Behnam Rahdari
Branislav Kveton
Peter Brusilovsky
ABSTRACT
Carousel-based interfaces with multiple topic-focused item lists have emerged as a de-facto standard for presenting recommendation results to end-users in real-life recommender systems. In this paper, we attempt to formalize and explain the “magic” power of carousel-based interfaces from a traditional hypertext prospect of navigability. By applying both, formal analysis and a data-driven evaluation, we demonstrate and measure the benefits offered by the carousel-based organization of recommendations. We hope that this work will benefit the researchers in both hypertext and recommender systems communities, where the research on carousel-based interfaces is gaining popularity.
Supplemental Material
- Gediminas Adomavicius, Bamshad Mobasher, Francesco Ricci, and Alexander Tuzhilin. 2011. Context-Aware Recommender Systems. AI Magazine 32, 3 (2011), 67–80.Google Scholar
Digital Library
- Gediminas Adomavicius, Ramesh Sankaranarayanan, Shahana Sen, and Alexander Tuzhilin. 2005. Incorporating Contextual Information in Recommender Systems Using a Multidimensional Approach. ACM Transactions on Information Systems. 23, 1 (2005), 103–145.Google ScholarDigital Library
- Eugene Agichtein, Eric Brill, Susan Dumais, and Robert Ragno. 2006. Learning User Interaction Models for Predicting Web Search Result Preferences. In Proceedings of the 29th Annual International ACM SIGIR Conference. Association for Computing Machinery, 3–10.Google ScholarDigital Library
- Fedor Bakalov, Marie-Jean Meurs, Birgitta König-Ries, Bahar Sateli, Rene Witte, Greg Butler, and Adrian Tsang. 2013. An Approach to Controlling User Models and Personalization Effects in Recommender Systems. In international conference on Intelligent user interfaces, IUI ’2013. ACM Press, 49–56.Google ScholarDigital Library
- Walid Bendada, Guillaume Salha, and Bontempelli. 2020. Carousel Personalization in Music Streaming Apps with Contextual Bandits. In Fourteenth ACM Conference on Recommender Systems. ACM, 420–425.Google Scholar
- Svetlin Bostandjiev, John O’Donovan, and Tobias Höllerer. 2012. TasteWeights: A Visual Interactive Hybrid Recommender System. In 6th ACM Conference on Recommender System. 35–42.Google ScholarDigital Library
- Sergey Brin and Lawrence Page. 1998. The anatomy of a large-scale hypertextual (Web) search engine. In Seventh International World Wide Web Conference, Helen Ashman and Paul Thistewaite (Eds.). Vol. 30. Elsevier Science B. V., 107–117.Google ScholarDigital Library
- Robin Burke, Kristian Hammond, and Benjamin C. Young. 1997. The FindMe Approach to Assisted Browsing. IEEE Intelligent Systems 12, 4 (1997), 32—40.Google ScholarDigital Library
- Olivier Chapelle and Ya Zhang. 2009. A Dynamic Bayesian Network Click Model for Web Search Ranking. In Proceedings of the 18th International Conference on World Wide Web. 1–10.Google ScholarDigital Library
- Li Chen and Pearl Pu. 2012. Critiquing-based recommenders: survey and emerging trends. User Modeling and User-Adapted Interaction 22, 1 (2012), 125–150.Google ScholarDigital Library
- Ed H. Chi, Lichan Hong, Julie Heiser, and Stuart K. Card. 2006. ScentIndex: Conceptually Reorganizing Subject Indexes for Reading. In IEEE Symposium on Visual Analytics Science and Technology, VAST 2006, Pak Chung Wong and Daniel Keim (Eds.). IEEE, 159–166.Google Scholar
- Ed H. Chi and Todd Mytkowicz. 2008. Understanding the Efficiency of Social Tagging Systems using Information Theory. In The 19th ACM Conference on Hypertext and Hypermedia. 81–88.Google Scholar
- Aleksandr Chuklin, Ilya Markov, and Maarten de Rijke. 2015. Click Models for Web Search. Morgan & Claypool Publishers.Google Scholar
- Richard Combes, Stefan Magureanu, Alexandre Proutiere, and Cyrille Laroche. 2015. Learning to Rank: Regret Lower Bounds and Efficient Algorithms. In Proceedings of the 2015 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems.Google ScholarDigital Library
- Nick Craswell, Onno Zoeter, Michael Taylor, and Bill Ramsey. 2008. An Experimental Comparison of Click Position-Bias Models. In Proceedings of the 1st ACM International Conference on Web Search and Data Mining. 87–94.Google ScholarDigital Library
- Nick Craswell, Onno Zoeter, Michael Taylor, and Bill Ramsey. 2008. An Experimental Comparison of Click Position-Bias Models. In Proceedings of the 2008 International Conference on Web Search and Data Mining (Palo Alto, California, USA) (WSDM ’08). Association for Computing Machinery, New York, NY, USA, 87–94. https://doi.org/10.1145/1341531.1341545Google ScholarDigital Library
- Dimitar Dimitrov, Denis Helic, and Markus Strohmaier. 2017. Tag-based navigation and visualization. LNCS, Vol. 10100. Springer, Heidelberg, in this volume.Google Scholar
- Nicolò Felicioni, Maurizio Ferrari Dacrema, and Paolo Cremonesi. 2021. A Methodology for the Offline Evaluation of Recommender Systems in a User Interface with Multiple Carousels. In Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization. ACM, 10–15.Google ScholarDigital Library
- Fan Guo, Chao Liu, Anitha Kannan, Tom Minka, Michael Taylor, Yi Min Wang, and Christos Faloutsos. 2009. Click Chain Model in Web Search. In Proceedings of the 18th International Conference on World Wide Web. 11–20.Google ScholarDigital Library
- Fan Guo, Chao Liu, and Yi Min Wang. 2009. Efficient Multiple-Click Models in Web Search. In Proceedings of the 2nd ACM International Conference on Web Search and Data Mining. 124–131.Google ScholarDigital Library
- F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Trans. Interact. Intell. Syst. 5, 4, Article 19 (dec 2015), 19 pages.Google ScholarDigital Library
- Taher H. Haveliwala, Sepandar D. Kamvar, and Glen Jeh. 2003. An Analytical Comparison of Approaches to Personalizing PageRank. Technical Report 2003-35. Stanford University. http://dbpubs.stanford.edu/pub/2003-35Google Scholar
- Chen He, Denis Parra, and Katrien Verbert. 2016. Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities. Expert Systems with Applications 56, C (2016), 59–27.Google Scholar
- Denis Helic, Sebastian Wilhelm, Ilire Hasani-Mavriqi, and Markus Strohmaier. 2011. The Effects of Navigation Tools on the Navigability of Web-based Information Systems. In Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies. ACM, 16:1–16:8. https://doi.org/10.1145/2024288.2024308Google ScholarDigital Library
- Tamas Jambor and Jun Wang. 2010. Optimizing Multiple Objectives in Collaborative Filtering. In 2010 ACM conference on Recommender systems, RecSys ’10. ACM, 55–62.Google Scholar
- Dietmar Jannach, Mathias Jesse, Michael Jugovac, and Christoph Trattner. 2021. Exploring Multi-List User Interfaces for Similar-Item Recommendations. In Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization. ACM, 224–228.Google ScholarDigital Library
- Jon Kleinberg. 1999. Authoritative sources in a hyperlinked environment. J. ACM 46, 5 (1999), 604–632.Google ScholarDigital Library
- Branislav Kveton, Csaba Szepesvari, Zheng Wen, and Azin Ashkan. 2015. Cascading Bandits: Learning to Rank in the Cascade Model. In Proceedings of the 32nd International Conference on Machine Learning.Google Scholar
- Shuai Li, Yasin Abbasi-Yadkori, Branislav Kveton, S. Muthukrishnan, Vishwa Vinay, and Zheng Wen. 2018. Offline Evaluation of Ranking Policies with Click Models. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1685–1694.Google ScholarDigital Library
- Denis Parra and Peter Brusilovsky. 2015. User-controllable personalization: A case study with SetFusion. International Journal of Human-Computer Studies 78 (2015), 43–67.Google ScholarDigital Library
- Behnam Rahdari, Peter Brusilovsky, and Dmitriy Babichenko. 2020. Personalizing Information Exploration with an Open User Model. In 31st ACM Conference on Hypertext and Social Media. ACM, 167–176.Google ScholarDigital Library
- Matthew Richardson, Ewa Dominowska, and Robert Ragno. 2007. Predicting Clicks: Estimating the Click-Through Rate for New Ads. In Proceedings of the 16th International Conference on World Wide Web. 521–530.Google ScholarDigital Library
- Dimitrios Skoutas and Mohammad Alrifai. 2011. Tag clouds revisited. In 20th ACM International Conference on Information and Knowledge Management, CIKM ’11. ACM, 221–230.Google ScholarDigital Library
- Barry Smyth, Lorraine McGinty, James Reilly, and Kevin McCarthy. 2004. Compound Critiques for Conversational Recommender Systems. In IEEE/WIC/ACM International Conference on Web Intelligence (WI’04). IEEE, 145–151.Google Scholar
- Alain Starke, Edis Asotic, and Christoph Trattner. 2021. “Serving Each User”: Supporting Different Eating Goals Through a Multi-List Recommender Interface. In Fifteenth ACM Conference on Recommender Systems. ACM, 124–132.Google ScholarDigital Library
- Christoph Trattner, Yi-Ling Lin, Denis Parra, Zhen Yue, William Real, and Peter Brusilovsky. 2012. Evaluating Tag-Based Information Access in Image Collections. In Proceedings of the 23rd ACM conference on Hypertext and hypermedia. ACM, New York, NY, USA, 113–122.Google ScholarDigital Library
- Mark van Setten, Stanislav Pokraev, and Johan Koolwaaij. 2004. Context-aware recommendations in the mobile tourist application COMPASS. In Third International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (AH’2004)(Lecture Notes in Computer Science, Vol. 3137), Paul De Bra and Wolfgang Nejdl (Eds.). Springer-Verlag, 235–244.Google ScholarCross Ref
- Saúl Vargas and Pablo Castells. 2011. Rank and Relevance in Novelty and Diversity Metrics for Recommender Systems. In the Fifth ACM Conference on Recommender Systems. ACM Press, 109–116.Google Scholar
- Petros Venetis, Georgia Koutrika, and Hector Garcia-Molina. 2011. On the selection of tags for tag cloudsx. In Fourth ACM International Conference on Web Search and Data Mining, WSDM ’11, Vol. 29. ACM, 835–844.Google ScholarDigital Library
- Jesse Vig, Shilad Sen, and John Riedl. 2012. The Tag Genome: Encoding Community Knowledge to Support Novel Interaction. ACM Transactions on Interactive Intelligent Systems 2, 3 (2012), Article 13.Google ScholarDigital Library
- Liang Wu, Mihajlo Grbovic, and Jundong Li. 2021. Toward User Engagement Optimization in 2D Presentation. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining. ACM, 1047–1055.Google ScholarDigital Library
- Ka-Ping Yee, Kirsten Swearingen, Kevin Li, and Marti A. Hearst. 2003. Faceted metadata for image search and browsing. In ACM Conference on Human Factors in Computing Systems, CHI 2003. ACM Press, 401–408. http://citeseer.ist.psu.edu/yee03faceted.htmlGoogle ScholarDigital Library
- Cai N. Ziegler, Sean M. McNee, Joseph Konstan, and Georg Lausen. 2005. Improving Recommendation Lists Through Topic Diversification. In 14th international conference on World Wide Web, WWW’2005. ACM Press, 25–32.Google Scholar
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