Does Personalisation Benefit Everyone in the Same Way?

M. Rami GhorabPostdoc, School of Computer Science & Statistics,

Trinity College Dublin

Today’s Web

Monolingual & MultilingualUsers

Searching acrossMultilingual Content

• Diverse linguistic backgrounds

• Different language capabilities

• Different language preferences

We want to personalise search, given these characteristics

• Various languages.

• Relevant content – which lang?

• User Modelling– Search interests (keywords) that span across multiple languages.– Grouped into language fragments.

• Adapting Results in Multilingual Web Search– Merging and Re-ranking the results.– Translating where necessary.

Extending Personalisationinto the Multilingual Dimension

Personalised Multilingual Information Retrieval (PMIR)

User Modelling

Native Language

Familiar Languages

Preferred Language

Attributes

Structure

Result Lists(English, French, German)

Ranked separately

against keywords

in User Model fragment

(textual similarity)

Re-ranked Result Lists

(English, French, German)

Merged & Translated List

Research Question - Revisited

Would multilingual search personalisation algorithms

achieve the same degree of improvements

for all search queries, regardless of query language?

• Evaluate the retrieval effectiveness of the multilingual search personalisation algorithms (User Modelling and Result Adaptation).

• Determine whether the algorithms achieve the same degree of effectiveness for users who have different language preferences (examine English vs. Non-English users).

Experiment - Objectives

Experiment - Setup

Phase 2: Result Pooling

• Last query reserved for testing.

• Construct the user models.

• Generate various result lists.Phase 3: Relevance Judgments

• 4-point scale of relevance

(not relevant / somewhat relevant / relevant / very relevant)

Phase 4: Evaluation

• Metric: Mean Average Precision (MAP).

• Measures effectiveness of each algorithm across all test queries

Phase 1: User Participation

• Sign up – language preferences.

• Two search topics.

• Use baseline multilingual Web search.

• Submit findings about topic.

Experiment - Results

MAP Improvements over Baselinefor various result list positions (cut-off points @5..@20)

Understanding the Results

List Position

EnglishNon-

English%

English over Non-English

P@5 0.58 0.45 29.15%

P@10 0.55 0.49 11.54%

P@15 0.51 0.45 14.46%

P@20 0.50 0.48 3.71%

Baseline (non-personalised) Precision Scores

• Does personalisation benefit everyone in the same way?– No.– Multilingual search adaptation algorithms work differently with users of

different language preferences/capabilities.

• Recommendation– Personalised Search systems should adopt different personalisation

strategies for certain languages or groups of languages.

• Future Work– Concept-based user models (multilingual ontology or web taxonomy).

Conclusion & Future Work

Thank You

This research is supported bythe Science Foundation Ireland (Grant 12/CE/I2267)

as part of the Centre for Next Generation Localisation (www.cngl.ie) at Trinity College, Dublin.