardini

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: web: skype: francomaria.nardini

3 Query Suggestion with Daniele Broccolo, Lorenzo Marcon Raffaele Perego, Fabrizio Silvestri

4 Our Contribution: Search Shortcuts

5

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.

7 Some Results

8 What’s 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

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.

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”.

11 Diversification of Web Search Engine Results with Gabriele Capannini, Raffaele Perego, Fabrizio Silvestri

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 Capannini G., Nardini F.M., Silvestri F., Perego R., Efficient Diversification of Web Search Results. Proceedings of VLDB 2011 (PVLDB), Volume 4, Issue 7.

13 Our Contribution

14

15 Some Results

16 What’s 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

17 Let’s 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

18 Retrieval over Query Sessions with M-Dyaa AlBakour (University of Glasgow)

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?

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, 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.

21 Some Results What’s Next? Entity-based representation of the user session. – to reduce the “sparsity” of the space.

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.

23 Little Announcement Models and Techniques for Tourist Facilities Evaluation and Test Collections User Interaction and Interfaces Paper Deadline 06/25/2012

24 Questions!?!


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