research-article

Can Virtual Assistants Produce Recommendations?

Authors:

Dimitrios Rafailidis,

Yannis ManolopoulosAuthors Info & Claims

WIMS2019: Proceedings of the 9th International Conference on Web Intelligence, Mining and Semantics

Article No.: 4, Pages 1 - 6

https://doi.org/10.1145/3326467.3326468

Published: 26 June 2019 Publication History

Abstract

Virtual assistants, also known as intelligent conversational systems such as Google's Virtual Assistant and Apple's Siri, interact with human-like responses to users' queries and finish specific tasks. Meanwhile, existing recommendation technologies model users' evolving, diverse and multi-aspect preferences to generate recommendations in various domains/applications, aiming to improve the citizens' daily life by making suggestions. The repertoire of actions is no longer limited to the one-shot presentation of recommendation lists, which can be insufficient when the goal is to offer decision support for the user, by quickly adapting to his/her preferences through conversations. Such an interactive mechanism is currently missing from recommendation systems. This article sheds light on the gap between virtual assistants and recommendation systems in terms of different technological aspects. In particular, we try to answer the most fundamental research question, which are the missing technological factors to implement a personalized intelligent conversational agent for producing accurate recommendations while taking into account how users behave under different conditions. The goal is, instead of adapting humans to machines, to actually provide users with better recommendation services so that machines will be adapted to humans in daily life.

References

[1]

Mohammad Aliannejadi, Dimitrios Rafailidis, and Fabio Crestani. 2017. Personalized Keyword Boosting for Venue Suggestion Based on Multiple LBSNs. In Proceedings of 39th European Conference on IR Research, ECIR 2017, Aberdeen, UK. 291--303.

[2]

Tomas Mikolov andIlya Sutskever, Kai Chen, Gregory S. Corrado, and Jeffrey Dean. 2013. Distributed Representations of Words and Phrases and their Compositionality. In Proceedings 27th Annual Conference on Neural Information Processing Systems. Lake Tahoe, NV, 3111--3119.

Digital Library

[3]

Rocío Cañamares and Pablo Castells. 2018. Should I Follow the Crowd?: A Probabilistic Analysis of the Effectiveness of Popularity in Recommender Systems. In Proceedings 41st ACM International Conference on Research & Development in Information Retrieval (SIGIR). Ann Arbor, MI, 415--424.

Digital Library

[4]

James Davidson, Benjamin Liebald, Junning Liu, Palash Nandy, Taylor Van Vleet, Ullas Gargi, Sujoy Gupta, Yu He, Mike Lambert, Blake Livingston, and Dasarathi Sampath. 2010. The YouTube Video Recommendation System. In Proceedings 4th ACM Conference on Recommender Systems (RecSys). Barcelona, Spain, 293--296.

Digital Library

[5]

Bhuwan Dhingra, Lihong Li, Xiujun Li, Jianfeng Gao, Yun-Nung Chen, Faisal Ahmed, and Li Deng. 2017. Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access. In Proceedings 55th Annual Meeting of the Association for Computational Linguistics (ACL), Vol. I. Vancouver, Canada, 484--495.

[6]

Ali Mamdouh Elkahky, Yang Song, and Xiaodong He. 2015. A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems. In Proceedings 24th International Conference on World Wide Web (WWW). Florence, Italy, 278--288.

Digital Library

[7]

Mehmet H. Göker, Pat Langley, and Cynthia A. Thompson. 2011. A Personalized System for Conversational Recommendations. CoRR abs/1107.0029 (2011).

[8]

Mehmet H. Göker and Cynthia A. Thompson. 2000. Personalized Conversational Case-Based Recommendation. In Proceedings 5th European Workshop on Advances in Case-Based Reasoning (EWCBR). Trento, Italy, 99--111.

Digital Library

[9]

Carlos A. Gomez-Uribe and Neil Hunt. 2016. The Netflix Recommender System: Algorithms, Business Value, and Innovation. ACM Transactions on Management Information Systems 6, 4 (2016), 13:1--13:19.

Digital Library

[10]

Asela Gunawardana and Guy Shani. 2015. Evaluating Recommender Systems. In Recommender Systems Handbook. Springer, 265--308.

[11]

Mahipal Jadeja and Neelanshi Varia. 2017. Perspectives for Evaluating Conversational AI. CoRR abs/1709.04734 (2017). arXiv:1709.04734 http://arxiv.org/abs/1709.04734

[12]

Saikishore Kalloori, Francesco Ricci, and Rosella Gennari. 2018. Eliciting Pairwise Preferences in Recommender Systems. In Proceedings 12th ACM Conference on Recommender Systems (RecSys). Vancouver, Canada, 329--337.

Digital Library

[13]

Saikishore Kalloori, Francesco Ricci, and Marko Tkalcic. 2016. Pairwise Preferences Based Matrix Factorization and Nearest Neighbor Recommendation Techniques. In Proceedings 10th ACM Conference on Recommender Systems (RecSys). Boston, MA, 143--146.

Digital Library

[14]

Jie Kang, Kyle Condiff, Shuo Chang, Joseph A. Konstan, Loren G. Terveen, and F. Maxwell Harper. 2017. Understanding How People Use Natural Language to Ask for Recommendations. In Proceedings 11th ACM Conference on Recommender Systems (RecSys). Como, Italy, 229--237.

Digital Library

[15]

Yehuda Koren, Robert M. Bell, and Chris Volinsky. 2009. Matrix Factorization Techniques for Recommender Systems. IEEE Computer 42, 8 (2009), 30--37.

Digital Library

[16]

Pasquale Lops, Marco de Gemmis, and Giovanni Semeraro. 2011. Content-based Recommender Systems: State of the Art and Trends. In Recommender Systems Handbook. Springer, 73--105.

[17]

Julian J. McAuley and Jure Leskovec. 2013. Hidden Factors and Hidden Topics: Understanding Rating Dimensions with Review Text. In Proceedings 7th ACM Conference on Recommender Systems (RecSys). Hong Kong, China, 165--172.

Digital Library

[18]

James McInerney, Benjamin Lacker, Samantha Hansen, Karl Higley, Hugues Bouchard, Alois Gruson, and Rishabh Mehrotra. 2018. Explore, Exploit, and Explain: Personalizing Explainable Recommendations with Bandits. In Proceedings 12th ACM Conference on Recommender Systems (RecSys). Vancouver, Canada, 31--39.

Digital Library

[19]

Alexandros Nanopoulos, Dimitrios Rafailidis, Panagiotis Symeonidis, and Yannis Manolopoulos. 2010. MusicBox: Personalized Music Recommendation Based on Cubic Analysis of Social Tags. IEEE Transactions on Audio, Speech & Language Processing 18, 2 (2010), 407--412.

Digital Library

[20]

Minghui Qiu, Liu Yang, Feng Ji, Wei Zhou, Jun Huang, Haiqing Chen, W. Bruce Croft, and Wei Lin. 2018. Transfer Learning for Context-aware Question Matching in Information-seeking Conversations in E-commerce. In Proceedings 56th Annual Meeting of the Association for Computational Linguistics (ACL), Vol. II. Melbourne, Australia, 208--213.

[21]

Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi, and Paolo Cremonesi. 2017. Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks. In Proceedings 11th ACM Conference on Recommender Systems (RecSys). Como, Italy, 130--137.

Digital Library

[22]

Dimitrios Rafailidis, Apostolos Axenopoulos, Jonas Etzold, Stavroula Manolopoulou, and Petros Daras. 2014. Content-based tag propagation and tensor factorization for personalized item recommendation based on social tagging. TiiS 3, 4 (2014), 26:1--26:27.

Digital Library

[23]

Dimitrios Rafailidis and Fabio Crestani. 2016. Collaborative Ranking with Social Relationships for Top-N Recommendations. In Proceedings of 39th International ACM SIGIR conference on Research and Development in Information Retrieval, SIGIR 2016, Pisa, Italy. 785--788.

Digital Library

[24]

Dimitrios Rafailidis and Fabio Crestani. 2016. Joint Collaborative Ranking with Social Relationships in Top-N Recommendation. In Proceedings of ACM International Conference on Information and Knowledge Management, CIKM 2016, Indianapolis, IN, USA. 1393--1402.

Digital Library

[25]

Dimitrios Rafailidis and Fabio Crestani. 2016. Top-N Recommendation via Joint Cross-domain User Clustering and Similarity Learning. In Proceedings European Conference on Machine Learning & Knowledge Discovery in Databases (ECML/PKDD), Vol. II. Riva del Garda, Italy, 426--441.

[26]

Dimitrios Rafailidis and Fabio Crestani. 2017. Learning to Rank with Trust and Distrust in Recommender Systems. In Proceedings of the 11th ACM Conference on Recommender Systems, RecSys 2017, Como, Italy. 5--13.

Digital Library

[27]

Dimitrios Rafailidis and Fabio Crestani. 2017. Recommendation with Social Relationships via Deep Learning. In Proceedings 7th ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR). Amsterdam, The Netherlands, 151--158.

Digital Library

[28]

Dimitrios Rafailidis and Fabio Crestani. 2017. A Regularization Method with Inference of Trust and Distrust in Recommender Systems. In Proceedings European Conference on Machine Learning & Knowledge Discovery in Databases (ECML/PKDD), Vol. II. Skopje, FYROM, 253--268.

[29]

Dimitrios Rafailidis, Pavlos Kefalas, and Yannis Manolopoulos. 2017. Preference dynamics with multimodal user-item interactions in social media recommendation. Expert Syst. Appl. 74 (2017), 11--18.

Digital Library

[30]

Dimitrios Rafailidis and Alexandros Nanopoulos. 2015. Repeat Consumption Recommendation Based on Users Preference Dynamics and Side Information. In Companion Proceedings 24th International Conference on World Wide Web (WWW). Florence, Italy, 99--100.

Digital Library

[31]

Rep1 2018. {Online}. Available: https://www.drgdigital.com/drgdigital-innovation-blog/hallo-siri-voice-assistants/. (2018).

[32]

Rep2 2018. {Online}. Available: https://www.statista.com/statistics. (2018).

[33]

Rep3 2018. {Online}. Available: https://www.techradar.com/. (2018).

[34]

Rep4 2018. {Online}. Available: https://aytm.com/blog/ai-personal-assistants/. (2018).

[35]

Rep5 2018. {Online}. Available: http://www.internetlivestats.com/internet-users/. (2018).

[36]

Rep6 2018. {Online}. Available: https://www.woebot.io/. (2018).

[37]

Rep6 2018. {Online}. Available: https://wit.ai/. (2018).

[38]

Lina Maria Rojas-Barahona, Milica Gasic, Nikola Mrksic, Pei-Hao Su, Stefan Ultes, Tsung-Hsien Wen, Steve J. Young, and David Vandyke. 2017. A Network-based End-to-End Trainable Task-oriented Dialogue System. In Proceedings 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL), Vol. I. Valencia, Spain, 438--449.

[39]

Jessica Schroeder, Chelsey Wilkes, Kael Rowan, Arturo Toledo, Ann Paradiso, Mary Czerwinski, Gloria Mark, and Marsha M. Linehan. 2018. Pocket Skills: A Conversational Mobile Web App to Support Dialectical Behavioral Therapy. In Proceedings Conference on Human Factors in Computing Systems (CHI). Montreal, Canada, 398.

Digital Library

[40]

Damiano Spina, Johanne R. Trippas, Lawrence Cavedon, and Mark Sanderson. 2017. Extracting Audio Summaries to Support Effective Spoken Document Search. Journal of the Association for Information Science & Technology 68, 9 (2017), 2101--2115.

Digital Library

[41]

Yueming Sun and Yi Zhang. 2018. Conversational Recommender System. In Proceedings 41st International ACM Conference on Research & Development in Information Retrieval (SIGIR). Ann Arbor, MI, 235--244.

Digital Library

[42]

Johanne R. Trippas, Damiano Spina, Lawrence Cavedon, and Mark Sanderson. 2017. How do People Interact in Conversational Speech-only Search Tasks: A Preliminary Analysis. In Proceedings Conference on Conference Human Information Interaction & Retrieval (CHIIR). Oslo, Norway, 325--328.

Digital Library

[43]

Svitlana Vakulenko, Maarten de Rijke, Michael Cochez, Vadim Savenkov, and Axel Polleres. 2018. Measuring Semantic Coherence of a Conversation. In Proceedings 17th International Semantic Web Conference (ISWC), Vol. I. Monterey, CA, 634--651.

Digital Library

[44]

Daniel Valcarce, Alejandro Bellogín, Javier Parapar, and Pablo Castells. 2018. On the Robustness and Discriminative Power of Information Retrieval Metrics for Top-N Recommendation. In Proceedings 12th ACM Conference on Recommender Systems (RecSys). Vancouver, Canada, 260--268.

Digital Library

[45]

Marisa Vasconcelos, Heloisa Candello, Claudio S. Pinhanez, and Thiago dos Santos. 2017. Bottester: Testing Conversational Systems with Simulated Users. In Proceedings XVI Brazilian Symposium on Human Factors in Computing Systems (IHC). Joinville, Brazil, 73:1--73:4.

Digital Library

[46]

Chong Wang and David M. Blei. 2011. Collaborative Topic Modeling for Recommending Scientific Articles. In Proceedings 17th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD). San Diego, CA, 448--456.

Digital Library

[47]

Longqi Yang, Michael Sobolev, Christina Tsangouri, and Deborah Estrin. 2018. Understanding User Interactions with Podcast Recommendations Delivered via Voice. In Proceedings 12th ACM Conference on Recommender Systems (RecSys). Vancouver, Canada, 190--194.

Digital Library

[48]

Yongfeng Zhang, Xu Chen, Qingyao Ai, Liu Yang, and W. Bruce Croft. 2018. Towards Conversational Search and Recommendation: System Ask, User Respond. In Proceedings 27th ACM International Conference on Information & Knowledge Management (CIKM). Torino, Italy, 177--186.

Digital Library

[49]

Tiancheng Zhao and Maxine Eskénazi. 2016. Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning. CoRR abs/1606.02560 (2016).

Cited By

Köksal C(2024)Where Is Digital Retailing Evolving?Navigating the Shifting Landscape of Consumer Behavior10.4018/979-8-3693-1594-1.ch006(139-173)Online publication date: 22-Mar-2024
https://doi.org/10.4018/979-8-3693-1594-1.ch006
Kim JLee JYun JKang U(2024)Dependency-aware action planning for smart homePLOS ONE10.1371/journal.pone.030541519:6(e0305415)Online publication date: 18-Jun-2024
https://doi.org/10.1371/journal.pone.0305415
Kalirai MWilliams AKuzminykh A(2024)Toward Faceted Skill Recommendation in Intelligent Personal AssistantsProceedings of the 29th International Conference on Intelligent User Interfaces10.1145/3640543.3645201(640-649)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3640543.3645201
Show More Cited By

Index Terms

Can Virtual Assistants Produce Recommendations?
1. Information systems
  1. Information systems applications
    1. Collaborative and social computing systems and tools

Recommendations

Agent-based collaborative filtering based on fuzzy recommendations

Recommender systems intend to provide suggestions based on the opinion of several sources of information. But personalised suggestions based on past user's likes and dislikes require a distributed approach. In this way, agents may automatically collect ...
Expediting Exploration by Attribute-to-Feature Mapping for Cold-Start Recommendations
RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

The item cold-start problem is inherent to collaborative filtering (CF) recommenders where items and users are represented by vectors in a latent space. It emerges since CF recommenders rely solely on historical user interactions to characterize their ...
Using Implicit Feedback for Neighbors Selection: Alleviating the Sparsity Problem in Collaborative Recommendation Systems
WebMedia '17: Proceedings of the 23rd Brazillian Symposium on Multimedia and the Web

The most popular Recommender systems (RSs) employ Collaborative Filtering (CF) algorithms where users explicitly rate items. Based on these ratings, a user-item rating matrix is generated and used to select the items to be recommended for a target user. ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

WIMS2019: Proceedings of the 9th International Conference on Web Intelligence, Mining and Semantics

June 2019

231 pages

ISBN:9781450361903

DOI:10.1145/3326467

Conference Chair:
Rajendra Akerkar,
General Chair:
Jason J. Jung

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

CAU: Chung-Ang University
KISM: Korean Institute of Smart Media

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 June 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

WIMS2019

WIMS2019: 9th International Conference on Web Intelligence, Mining and Semantics

June 26 - 28, 2019

Seoul, Republic of Korea

Acceptance Rates

Overall Acceptance Rate 140 of 278 submissions, 50%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

20
Total Citations
View Citations
597
Total Downloads

Downloads (Last 12 months)51
Downloads (Last 6 weeks)3

Reflects downloads up to 05 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Köksal C(2024)Where Is Digital Retailing Evolving?Navigating the Shifting Landscape of Consumer Behavior10.4018/979-8-3693-1594-1.ch006(139-173)Online publication date: 22-Mar-2024
https://doi.org/10.4018/979-8-3693-1594-1.ch006
Kim JLee JYun JKang U(2024)Dependency-aware action planning for smart homePLOS ONE10.1371/journal.pone.030541519:6(e0305415)Online publication date: 18-Jun-2024
https://doi.org/10.1371/journal.pone.0305415
Kalirai MWilliams AKuzminykh A(2024)Toward Faceted Skill Recommendation in Intelligent Personal AssistantsProceedings of the 29th International Conference on Intelligent User Interfaces10.1145/3640543.3645201(640-649)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3640543.3645201
Zaidi SRai SJuneja K(2024)A Review of Existing Conversational Recommendation Systems2024 2nd International Conference on Disruptive Technologies (ICDT)10.1109/ICDT61202.2024.10489053(22-26)Online publication date: 15-Mar-2024
https://doi.org/10.1109/ICDT61202.2024.10489053
Nawaz AAfzal AKhatibi AShankar AMadan HFaisal HShahbaz AUsman IZulfiqar NSaeed FAhmed AImran AAfzaal Mkarni AAhmed FAkram NRasheed MIslam F(2024)Role of Artificial Intelligence in Halal Authentication and Traceability: A Concurrent ReviewFood Control10.1016/j.foodcont.2024.111003(111003)Online publication date: Nov-2024
https://doi.org/10.1016/j.foodcont.2024.111003
Musto CMartina AIovine ANarducci Fde Gemmis MSemeraro G(2024)Tell me what you Like: introducing natural language preference elicitation strategies in a virtual assistant for the movie domainJournal of Intelligent Information Systems10.1007/s10844-023-00835-862:2(575-599)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s10844-023-00835-8
Salem MShaalan K(2024)ChatGPT: Advancing Education with Virtual AssistantsBusiness Intelligence and Information Technology10.1007/978-981-97-3980-6_25(283-293)Online publication date: 30-Aug-2024
https://doi.org/10.1007/978-981-97-3980-6_25
Fetahu BCohen NHaramaty ELewin-Eytan LRokhlenko OMalmasi S(2024)Identifying Shopping Intent in Product QA for Proactive RecommendationsAdvances on Graph-Based Approaches in Information Retrieval10.1007/978-3-031-71382-8_3(25-40)Online publication date: 10-Oct-2024
https://doi.org/10.1007/978-3-031-71382-8_3
Mina PSolon ISanchez FDelante TVillegas JBasay FAndales JPasko FEstrera MSamson Jr. RMutya R(2023)Leveraging education through artificial intelligence virtual assistance: A case study of visually impaired learnersPedagogical Research10.29333/pr/127358:1(em0150)Online publication date: 2023
https://doi.org/10.29333/pr/12735
Bouguettaya AZarzour HKechida ATaberkit A(2022)Machine Learning and Deep Learning as New Tools for Business AnalyticsHandbook of Research on Foundations and Applications of Intelligent Business Analytics10.4018/978-1-7998-9016-4.ch008(166-188)Online publication date: 2022
https://doi.org/10.4018/978-1-7998-9016-4.ch008
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents