short-paper

Combining Rating and Review Data by Initializing Latent Factor Models with Topic Models for Top-N Recommendation

Authors:

Francisco J. Peña,

Diarmuid O'Reilly-Morgan,

Elias Z. Tragos,

Erika Duriakova,

Aonghus LawlorAuthors Info & Claims

RecSys '20: Proceedings of the 14th ACM Conference on Recommender Systems

Pages 438 - 443

https://doi.org/10.1145/3383313.3412207

Published: 22 September 2020 Publication History

Abstract

Nowadays we commonly have multiple sources of data associated with items. Users may provide numerical ratings, or implicit interactions, but may also provide textual reviews. Although many algorithms have been proposed to jointly learn a model over both interactions and textual data, there is room to improve the many factorization models that are proven to work well on interactions data, but are not designed to exploit textual information. Our focus in this work is to propose a simple, yet easily applicable and effective, method to incorporate review data into such factorization models. In particular, we propose to build the user and item embeddings within the topic space of a topic model learned from the review data. This has several advantages: we observe that initializing the user and item embeddings in topic space leads to faster convergence of the factorization algorithm to a model that out-performs models initialized randomly, or with other state-of-the-art initialization strategies. Moreover, constraining user and item factors to topic space allows for the learning of an interpretable model that users can visualise.

References

[1]

Syed Muhammad Atif, Sameer Qazi, and Nicolas Gillis. 2019. Improved SVD-based initialization for nonnegative matrix factorization using low-rank correction. Pattern Recognition Letters 122 (2019), 53 – 59. https://doi.org/10.1016/j.patrec.2019.02.018

Digital Library

[2]

Mark Belford, Brian Mac Namee, and Derek Greene. 2018. Stability of topic modeling via matrix factorization. Expert Systems with Applications 91 (2018), 159 – 169. https://doi.org/10.1016/j.eswa.2017.08.047

Digital Library

[3]

David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent Dirichlet Allocation. J. Mach. Learn. Res. 3 (March 2003), 993–1022.

[4]

C. Boutsidis and E. Gallopoulos. 2008. SVD based initialization: A head start for nonnegative matrix factorization. Pattern Recognition 41, 4 (2008), 1350 – 1362. https://doi.org/10.1016/j.patcog.2007.09.010

Digital Library

[5]

Rose Catherine and William Cohen. 2017. TransNets: Learning to Transform for Recommendation. In Proceedings of the Eleventh ACM Conference on Recommender Systems (Como, Italy) (RecSys ’17). Association for Computing Machinery, New York, NY, USA, 288–296. https://doi.org/10.1145/3109859.3109878

Digital Library

[6]

Chong Chen, Min Zhang, Yiqun Liu, and Shaoping Ma. 2018. Neural Attentional Rating Regression with Review-Level Explanations. In Proceedings of the 2018 World Wide Web Conference (Lyon, France) (WWW ’18). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 1583–1592. https://doi.org/10.1145/3178876.3186070

Digital Library

[7]

Li Chen, Guanliang Chen, and Feng Wang. 2015. Recommender Systems Based on User Reviews: The State of the Art. User Modeling and User-Adapted Interaction 25, 2 (June 2015), 99–154. https://doi.org/10.1007/s11257-015-9155-5

Digital Library

[8]

Zhiyong Cheng, Xiaojun Chang, Lei Zhu, Rose C. Kanjirathinkal, and Mohan Kankanhalli. 2019. MMALFM: Explainable Recommendation by Leveraging Reviews and Images. ACM Trans. Inf. Syst. 37, 2, Article 16 (Jan. 2019), 28 pages. https://doi.org/10.1145/3291060

Digital Library

[9]

Zhiyong Cheng, Ying Ding, Lei Zhu, and Mohan Kankanhalli. 2018. Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews. In Proceedings of the 2018 World Wide Web Conference (Lyon, France) (WWW ’18). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 639–648. https://doi.org/10.1145/3178876.3186145

Digital Library

[10]

Qiming Diao, Minghui Qiu, Chao-Yuan Wu, Alexander J. Smola, Jing Jiang, and Chong Wang. 2014. Jointly Modeling Aspects, Ratings and Sentiments for Movie Recommendation (JMARS). In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (New York, New York, USA) (KDD ’14). ACM, New York, NY, USA, 193–202. https://doi.org/10.1145/2623330.2623758

Digital Library

[11]

Ruihai Dong, Markus Schaal, Michael P. O’Mahony, Kevin McCarthy, and Barry Smyth. 2013. Opinionated Product Recommendation. In Case-Based Reasoning Research and Development, Sarah Jane Delany and Santiago Ontañón (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 44–58.

[12]

Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural Collaborative Filtering. In Proceedings of the 26th International Conference on World Wide Web (Perth, Australia) (WWW ’17). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 173–182. https://doi.org/10.1145/3038912.3052569

[13]

Balázs Hidasi and Domonkos Tikk. 2012. Enhancing Matrix Factorization through Initialization for Implicit Feedback Databases. In Proceedings of the 2nd Workshop on Context-Awareness in Retrieval and Recommendation (Lisbon, Portugal) (CaRR ’12). Association for Computing Machinery, New York, NY, USA, 2–9. https://doi.org/10.1145/2162102.2162104

Digital Library

[14]

Balazs Hidasi and Domonkos Tikk. 2013. Initializing Matrix Factorization Methods on Implicit Feedback Databases. 19, 12 (jun 2013), 1834–1853.

[15]

Thomas Hofmann. 2001. Unsupervised Learning by Probabilistic Latent Semantic Analysis. Mach. Learn. 42, 1/2 (Jan. 2001), 177–196. https://doi.org/10.1023/A:1007617005950

[16]

Yunfeng Hou, Ning Yang, Yi Wu, and Philip S. Yu. 2019. Explainable recommendation with fusion of aspect information. World Wide Web 22, 1 (01 Jan 2019), 221–240. https://doi.org/10.1007/s11280-018-0558-1

[17]

Binbin Hu, Chuan Shi, Wayne Xin Zhao, and Philip S. Yu. 2018. Leveraging Meta-Path Based Context for Top- N Recommendation with A Neural Co-Attention Model. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (London, United Kingdom) (KDD ’18). Association for Computing Machinery, New York, NY, USA, 1531–1540. https://doi.org/10.1145/3219819.3219965

Digital Library

[18]

Y. Hu, Y. Koren, and C. Volinsky. 2008. Collaborative Filtering for Implicit Feedback Datasets. In 2008 Eighth IEEE International Conference on Data Mining. 263–272. https://doi.org/10.1109/ICDM.2008.22

[19]

Michael Jahrer and Andreas Töscher. 2011. Collaborative Filtering Ensemble for Ranking. In Proceedings of the 2011 International Conference on KDD Cup 2011 - Volume 18(KDDCUP’11). JMLR.org, 153–167.

[20]

Kalervo Järvelin and Jaana Kekäläinen. 2000. IR Evaluation Methods for Retrieving Highly Relevant Documents. In Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (Athens, Greece) (SIGIR ’00). Association for Computing Machinery, New York, NY, USA, 41–48. https://doi.org/10.1145/345508.345545

Digital Library

[21]

Yehuda Koren. 2008. Factorization Meets the Neighborhood: A Multifaceted Collaborative Filtering Model. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Las Vegas, Nevada, USA) (KDD ’08). Association for Computing Machinery, New York, NY, USA, 426–434. https://doi.org/10.1145/1401890.1401944

Digital Library

[22]

Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix Factorization Techniques for Recommender Systems. Computer 42, 8 (Aug. 2009), 30–37. https://doi.org/10.1109/MC.2009.263

Digital Library

[23]

Amy N Langville, Carl D Meyer, Russell Albright, James Cox, and David Duling. 2014. Algorithms, initializations, and convergence for the nonnegative matrix factorization. arXiv preprint arXiv:1407.7299(2014).

[24]

Daniel D Lee and H. Sebastian Seung. 1999. Learning the parts of objects by non-negative matrix factorization. Nature 401 (10 1999), 788–791. https://doi.org/10.1038/44565

[25]

Guang Ling, Michael R. Lyu, and Irwin King. 2014. Ratings Meet Reviews, a Combined Approach to Recommend. In Proceedings of the 8th ACM Conference on Recommender Systems (Foster City, Silicon Valley, California, USA) (RecSys ’14). ACM, New York, NY, USA, 105–112. https://doi.org/10.1145/2645710.2645728

[26]

Dionisis Margaris, Costas Vassilakis, and Dimitris Spiliotopoulos. 2020. What makes a review a reliable rating in recommender systems?Information Processing & Management 57, 6 (2020), 102304. https://doi.org/10.1016/j.ipm.2020.102304

[27]

Julian McAuley and Jure Leskovec. 2013. Hidden Factors and Hidden Topics: Understanding Rating Dimensions with Review Text. In Proceedings of the 7th ACM Conference on Recommender Systems (Hong Kong, China) (RecSys ’13). ACM, New York, NY, USA, 165–172. https://doi.org/10.1145/2507157.2507163

Digital Library

[28]

Mahdi Nasiri and Behrouz Minaei. 2016. Increasing Prediction Accuracy in Collaborative Filtering with Initialized Factor Matrices. J. Supercomput. 72, 6 (June 2016), 2157–2169. https://doi.org/10.1007/s11227-016-1717-8

Digital Library

[29]

Francisco J. Peña. 2017. Unsupervised Context-Driven Recommendations Based On User Reviews. In Proceedings of the Eleventh ACM Conference on Recommender Systems (Como, Italy) (RecSys ’17). ACM, New York, NY, USA, 426–430. https://doi.org/10.1145/3109859.3109865

Digital Library

[30]

Francisco J. Peña and Derek Bridge. 2017. Recommending from Experience. In Proceedings of the Thirtieth Florida Artificial Intelligence Research Society Conference. https://aaai.org/ocs/index.php/FLAIRS/FLAIRS17/paper/view/15439

[31]

Lin Qiu, Sheng Gao, Wenlong Cheng, and Jun Guo. 2016. Aspect-based latent factor model by integrating ratings and reviews for recommender system. Knowledge-Based Systems 110 (2016), 233 – 243. https://doi.org/10.1016/j.knosys.2016.07.033

Digital Library

[32]

Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian Personalized Ranking from Implicit Feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (Montreal, Quebec, Canada) (UAI ’09). AUAI Press, Arlington, Virginia, USA, 452–461.

Digital Library

[33]

Chong Wang and David M. Blei. 2011. Collaborative Topic Modeling for Recommending Scientific Articles. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(San Diego, California, USA) (KDD ’11). ACM, New York, NY, USA, 448–456. https://doi.org/10.1145/2020408.2020480

Digital Library

[34]

Yongfeng Zhang, Qingyao Ai, Xu Chen, and W. Bruce Croft. 2017. Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (Singapore, Singapore) (CIKM ’17). Association for Computing Machinery, New York, NY, USA, 1449–1458. https://doi.org/10.1145/3132847.3132892

Digital Library

[35]

Lei Zheng, Vahid Noroozi, and Philip S. Yu. 2017. Joint Deep Modeling of Users and Items Using Reviews for Recommendation. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining(Cambridge, United Kingdom) (WSDM ’17). Association for Computing Machinery, New York, NY, USA, 425–434. https://doi.org/10.1145/3018661.3018665

Digital Library

Cited By

Che SLiu HLiu S(2024)Tagging Items with Emerging Tags: A Neural Topic Model Based Few-Shot Learning ApproachACM Transactions on Information Systems10.1145/364185942:4(1-37)Online publication date: 23-Jan-2024
https://dl.acm.org/doi/10.1145/3641859
Yılmaz Yİşlek İÖğüdücü Ş(2024)Topic Modeling Enhanced Tripartite Graph for Recommendation Using Metapaths2024 9th International Conference on Computer Science and Engineering (UBMK)10.1109/UBMK63289.2024.10773526(1144-1149)Online publication date: 26-Oct-2024
https://doi.org/10.1109/UBMK63289.2024.10773526
Gheewala SXu SYeom S(2024)Deep shared learning and attentive domain mapping for cross-domain recommendationUser Modeling and User-Adapted Interaction10.1007/s11257-024-09416-yOnline publication date: 27-Sep-2024
https://doi.org/10.1007/s11257-024-09416-y
Show More Cited By

Combining Rating and Review Data by Initializing Latent Factor Models with Topic Models for Top-N Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals

Recommendations

Improving Latent Factor Models via Personalized Feature Projection for One Class Recommendation
CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

Latent Factor models, which transform both users and items into the same latent feature space, are one of the most successful and ubiquitous models in recommender systems. Most existing models in this paradigm define both users' and items' latent ...
Personalised rating prediction for new users using latent factor models
HT '11: Proceedings of the 22nd ACM conference on Hypertext and hypermedia

In recent years, personalised recommendations have gained importance in helping users deal with the abundance of information available online. Personalised recommendations are often based on rating predictions, and thus accurate rating prediction is ...
A Fine-Grained Latent Aspects Model for Recommendation: Combining Each Rating with Its Associated Review
Web Information Systems Engineering – WISE 2017
Abstract
Recently, several approaches simultaneously exploiting ratings and review texts have been proposed for personalized recommendations. These approaches apply topic modeling techniques on review texts to mining major latent aspects of the item (or ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

RecSys '20: Proceedings of the 14th ACM Conference on Recommender Systems

September 2020

796 pages

ISBN:9781450375832

DOI:10.1145/3383313

Copyright © 2020 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 the author(s) 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].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 September 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Short-paper
Research
Refereed limited

Funding Sources

Science Foundation Ireland

Conference

RecSys '20

Sponsor:

RecSys '20: Fourteenth ACM Conference on Recommender Systems

September 22 - 26, 2020

Virtual Event, Brazil

Acceptance Rates

Overall Acceptance Rate 254 of 1,295 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

16
Total Citations
View Citations
1,023
Total Downloads

Downloads (Last 12 months)36
Downloads (Last 6 weeks)4

Reflects downloads up to 25 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Che SLiu HLiu S(2024)Tagging Items with Emerging Tags: A Neural Topic Model Based Few-Shot Learning ApproachACM Transactions on Information Systems10.1145/364185942:4(1-37)Online publication date: 23-Jan-2024
https://dl.acm.org/doi/10.1145/3641859
Yılmaz Yİşlek İÖğüdücü Ş(2024)Topic Modeling Enhanced Tripartite Graph for Recommendation Using Metapaths2024 9th International Conference on Computer Science and Engineering (UBMK)10.1109/UBMK63289.2024.10773526(1144-1149)Online publication date: 26-Oct-2024
https://doi.org/10.1109/UBMK63289.2024.10773526
Gheewala SXu SYeom S(2024)Deep shared learning and attentive domain mapping for cross-domain recommendationUser Modeling and User-Adapted Interaction10.1007/s11257-024-09416-yOnline publication date: 27-Sep-2024
https://doi.org/10.1007/s11257-024-09416-y
Bittencourt GFonseca GAndrade YSilva NRocha L(2023)A Survey on Review - Aware Recommendation SystemsProceedings of the 29th Brazilian Symposium on Multimedia and the Web10.1145/3617023.3617050(198-207)Online publication date: 23-Oct-2023
https://dl.acm.org/doi/10.1145/3617023.3617050
Coscrato VBridge D(2023)Estimating and Evaluating the Uncertainty of Rating Predictions and Top-n Recommendations in Recommender SystemsACM Transactions on Recommender Systems10.1145/35840211:2(1-34)Online publication date: 24-Apr-2023
https://dl.acm.org/doi/10.1145/3584021
Coscrato VBridge D(2023)Recommendation Uncertainty in Implicit Feedback Recommender SystemsArtificial Intelligence and Cognitive Science10.1007/978-3-031-26438-2_22(279-291)Online publication date: 23-Feb-2023
https://doi.org/10.1007/978-3-031-26438-2_22
Liu YMiyazaki JChang QHong JBures MPark JCerny T(2022)Knowledge graph enhanced multi-task learning between reviews and ratings for movie recommendationProceedings of the 37th ACM/SIGAPP Symposium on Applied Computing10.1145/3477314.3507072(1882-1889)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3477314.3507072
Lin ZZang SWang RSun ZSenthilnath JXu CKwoh C(2022)Attention Over Self-Attention: Intention-Aware Re-Ranking With Dynamic Transformer Encoders for RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3208633(1-12)Online publication date: 2022
https://doi.org/10.1109/TKDE.2022.3208633
Mauro NArdissono LCocomazzi SCena F(2022)Using consumer feedback from location-based services in PoI recommender systems for people with autismExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.116972199:COnline publication date: 23-May-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.116972
Jiang RFeng SZhang SLi XYao YZhang H(2022)A personalized recommendation method based on collaborative ranking with random walkMultimedia Tools and Applications10.1007/s11042-022-11980-781:5(7345-7363)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s11042-022-11980-7
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.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Table of Contents