Improving Search Personalisation with Dynamic Group Formation

Tien-Thanh VuComputing and Communications DepartmentThe Open UniversityImproving Search Personalisation with Dynamic Group Formation

Table of ContentsMotivationMethodologyA pilot studyConclusions and Future Work

MotivationMethodologyA pilot studyConclusions and Future Work

What is search personalisation?

The search performancedepends onthe richness of a user profileJ. Teevan, M. R. Morris, and S. Bush. Discovering and using groups to improve personalized search. WSDM09, pages 15-24, USA, 2009. ACM

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How can we enrich a user profile?Use information of the group of users who share common interests

R. W. White, W. Chu, A. Hassan, X. He, Y. Song, and H. Wang. Enhancing personalized search by mining and modeling task behavior. WWW '13, pages 1411-1420, Switzerland, 2013. ACM

What is the main research problem?

Construct groups statically using some predetermined criterions such as common clicked documentsUsers in a group may have different interests on different topics w.r.t the input query

Z. Dou, R. Song, and J.-R. Wen. A large-scale evaluation and analysis of personalized search strategies. WWW '07, pages 581-590, NY, USA, 2007. ACM.

Our proposalThe groups should be dynamically constructed in response to the users input query

Research questionHow can we improve the performance of search personalisation with dynamic group formation?How can we dynamically group users who share common interests?How can we enrich user profiles with group information?Can enriched user profiles help to improve search performance?

MotivationMethodologyA pilot studyConclusions and Future Work

Constructing a user profileConstruct implicitly using the relevant data extracted from each users search history (i.e. query logs)Extract related topics from the dataBuild a user profile based on the topics

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Constructing a user profileAverage the relevant documents over topics

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Query-dependent user groupingConstruct shared user profilesUse the input query as an indicator for grouping users

Constructing a shared user profile

Query-dependent user grouping

Query-dependent user grouping

The 2-nearest users

Enriching a user profileAverage all users in the group over topics

Re-ranking search resultsFor each input queryDownload the top n ranked search results from the search engineCompute a personalised score for each web page d given the current user uCombine the personalised score p(d|u) and the original rank r(q,d), to get a final score

Re-ranking search results

MotivationMethodologyA pilot studyConclusions and Future Work

A pilot studyEvaluation metricsDatasetPreliminary results

Evaluation metricPersonalisation Gain (P-Gain)

DatasetQuery logs from Bing search engine for 15 days from 1st to 15th July 2012, 106 anonymous usersA relevant document is a click with dwell time of at least 30 seconds or the last click in a session (SAT click)ItemALLTrainingTest#days15105#users106106106#queries17947116956252#distinct queries800852373102#clicks24041156888353#SAT clicks16166106075559#SAT clicks/#queries0.90080.90690.8892

Preliminary resultsBaseline and Personalisation StrategiesBaseline: The original ranked results from BingS_Profile: Use only the current user profileS_Group: Enrich the profile with static groupD_Group: Enrich the profile with dynamic group

Preliminary resultsOverall Performance

Strategy#Better#WorseP-GainS_Profile9136640.1579S_Group8824910.2848D_Group8844500.3253

MotivationMethodologyA pilot studyConclusions and Future Work

ConclusionsGrouping improves search performanceDynamic grouping outperforms static grouping

OutlookCarry out evaluation on larger-scale data setsExtend the model to capture users interests that change over time

Thank You!Any Questions?

Constructing a user profileUse a topic modelling method to learn topics from the data

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How can we build a user profile?Ask the user explicitly to provide her interests (e.g. questionnaires)Infer her interests implicitly using the users search history