Towards Knowledge-Based Recommender Dialog System

Chen, Qibin; Lin, Junyang; Zhang, Yichang; Ding, Ming; Cen, Yukuo; Yang, Hongxia; Tang, Jie

doi:10.18653/v1/d19-1189

Cited by 150 publications

(270 citation statements)

References 38 publications

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“…A key point for recommendation is to learn a good representation of user preference. Different from traditional recommender systems, following [4,18], we assume no previous interaction records are available. We can only utilize the conversation data to infer user preference.…”

Section: Kg-enhanced Recommender Modulementioning

confidence: 99%

“…• KBRD [4]: This model utilizes DBpedia to enhance the semantics of contextual items or entities. The dialog generation module is based on the Transformer architecture, in which KG information serves as word bias for generation.…”

Section: Baselinesmentioning

confidence: 99%

“…For the conversation task, the evaluation consists of automatic evaluation and human evaluation. Following [4], we use Distinct n-gram (n = 2, 3, 4) to measure the diversity at sentence level. For CRS, it is particularly important that the dialog system is able to generate informative replies related to items or entities.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…Without bridging the semantic gap, it is infeasible to generate the text for explaining the recommendation, e.g., "thriller movie with good plot". For enriching the conversation information, external knowledge graph (KG) has been utilized in CRS [4]. They mainly focus on incorporating item knowledge, while the word-level enrichment (e.g., the relation between "scary" and "thriller") has been somehow neglected.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion

Zhou

Zhao

Bian

et al. 2020

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

215

220

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Conversational recommender systems (CRS) aim to recommend high-quality items to users through interactive conversations. Although several efforts have been made for CRS, two major issues still remain to be solved. First, the conversation data itself lacks of sufficient contextual information for accurately understanding users' preference. Second, there is a semantic gap between natural language expression and item-level user preference. To address these issues, we incorporate both word-oriented and entity-oriented knowledge graphs (KG) to enhance the data representations in CRSs, and adopt Mutual Information Maximization to align the word-level and entity-level semantic spaces. Based on the aligned semantic representations, we further develop a KGenhanced recommender component for making accurate recommendations, and a KG-enhanced dialog component that can generate informative keywords or entities in the response text. Extensive experiments have demonstrated the effectiveness of our approach in yielding better performance on both recommendation and conversation tasks. CCS CONCEPTS • Information systems → Recommender systems; • Computing methodologies → Natural language generation.

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Section: Kg-enhanced Recommender Modulementioning

confidence: 99%

Section: Baselinesmentioning

confidence: 99%

Section: Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion

Zhou

Zhao

Bian

et al. 2020

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

215

220

View full text Add to dashboard Cite

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“…One of the key components of conversational search and recommender systems [5,7] is the construction and selection of good clarifying questions to gather item information from users in a searchable repository. Most current studies either collect and learn from human-to-human conversations [1,2,4], or create a pool of questions on the basis of some "anchor" text (e.g. item aspects [5], entities [6][7][8], grounding text [3]) that characterizes the searchable items themselves.…”

Section: Introductionmentioning

confidence: 99%

An Empirical Study on Clarifying Question-Based Systems

Zou

Kanoulas

Liu

2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Search and recommender systems that take the initiative to ask clarifying questions to better understand users' information needs are receiving increasing attention from the research community. However, to the best of our knowledge, there is no empirical study to quantify whether and to what extent users are willing or able to answer these questions. In this work, we conduct an online experiment by deploying an experimental system, which interacts with users by asking clarifying questions against a product repository. We collect both implicit interaction behavior data and explicit feedback from users showing that: (a) users are willing to answer a good number of clarifying questions (11 on average), but not many more than that; (b) most users answer questions until they reach the target product, but also a fraction of them stops due to fatigue or due to receiving irrelevant questions; (c) part of the users' answers (17%) are actually opposite to the description of the target product; while (d) most of the users (84%) find the question-based system helpful towards completing their tasks. Some of the findings of the study contradict current assumptions on simulated evaluations in the field, while they point towards improvements in the evaluation framework and can inspire future interactive search/recommender system designs.

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Recommender systems: Trends and frontiers

Jannach

Ricci

et al. 2022

AI Magazine

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Recommender systems (RSs), as used by Netflix, YouTube, or Amazon, are one of the most compelling success stories of AI. Enduring research activity in this area has led to a continuous improvement of recommendation techniques over the years, and today's RSs are indeed often capable to make astonishingly good suggestions. With countless papers being published on the topic each year, one might think the recommendation problem is almost solved. In reality, however, the large majority of published works focuses on algorithmic improvements and relies on data-based evaluation procedures which may sometimes tell us little regarding the effects new algorithms will have in practice. This special issue contains a set of papers which address some of the open challenges and frontiers in RSs research: (i) building interactive and conversational solutions, (ii) understanding recommender systems as socio-technical systems with longitudinal dynamics, (iii) avoiding abstraction traps, and (iv) finding better ways of assessing the impact and value of recommender systems without field tests. RECOMMENDER SYSTEMS -A SUCCESS STORY, MOSTLYPersonalized suggestions for items to buy, news to read or movies to watch are nowadays ubiquitous on the web. Recommender systems are software solutions that generate these suggestions, commonly with the help of statistical models and machine learning techniques. Given their widespread use in practice, their often astonishingly good recommendations, and their proven value for consumers and providers, it is no surprise that research on recommender systems is flourishing. Today, we are witnessing a constantly growing interest in the topic both in academia and industry, with countless papers being published every year.Given this continued research interest, the use of latest deep learning technology also in industry, for example (Steck et al. 2021), and the high quality of many deployedThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Towards Knowledge-Based Recommender Dialog System

Cited by 150 publications

References 38 publications

Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion

Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion

An Empirical Study on Clarifying Question-Based Systems

Recommender systems: Trends and frontiers

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