Presentation at joint PIA workshop at UMAP 2014
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CNGL's Dr. Rami Ghorab presented research in multilingual search personalisation during the joint PIA workshop at UMAP 2014. 'Does Personalisation Benefit Everyone in the Same Way? Multilingual Search Personalisation for English vs. Non-English Users'. The research paper, which is accessible here http://bit.ly/1qjRyY5 is authored by; M. Rami Ghorab, Séamus Lawless, Alexander O'Connor and Vincent Wade.
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Presentation at joint PIA workshop at UMAP 2014
- 1. Does Personalisation Benefit Everyone in the Same Way? M. Rami Ghorab Postdoc, School of Computer Science & Statistics, Trinity College Dublin
- 2. Today’s Web Monolingual & Multilingual Users Searching across Multilingual Content • Diverse linguistic backgrounds • Different language capabilities • Different language preferences We want to personalise search, given these characteristics • Various languages. • Relevant content – which lang?
- 3. • 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 Personalisation into the Multilingual Dimension Personalised Multilingual Information Retrieval (PMIR)
- 4. User Modelling Native Language Familiar Languages Preferred Language Attributes Structure
- 5. 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
- 6. Research Question - Revisited Would multilingual search personalisation algorithms achieve the same degree of improvements for all search queries, regardless of query language?
- 7. • 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
- 8. 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.
- 9. Experiment - Results MAP Improvements over Baseline for various result list positions (cut-off points @5..@20)
- 10. Understanding the Results List Position English Non- 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
- 11. • 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
- 12. Thank You This research is supported by the Science Foundation Ireland (Grant 12/CE/I2267) as part of the Centre for Next Generation Localisation (www.cngl.ie) at Trinity College, Dublin.