Dynamic Facet Ordering for Faceted Product Search Engines

Vandic, Damir; Aanen, Steven S.; Frăsincar, Flavius; Kaymak, Uzay

doi:10.1109/tkde.2017.2652461

Cited by 33 publications

(13 citation statements)

References 21 publications

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“…Faceted search, a commonly used web technique in the e-commerce domain, is also a technique that interactively helps a user find candidate items. On faceted-search-enabled websites, buyers can narrow down their list of products by adding constraints on a group of merchandise facets [24]. It has been shown that a well designed faceted search idea can be understood by the average user.…”

Section: Faceted Searchmentioning

confidence: 99%

“…When trying to buy products on an e-Commerce website, users often navigate the product space through faceted search [31][6] [24]. Motivated by this and in order to assist users to find the item they want in conversation, it is crucial that the system understands which values the user has provided for product facets, and represents the user utterances with a semi-structured query.…”

Section: Belief Trackermentioning

confidence: 99%

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Conversational Recommender System

Sun

Zhang

2018

The 41st International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

291

379

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A personalized conversational sales agent could have much commercial potential. E-commerce companies such as Amazon, eBay, JD, Alibaba etc. are piloting such kind of agents with their users. However, the research on this topic is very limited and existing solutions are either based on single round adhoc search engine or traditional multi round dialog system. They usually only utilize user inputs in the current session, ignoring users' long term preferences. On the other hand, it is well known that sales conversion rate can be greatly improved based on recommender systems, which learn user preferences based on past purchasing behavior and optimize business oriented metrics such as conversion rate or expected revenue. In this work, we propose to integrate research in dialog systems and recommender systems into a novel and unified deep reinforcement learning framework to build a personalized conversational recommendation agent that optimizes a per session based utility function.In particular, we propose to represent a user conversation history as a semi-structured user query with facet-value pairs. This query is generated and updated by belief tracker that analyzes natural language utterances of user at each step. We propose a set of machine actions tailored for recommendation agents and train a deep policy network to decide which action (i.e. asking for the value of a facet or making a recommendation) the agent should take at each step. We train a personalized recommendation model that uses both the user's past ratings and user query collected in the current conversational session when making rating predictions and generating recommendations. Such a conversational system often tries to collect user preferences by asking questions. Once enough user preference is collected, it makes personalized recommendations to the user. We perform both simulation experiments and real online user studies to demonstrate the effectiveness of the proposed framework.

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Section: Faceted Searchmentioning

confidence: 99%

Section: Belief Trackermentioning

confidence: 99%

Conversational Recommender System

Sun

Zhang

2018

The 41st International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

291

379

View full text Add to dashboard Cite

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“…It is advanced the usage of ASP.NET [14]. Bing has an instant answer feature that provides quick information to the user about sports, finance, health information, flight tracking, package tracking and tracing, Advanced computations, and many more [15]. Bing rewards became a loyalty application released through microsoft in september 2010.…”

Section: Comparative Analysis Of Bing Search Enginementioning

confidence: 99%

A Comparative Analysis of Search Engine Ranking Algorithms

2021

IJATCSE

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Ranking Algorithm is the most proper way of positioning on a scale. As the information and knowledge on the internet are increasing every day.The search engine's ability to deliver the most appropriate material to the customer. It is more and more challenging without even any assistance in filtering through all of it. However, searching what user requires is extremely difficult. In this research, an effort has been made to compare and analyze the most popular and effective search engines. The keywords were used in uniform resource locator like, title tag, header, or even the keyword's resembles to the actual text. The page rank algorithm computes a perfect judgment of how relevant a webpage is by analyzing the quality and calculating the number of links connected to it. In this study the keyword relevancy and time response were used for search engines and observed the results. It is observed that the google search engine is faster than the bing and youtube, and after all, bing is the best search engine after google. Moreover, youtube is the fastest search engine in terms of video content search. The google results were found more accurate. However, it is better than all of the search engine

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“…System-wise, specialized types of search engines have been developed, for instance, enterprise search engines (e-commerce, e-marketing) or consumer search engines, market monitors, product/service recommender systems (Vandic et al, 2017;Trotman et al, 2017). This also includes customer-supplier interaction platforms (e.g., portals, helps desks, newsgroups) and transaction support systems based on natural language communication (including business chat bots) (Cui et al, 2017;Altinok, 2018).…”

Section: Related Workmentioning

confidence: 99%

A Corpus of Corporate Annual and Social Responsibility Reports: 280 Million Tokens of Balanced Organizational Writing

Händschke

Buechel

Goldenstein

et al. 2018

Proceedings of the First Workshop on Economics and Natural Language Processing

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We introduce JOCO, a novel text corpus for NLP analytics in the field of economics, business and management. This corpus is composed of corporate annual and social responsibility reports of the top 30 US, UK and German companies in the major (DJIA, FTSE 100, DAX), middlesized (S&P 500, FTSE 250, MDAX) and technology (NASDAQ, FTSE AIM 100, TECDAX) stock indices, respectively. Altogether, this adds up to 5,000 reports from 270 companies headquartered in three of the world's most important economies. The corpus spans a time frame from 2000 up to 2015 and contains, in total, 282M tokens. We also feature JOCO in a smallscale experiment to demonstrate its potential for NLP-fueled studies in economics, business and management research.

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Dynamic Facet Ordering for Faceted Product Search Engines

Cited by 33 publications

References 21 publications

Conversational Recommender System

Conversational Recommender System

A Comparative Analysis of Search Engine Ranking Algorithms

A Corpus of Corporate Annual and Social Responsibility Reports: 280 Million Tokens of Balanced Organizational Writing

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