A Workflow Analysis of Context-driven Conversational Recommendation Article Swipe
Shengnan Lyu
,
Arpit Rana
,
Scott Sanner
,
Mohamed Reda Bouadjenek
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1145/3442381.3450123
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1145/3442381.3450123
A number of recent works have made seminal contributions to the understanding of user intent and recommender interaction in conversational recommendation. However, to date, these studies have not focused explicitly on context-driven interaction that underlies the typical use of more pervasive Question Answering (QA) focused conversational assistants like Amazon Alexa, Apple Siri, and Google Assistant. In this paper, we aim to understand a general workflow of natural context-driven conversational recommendation that arises from a pairwise study of a human user interacting with a human simulating the role of a recommender. In our analysis of this intrinsically organic human-to-human conversation, we observe a clear structure of interaction workflow consisting of a preference elicitation and refinement stage, followed by inquiry and critiquing stages after the first recommendation. To better understand the nature of these stages and the conversational flow within them, we augment existing taxonomies of intent and action to label all interactions at each stage and analyze the workflow. From this analysis, we identify distinct conversational characteristics of each stage, e.g., (i) the preference elicitation stage consists of significant iteration to clarify, refine, and obtain a mutual understanding of preferences, (ii) the inquiry and critiquing stage consists of extensive informational queries to understand features of the recommended item and to (implicitly) specify critiques, and (iii) explanation appears to drive a substantial portion of the post-recommendation interaction, suggesting that beyond the purpose of justification, explanation serves a critical role to direct the evolving conversation itself. Altogether, we contribute a novel qualitative and quantitative analysis of workflow in conversational recommendation that further refines our existing understanding of this important frontier of conversational systems and suggests a number of critical avenues for further research to better automate natural recommendation conversations.
Related Topics
Concepts
Conversation
Computer science
Workflow
Context (archaeology)
Recommender system
Preference
Pairwise comparison
Preference elicitation
Conversation analysis
Human–computer interaction
World Wide Web
Data science
Information retrieval
Artificial intelligence
Psychology
Communication
Paleontology
Biology
Economics
Database
Microeconomics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3442381.3450123
- OA Status
- gold
- Cited By
- 13
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3155116159
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3155116159Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1145/3442381.3450123Digital Object Identifier
- Title
-
A Workflow Analysis of Context-driven Conversational RecommendationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-04-19Full publication date if available
- Authors
-
Shengnan Lyu, Arpit Rana, Scott Sanner, Mohamed Reda BouadjenekList of authors in order
- Landing page
-
https://doi.org/10.1145/3442381.3450123Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1145/3442381.3450123Direct OA link when available
- Concepts
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Conversation, Computer science, Workflow, Context (archaeology), Recommender system, Preference, Pairwise comparison, Preference elicitation, Conversation analysis, Human–computer interaction, World Wide Web, Data science, Information retrieval, Artificial intelligence, Psychology, Communication, Paleontology, Biology, Economics, Database, MicroeconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
13Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 4, 2023: 3, 2022: 3, 2021: 2Per-year citation counts (last 5 years)
- References (count)
-
33Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.to | 9, 22, 60, 147, 179, 200, 208, 216, 237, 280 |
| abstract_inverted_index.we | 58, 99, 139, 161, 244 |
| abstract_inverted_index.(i) | 170 |
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| abstract_inverted_index.all | 149 |
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| abstract_inverted_index.for | 277 |
| abstract_inverted_index.not | 27 |
| abstract_inverted_index.our | 91, 260 |
| abstract_inverted_index.the | 10, 35, 85, 122, 128, 134, 156, 171, 190, 204, 222, 228, 239 |
| abstract_inverted_index.use | 37 |
| abstract_inverted_index.(QA) | 43 |
| abstract_inverted_index.(ii) | 189 |
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| abstract_inverted_index.each | 152, 167 |
| abstract_inverted_index.flow | 136 |
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| abstract_inverted_index.have | 5, 26 |
| abstract_inverted_index.item | 206 |
| abstract_inverted_index.like | 47 |
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| abstract_inverted_index.that | 33, 70, 226, 257 |
| abstract_inverted_index.this | 56, 94, 159, 264 |
| abstract_inverted_index.user | 13, 79 |
| abstract_inverted_index.with | 81 |
| abstract_inverted_index.(iii) | 213 |
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| abstract_inverted_index.Siri, | 51 |
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| abstract_inverted_index.clear | 102 |
| abstract_inverted_index.date, | 23 |
| abstract_inverted_index.drive | 217 |
| abstract_inverted_index.e.g., | 169 |
| abstract_inverted_index.first | 123 |
| abstract_inverted_index.human | 78, 83 |
| abstract_inverted_index.label | 148 |
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| abstract_inverted_index.stage | 153, 174, 194 |
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| abstract_inverted_index.Google | 53 |
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| abstract_inverted_index.arises | 71 |
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| abstract_inverted_index.beyond | 227 |
| abstract_inverted_index.direct | 238 |
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| abstract_inverted_index.inquiry | 117, 191 |
| abstract_inverted_index.itself. | 242 |
| abstract_inverted_index.natural | 66, 283 |
| abstract_inverted_index.observe | 100 |
| abstract_inverted_index.organic | 96 |
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| abstract_inverted_index.systems | 269 |
| abstract_inverted_index.typical | 36 |
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| abstract_inverted_index.analysis | 92, 251 |
| abstract_inverted_index.automate | 282 |
| abstract_inverted_index.clarify, | 180 |
| abstract_inverted_index.consists | 175, 195 |
| abstract_inverted_index.critical | 235, 275 |
| abstract_inverted_index.distinct | 163 |
| abstract_inverted_index.evolving | 240 |
| abstract_inverted_index.existing | 141, 261 |
| abstract_inverted_index.features | 202 |
| abstract_inverted_index.followed | 115 |
| abstract_inverted_index.frontier | 266 |
| abstract_inverted_index.identify | 162 |
| abstract_inverted_index.pairwise | 74 |
| abstract_inverted_index.research | 279 |
| abstract_inverted_index.suggests | 271 |
| abstract_inverted_index.workflow | 64, 106, 253 |
| abstract_inverted_index.Answering | 42 |
| abstract_inverted_index.analysis, | 160 |
| abstract_inverted_index.extensive | 197 |
| abstract_inverted_index.important | 265 |
| abstract_inverted_index.iteration | 178 |
| abstract_inverted_index.pervasive | 40 |
| abstract_inverted_index.structure | 103 |
| abstract_inverted_index.underlies | 34 |
| abstract_inverted_index.workflow. | 157 |
| abstract_inverted_index.Assistant. | 54 |
| abstract_inverted_index.assistants | 46 |
| abstract_inverted_index.consisting | 107 |
| abstract_inverted_index.contribute | 245 |
| abstract_inverted_index.critiques, | 211 |
| abstract_inverted_index.critiquing | 119, 193 |
| abstract_inverted_index.explicitly | 29 |
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| abstract_inverted_index.refinement | 113 |
| abstract_inverted_index.simulating | 84 |
| abstract_inverted_index.suggesting | 225 |
| abstract_inverted_index.taxonomies | 142 |
| abstract_inverted_index.understand | 61, 127, 201 |
| abstract_inverted_index.Altogether, | 243 |
| abstract_inverted_index.elicitation | 111, 173 |
| abstract_inverted_index.explanation | 214, 232 |
| abstract_inverted_index.interacting | 80 |
| abstract_inverted_index.interaction | 17, 32, 105 |
| abstract_inverted_index.qualitative | 248 |
| abstract_inverted_index.recommended | 205 |
| abstract_inverted_index.recommender | 16 |
| abstract_inverted_index.significant | 177 |
| abstract_inverted_index.substantial | 219 |
| abstract_inverted_index.(implicitly) | 209 |
| abstract_inverted_index.conversation | 241 |
| abstract_inverted_index.interaction, | 224 |
| abstract_inverted_index.interactions | 150 |
| abstract_inverted_index.preferences, | 188 |
| abstract_inverted_index.quantitative | 250 |
| abstract_inverted_index.recommender. | 89 |
| abstract_inverted_index.contributions | 8 |
| abstract_inverted_index.conversation, | 98 |
| abstract_inverted_index.informational | 198 |
| abstract_inverted_index.intrinsically | 95 |
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| abstract_inverted_index.context-driven | 31, 67 |
| abstract_inverted_index.conversational | 19, 45, 68, 135, 164, 255, 268 |
| abstract_inverted_index.conversations. | 285 |
| abstract_inverted_index.human-to-human | 97 |
| abstract_inverted_index.justification, | 231 |
| abstract_inverted_index.recommendation | 69, 256, 284 |
| abstract_inverted_index.characteristics | 165 |
| abstract_inverted_index.recommendation. | 20, 124 |
| abstract_inverted_index.post-recommendation | 223 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.6499999761581421 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.86919877 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |