Probabilistic field mapping for product search

Master's thesis in Computer scienceOnline shopping has shown a rapid growth in the last few years. Robust search systems are arguably fundamental to e-commerce sites. Most importantly, sites should have smart retrieval systems to present optimized results that could best satisfy customers purchase intent. To address the demand for such systems we adapted retrieval approaches based on a generative language modeling framework, representing products as semi-structured documents. We present and experimentally compare three alternative ranking functions which make use of different prior estimates. The first method is static field weighting approach relying on field’s individual performance taking nDCG as an effectiveness measure. Two other methods dynamically assign term-field weights according to the distribution of terms in field’s collection. These retrieval functions infers from user search keywords the most likely matching product property probabilistically. The methods differ as one of them considers a uniform field prior whereas the other utilizes performance based prior. The methods were evaluated in relatively new evaluation methodology that evaluated ranking systems when real customer were doing online shopping at toy webshop ‘regiojatek.hu’ : Living labs. In the experiment the lab present an interleaved result, based on Team draft interleaving, from production site and our experimental rankings to customers. The Lab employ an evaluation metric “outcome” and we applied outcome measure to compare our methods and to interpret our results. Our results show that both term-specific mapping methods outperformed the static weight assignment approach. In addition results also suggest that estimating field mapping priors based on historical clicks does not outperform the setting where the priors are uniformly distributed. Furthermore,we also discovered that a trec-style evaluation carried out deeming historical clicks as relevance indicators had ordered the methods inversely in relation to Living labs. This has possible implication that Living labs evaluation platform are essential in IR tasks

A product feature-based user-centric product search model

During the online shopping process, users would search for interesting products and quickly access those that fit with their needs among a long tail of similar or closely related products. Our contribution addresses head queries that are frequently submitted on e-commerce Web sites. Head queries usually target featured products with several variations, accessories, and complementary products. We present in this paper a product feature-based user-centric model for product search involving in addition to product characteristics the user engagement toward the product. This model has been evaluated through the product search track of the LL4IR lab at CLEF 2015 in order to highlight the effectiveness of our model as well as the impact of the user engagement factor

Quels facteurs de pertinence pour la recherche de produits e-commerce ?

National audienceUn moteur de recherche e-commerce vise à fournir un accès rapide et efficace à des produits qui correspondent aux besoins et aux préférences de l'utilisateur parmi une liste de produits similaires ou étroitement liés. Nous avons participé à la campagne d'évaluation « Living Lab for Information Retrieval » qui proposait une tâche de recherche de produits évaluée par des utilisateurs réels lors de scénarios de recherche réelle sur un site de e-commerce. L'évaluation expérimentale a montré des résultats prometteurs de notre modèle. Dans ce papier, nous proposons une analyse des fichiers logs issus de notre modèle afin d'identifier des facteurs d'efficacité liés à la requête et aux produits. L'objectif de cette étude est d'ouvrir des pistes de recherche pour la formalisation de modèles de recherche de produits. ABSTRACT. E-commerce product retrieval aims to provide a quick and efficient access to products that fit user's needs and preferences among a tail of similar or closely related products. We participated to the " Living Lab for Information Retrieval " evaluation campaign devoted to a product search task in which real users evaluated par-ticipants' retrieval models in real search scenarios on e-commerce websites. The experimental evaluation has shown encouraging results for our proposed model. In this paper, we conduct an analysis of users' feeadback with respect to the clicks obtained by our model. The goal of the paper is therefore to identify the effectiveness factors underlying the user's queries and the retrieved products in order to open perspectives in the formalization of product search models. MOTS-CLÉS : Recherche d'information, recherche de produits, facteurs d'efficacit

Overview of the living labs for information retrieval evaluation (ll4ir) clef lab

Abstract. In this extended overview paper we discuss the first Living Labs for Information Retrieval Evaluation (LL4IR) lab which was held at CLEF 2015. The idea with living labs is to provide a benchmarking platform for researchers to evaluate their ranking systems in a live setting with real users in their natural task environments. LL4IR represents the first attempt to offer such experimental platform to the IR research community in the form of a community challenge. For this first edition of the challenge we focused on two specific use-cases: product search and web search. Ranking systems submitted by participants were experimentally compared using interleaved comparisons to the production system from the corresponding use-case. In this paper we describe how these experiments were performed, what the resulting outcomes are, and provide a detailed analysis of the use-cases and a discussion of ideas and opportunities for future development

Continuous evaluation of large-scale information access systems : a case for living labs

A/B testing is currently being increasingly adopted for the evaluation of commercial information access systems with a large user base since it provides the advantage of observing the efficiency and effectiveness of information access systems under real conditions. Unfortunately, unless university-based researchers closely collaborate with industry or develop their own infrastructure or user base, they cannot validate their ideas in live settings with real users. Without online testing opportunities open to the research communities, academic researchers are unable to employ online evaluation on a larger scale. This means that they do not get feedback for their ideas and cannot advance their research further. Businesses, on the other hand, miss the opportunity to have higher customer satisfaction due to improved systems. In addition, users miss the chance to benefit from an improved information access system. In this chapter, we introduce two evaluation initiatives at CLEF, NewsREEL and Living Labs for IR (LL4IR), that aim to address this growing “evaluation gap” between academia and industry. We explain the challenges and discuss the experiences organizing these living labs

Probabilistic field mapping for product search

Abstract. This paper describes our participation in the product search task of the CLEF 2015 LL4IR Lab. Working within a generative language modeling framework, we represent products as semi-structured documents. Our focus is on establishing a probabilistic mapping from query terms to document fields. We present and experimentally compare three alternatives. Our results show that term-specific mapping is beneficial. We also find evidence suggesting that estimating field mapping priors based on historical clicks outperforms the setting where the priors are uniformly distributed

Probabilistic Field Mapping for Product Search

This paper describes our participation in the product search task of the CLEF 2015 LL4IR Lab. Working within a generative language modeling framework, we represent products as semi-structured documents. Our focus is on establishing a probabilistic mapping from query terms to document fields. We present and experimentally compare three alternatives. Our results show that term-specific mapping is beneficial. We also find evidence suggesting that estimating field mapping priors based on historical clicks outperforms the setting where the priors are uniformly distributed.publishedVersio