Performance Predictors for Conversational Fashion Recommendation

Vlachou, M. and Macdonald, C. (2022) Performance Predictors for Conversational Fashion Recommendation. In: 4th Edition of Knowledge-aware and Conversational Recommender Systems (KaRS) Workshop @ RecSys 2022, Seattle, WA, USA, 18-23rd Sept 2022, pp. 91-100.

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In Conversational Recommendation Systems (CRS), a user can provide natural language feedback on suggested items, which the recommender uses to produce improved suggestions. Therefore, the success of a user’s conversation with the CRS is determined by how well the system is able to interpret the user’s feedback and the quality of the recommendations. Knowing whether a conversation is likely to be successful may allow the CRS to adjust accordingly - for instance, changing its retrieval strategy, or asking a clarifying question. Existing work on Query Performance Prediction (QPP) has examined a number of predictors that indicate the effectiveness of a search engine’s ranking in response to a query. Inspired by existing work in QPP, we propose a framework for Conversational Performance Prediction (CPP) that aims to predict conversation failures by considering the recommendation ranking at different turns of a conversation, either one turn at a time, or by considering multiple consecutive turns. In this regard, we adapt post-retrieval predictors to address the multi-turn nature of the CRS task. We conduct our analysis on Shoes and FashionIQ Shirts & Dresses datasets. In particular, as a ground truth, we measure conversation difficulty by the effectiveness of the ranking at a given turn of the conversation. Overall, we find some promise in score-based retrieval predictors for CPP, obtaining medium strength correlations with conversation difficulty - for instance, observing a Spearman’s p of 0.423 on the Shoes dataset, which is comparable to correlations observed for standard QPP predictors on adhoc search tasks.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Macdonald, Professor Craig and Vlachou, Ms Maria
Authors: Vlachou, M., and Macdonald, C.
College/School:College of Science and Engineering > School of Computing Science
Research Centre:College of Science and Engineering > School of Computing Science > IDA Section > GPU Cluster
Published Online:04 December 2022
Copyright Holders:Copyright © 2022 The Authors
First Published:First published in Proceedings of the Fourth Knowledge-aware and Conversational Recommender Systems Workshop co-located with 16th ACM Conference on Recommender Systems (RecSys 202: 91-100
Publisher Policy:Reproduced under a Creative Commons licence
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
303764EPSRC CDT - Socially Intelligent Artificial AgentsAlessandro VinciarelliEngineering and Physical Sciences Research Council (EPSRC)EP/S02266X/1Computing Science