Fulvio Corno
Luigi De Russis
Skip Abstract Section
Abstract
Nowadays, end users can personalize their smart devices and web applications by defining or reusing IF-THEN rules through dedicated End-User Development (EUD) tools. Despite apparent simplicity, such tools present their own set of issues. The emerging and increasing complexity of the Internet of Things, for example, is barely taken into account, and the number of possible combinations between triggers and actions of different smart devices and web applications is continuously growing. Such a large design space makes end-user personalization a complex task for non-programmers, and motivates the need of assisting users in easily discovering and managing rules and functionality, e.g., through recommendation techniques. In this article, we tackle the emerging problem of recommending IF-THEN rules to end users by presenting RecRules, a hybrid and semantic recommendation system. Through a mixed content and collaborative approach, the goal of RecRules is to recommend by functionality: it suggests rules based on their final purposes, thus overcoming details like manufacturers and brands. The algorithm uses a semantic reasoning process to enrich rules with semantic information, with the aim of uncovering hidden connections between rules in terms of shared functionality. Then, it builds a collaborative semantic graph, and it exploits different types of path-based features to train a learning to rank algorithm and compute top-N recommendations. We evaluate RecRules through different experiments on real user data extracted from IFTTT, one of the most popular EUD tools. Results are promising: they show the effectiveness of our approach with respect to other state-of-the-art algorithms and open the way for a new class of recommender systems for EUD that take into account the actual functionality needed by end users.
- Pierre A. Akiki, Arosha K. Bandara, and Yijun Yu. 2017. Visual simple transformations: Empowering end-users to wire Internet of Things objects. ACM Transactions on Computer-Human Interaction 24, 2, Article 10 (April 2017), 43 pages. Google Scholar
Digital Library
- Sarabjot Singh Anand, Patricia Kearney, and Mary Shapcott. 2007. Generating semantically enriched user profiles for web personalization. ACM Transactions on Internet Technology 7, 4, Article 22 (Oct. 2007). Google ScholarDigital Library
- C. Bizer and T. Heathand T. Berners-Lee. 2009. Linked data—The story so far. International Journal on Semantic Web and Information System 5, 3 (2009), 1--22.Google ScholarCross Ref
- Leo Breiman. 2001. Random forests. Machine Learning 45, 1 (Oct. 2001), 5--32. Google ScholarDigital Library
- Julia Brich, Marcel Walch, Michael Rietzler, Michael Weber, and Florian Schaub. 2017. Exploring end user programming needs in home automation. ACM Transaction on Computer-Human Interaction 24, 2, Article 11 (April 2017), 35 pages. Google ScholarDigital Library
- Iván Cantador, Alejandro Bellogín, and Pablo Castells. 2008. A multilayer ontology-based hybrid recommendation model. AI Communications 21, 2--3 (April 2008), 203--210. Google ScholarDigital Library
- Vint Cerf and Max Senges. 2016. Taking the Internet to the Next Physical Level. IEEE Computer 49, 2 (Feb. 2016), 80--86. Google ScholarDigital Library
- F. Corno, L. De Russis, and A. Monge Roffarello. 2017. A semantic web approach to simplifying trigger-action programming in the IoT. Computer 50, 11 (Nov. 2017), 18--24.Google ScholarCross Ref
- Fulvio Corno, Luigi De Russis, and Alberto Monge Roffarello. 2019. A high-level semantic approach to end-user development in the Internet of Things. International Journal of Human-Computer Studies 125 (2019), 41--54.Google ScholarDigital Library
- Allen Cypher. 1991. EAGER: Programming repetitive tasks by example. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’91). ACM, New York, NY, 33--39. Google ScholarDigital Library
- Jose Danado and Fabio Paternò. 2014. Puzzle: A mobile application development environment using a jigsaw metaphor. Journal of Visual Languages 8 Computing 25, 4 (2014), 297--315. Google ScholarDigital Library
- G. Desolda, C. Ardito, and M. Matera. 2017. Empowering end users to customize their smart environments: Model, composition paradigms, and domain-specific tools. ACM Transaction on Computer-Human Interaction (TOCHI) 24, 2, Article 12 (April 2017), 52 pages. Google ScholarDigital Library
- Anind K. Dey, Timothy Sohn, Sara Streng, and Justin Kodama. 2006. iCAP: Interactive prototyping of context-aware applications. In Proceedings of the 4th International Conference on Pervasive Computing (PERVASIVE’06). Springer-Verlag, Berlin, 254--271. Google ScholarDigital Library
- A. R. D’Souza, D. Yang, and C. V. Lopes. 2016. Collective intelligence for smarter API recommendations in python. In 2016 IEEE 16th International Working Conference on Source Code Analysis and Manipulation (SCAM). 51--60.Google Scholar
- Ekwa Duala-Ekoko and Martin P. Robillard. 2011. Using Structure-Based Recommendations to Facilitate Discoverability in APIs. Springer, Berlin, 79--104.Google ScholarDigital Library
- Ignacio Fernández-Tobías, Iván Cantador, Marius Kaminskas, and Francesco Ricci. 2011. A generic semantic-based framework for cross-domain recommendation. In Proceedings of the 2Nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec’11). ACM, New York, NY, 25--32. Google ScholarDigital Library
- Yoav Freund, Raj Iyer, Robert E. Schapire, and Yoram Singer. 2003. An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research 4 (Dec. 2003), 933--969. Google ScholarDigital Library
- J. Friedman, T. Hastie, and R. Tibshirani. 2010. Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software 33, 1 (2010), 1--22. http://www.jstatsoft.org/v33/i01/.Google ScholarCross Ref
- Z. Gantner, L. Drumond, C. Freudenthaler, S. Rendle, and L. Schmidt-Thieme. 2010. Learning attribute-to-feature mappings for cold-start recommendations. In Proceedings of the 2010 IEEE International Conference on Data Mining (ICDM’10). IEEE Computer Society, Washington, D.C., 176--185. Google ScholarDigital Library
- Zeno Gantner, Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2011. MyMediaLite: A free recommender system library. In Proceedings of the 5th ACM Conference on Recommender Systems (RecSys’11). ACM, New York, NY, 305--308. Google ScholarDigital Library
- Andrés Garcí-a silva, Oscar Corcho, Harith Alani, and Asunción Gómez-pérez. 2012. Review: Review of the state of the art: Discovering and associating semantics to tags in folksonomies. The Knowledge Engineering Review 27, 1 (Feb. 2012), 57--85. Google ScholarDigital Library
- G. Ghiani, M. Manca, F. Paternò, and C. Santoro. 2017. Personalization of context-dependent applications through trigger-action rules. ACM Transactions on Computer-Human Interaction (TOCHI) 24, 2, Article 14 (April 2017), 33 pages. Google ScholarDigital Library
- Birte Glimm, Ian Horrocks, Boris Motik, Giorgos Stoilos, and Zhe Wang. 2014. HermiT: An OWL 2 reasoner. Journal of Automated Reasoning 53, 3 (Oct. 2014), 245--269. Google ScholarDigital Library
- Will Haines, Melinda Gervasio, Aaron Spaulding, and Bart Peintner. 2010. Recommendations for end-user development. In Proceedings of the ACM RecSys 2010 Workshop on User-Centric Evaluation of Recommender Systems and Their Interfaces (UCERSTI).Google Scholar
- Mostafa Hamza and Robert J. Walker. 2015. Recommending features and feature relationships from requirements documents for software product lines. In Proceedings of the 4th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE’15). IEEE Press, Piscataway, NJ, 25--31. Google ScholarDigital Library
- Benjamin Heitmann and Conor Hayes. 2010. Using linked data to build open, collaborative recommender systems. Artificial Intelligence (2010), 76--81.Google Scholar
- Matthew Horridge and Sean Bechhofer. 2011. The OWL API: A java API for OWL ontologies. Semantic Web 2, 1 (Jan. 2011), 11--21. Google ScholarDigital Library
- Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative filtering for implicit feedback datasets. In Proceedings of the 2008 8th IEEE International Conference on Data Mining (ICDM’08). IEEE Computer Society, Washington, D.C., 263--272. Google ScholarDigital Library
- Justin Huang and Maya Cakmak. 2015. Supporting mental model accuracy in trigger-action programming. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp’15). ACM, New York, NY, 215--225. Google ScholarDigital Library
- Ting-Hao K. Huang, A. Azaria, and J. P. Bigham. 2016. InstructableCrowd: Creating IF-THEN rules via conversations with the crowd. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA’16). ACM, New York, NY, 1555--1562. Google ScholarDigital Library
- Houda Khrouf and Raphaël Troncy. 2013. Hybrid event recommendation using linked data and user diversity. In Proceedings of the 7th ACM Conference on Recommender Systems (RecSys’13). ACM, New York, NY, 185--192. Google ScholarDigital Library
- Jacob Krüger. 2018. When to extract features: Towards a recommender system. In Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings (ICSE’18). ACM, New York, NY, 518--520. Google ScholarDigital Library
- Ni Lao and William W. Cohen. 2010. Relational retrieval using a combination of path-constrained random walks. Machine Learning 81, 1 (Oct. 2010), 53--67. Google ScholarDigital Library
- Lukas Lerche and Dietmar Jannach. 2014. Using graded implicit feedback for Bayesian personalized ranking. In Proceedings of the 8th ACM Conference on Recommender Systems (RecSys’14). ACM, New York, NY, 353--356. Google ScholarDigital Library
- Henry Lieberman, Fabio Paternò, Markus Klann, and Volker Wulf. 2006. End User Development. Springer, Dordrecht, Netherlands, Chapter End-User Development: An Emerging Paradigm, 1--8.Google Scholar
- Tie-Yan Liu. 2009. Learning to rank for information retrieval. Foundations and Trends In Information Retrieval 3, 3 (March 2009), 225--331. Google ScholarDigital Library
- Y. Malheiros, A. Moraes, C. Trindade, and S. Meira. 2012. A source code recommender system to support newcomers. In 2012 IEEE 36th Annual Computer Software and Applications Conference. 19--24. Google ScholarDigital Library
- Toshiyuki Masui and Ken Nakayama. 1994. Repeat and predict: Two keys to efficient text editing. In Conference Companion on Human Factors in Computing Systems (CHI’94). ACM, New York, NY, 31--32. Google ScholarDigital Library
- Sean M. McNee, John Riedl, and Joseph A. Konstan. 2006. Being accurate is not enough: How accuracy metrics have hurt recommender systems. In CHI’06 Extended Abstracts on Human Factors in Computing Systems (CHI EA’06). ACM, New York, NY, 1097--1101. Google ScholarDigital Library
- Kim Mens and Angela Lozano. 2014. Source Code-Based Recommendation Systems. Springer, Berlin,, 93--130.Google Scholar
- Bamshad Mobasher, Xin Jin, and Yanzan Zhou. 2004. Web Mining: From Web to Semantic Web. Springer, Berlin, Chapter Semantically Enhanced Collaborative Filtering on the Web, 57--76.Google Scholar
- Anh Tuan Nguyen, Michael Hilton, Mihai Codoban, Hoan Anh Nguyen, Lily Mast, Eli Rademacher, Tien N. Nguyen, and Danny Dig. 2016. API code recommendation using statistical learning from fine-grained changes. In Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE 2016). ACM, New York, NY, 511--522. Google ScholarDigital Library
- X. Ning and G. Karypis. 2011. SLIM: Sparse linear methods for top-N recommender systems. In 2011 IEEE 11th International Conference on Data Mining. 497--506. Google ScholarDigital Library
- Xia Ning and George Karypis. 2012. Sparse linear methods with side information for top-n recommendations. In Proceedings of the 6th ACM Conference on Recommender Systems (RecSys’12). ACM, New York, NY, 155--162. Google ScholarDigital Library
- Tommaso Di Noia, Vito Claudio Ostuni, Paolo Tomeo, and Eugenio Di Sciascio. 2016. SPrank: Semantic path-based ranking for top-N recommendations using linked open data. ACM Transactions on Intelligent Systems and Technology 8, 1, Article 9 (Sept. 2016), 34 pages. Google ScholarDigital Library
- Sergio Oramas, Vito Claudio Ostuni, Tommaso Di Noia, Xavier Serra, and Eugenio Di Sciascio. 2016. Sound and music recommendation with knowledge graphs. ACM Transactions on Intelligent Systems and Technology 8, 2, Article 21 (Oct. 2016), 21 pages. Google ScholarDigital Library
- Vito Claudio Ostuni, Tommaso Di Noia, Eugenio Di Sciascio, and Roberto Mirizzi. 2013. Top-N recommendations from implicit feedback leveraging linked open data. In Proceedings of the 7th ACM Conference on Recommender Systems (RecSys’13). ACM, New York, NY, 85--92. Google ScholarDigital Library
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI’09). AUAI Press, Arlington, Virginia, 452--461. Google ScholarDigital Library
- Giovanni Semeraro, Pasquale Lops, Pierpaolo Basile, and Marco de Gemmis. 2009. Knowledge infusion into content-based recommender systems. In Proceedings of the 3rd ACM Conference on Recommender Systems (RecSys’09). ACM, New York, NY, 301--304. Google ScholarDigital Library
- W. Shen, J. Wang, and J. Han. 2015. Entity linking with a knowledge base: Issues, techniques, and solutions. IEEE Transactions on Knowledge and Data Engineering 27, 2 (Feb. 2015), 443--460.Google ScholarCross Ref
- Yue Shi, Alexandros Karatzoglou, Linas Baltrunas, Martha Larson, Alan Hanjalic, and Nuria Oliver. 2012. TFMAP: Optimizing MAP for top-n context-aware recommendation. In Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’12). ACM, New York, NY, 155--164. Google ScholarDigital Library
- Harald Steck. 2013. Evaluation of recommendations: Rating-prediction and ranking. In Proceedings of the 7th ACM Conference on Recommender Systems (RecSys’13). ACM, New York, NY, 213--220. Google ScholarDigital Library
- Zhu Sun, Jie Yang, Jie Zhang, Alessandro Bozzon, Long-Kai Huang, and Chi Xu. 2018. Recurrent knowledge graph embedding for effective recommendation. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys’18). ACM, New York, NY, 297--305. Google ScholarDigital Library
- Blase Ur, Elyse McManus, Melwyn Pak Yong Ho, and Michael L. Littman. 2014. Practical trigger-action programming in the smart home. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’14). ACM, New York, NY, 803--812. Google ScholarDigital Library
- B. Ur, M. Pak Yong Ho, S. Brawner, J. Lee, S. Mennicken, N. Picard, D. Schulze, and M. L. Littman. 2016. Trigger-action programming in the wild: An analysis of 200,000 IFTTT recipes. In Proceedings of the 34rd Annual ACM Conference on Human Factors in Computing Systems (CHI’16). ACM, New York, NY, 3227--3231. Google ScholarDigital Library
- Markus Weimer, Alexandros Karatzoglou, and Alex Smola. 2008. Improving maximum margin matrix factorization. Machine Learning 72, 3 (Sept. 2008), 263--276. Google ScholarDigital Library
- Qiang Wu, Christopher J. Burges, Krysta M. Svore, and Jianfeng Gao. 2010. Adapting boosting for information retrieval measures. Information Retrieval Journal 13, 3 (June 2010), 254--270. Google ScholarDigital Library
- Yao Wu, Christopher DuBois, Alice X. Zheng, and Martin Ester. 2016. Collaborative denoising auto-encoders for top-N recommender systems. In Proceedings of the 9th ACM International Conference on Web Search and Data Mining (WSDM’16). ACM, New York, NY, 153--162. Google ScholarDigital Library
- Lina Yao, Quan Z. Sheng, Anne H. H. Ngu, Helen Ashman, and Xue Li. 2014. Exploring recommendations in Internet of Things. In Proceedings of the 37th International ACM SIGIR Conference on Research 8 Development in Information Retrieval (SIGIR’14). ACM, New York, NY, 855--858. Google ScholarDigital Library
- Y. Ye and G. Fischer. 2002. Supporting reuse by delivering task-relevant and personalized information. In Proceedings of the 24th International Conference on Software Engineering. ICSE 2002. 513--523. Google ScholarDigital Library
- Xiao Yu, Xiang Ren, Yizhou Sun, Quanquan Gu, Bradley Sturt, Urvashi Khandelwal, Brandon Norick, and Jiawei Han. 2014. Personalized entity recommendation: A heterogeneous information network approach. In Proceedings of the 7th ACM International Conference on Web Search and Data Mining (WSDM’14). ACM, New York, NY, 283--292. Google ScholarDigital Library
- Yuan Cao Zhang, Diarmuid Ó Séaghdha, Daniele Quercia, and Tamas Jambor. 2012. Auralist: Introducing serendipity into music recommendation. In Proceedings of the 5th ACM International Conference on Web Search and Data Mining (WSDM’12). ACM, New York, NY, 13--22. Google ScholarDigital Library
- Cai-Nicolas Ziegler, Sean M. McNee, Joseph A. Konstan, and Georg Lausen. 2005. Improving recommendation lists through topic diversification. In Proceedings of the 14th International Conference on World Wide Web (WWW’05). ACM, New York, NY, 22--32. Google ScholarDigital Library
- Yue Shi, Alexandros Karatzoglou, Linas Baltrunas, Martha Larson, Nuria Oliver, and Alan Hanjalic. 2012. CLiMF: Learning to maximize reciprocal rank with collaborative less-is-more filtering. In Proceedings of the Sixth ACM Conference on Recommender Systems (RecSys’12). ACM, 139--146. Google ScholarDigital Library
Index Terms
- RecRules: Recommending IF-THEN Rules for End-User Development
Recommendations
From Users’ Intentions to IF-THEN Rules in the Internet of Things
In the Internet of Things era, users are willing to personalize the joint behavior of their connected entities, i.e., smart devices and online service, by means of trigger-action rules such as “IF the entrance Nest security camera detects a movement, THEN ...
TAPrec: supporting the composition of trigger-action rules through dynamic recommendations
IUI '20: Proceedings of the 25th International Conference on Intelligent User InterfacesNowadays, users can personalize the joint behavior of their connected entities, i.e., smart devices and online service, by means of trigger-action rules. As the number of supported technologies grows, however, so does the design space, i.e., the ...
Effects of Personalized and Aggregate Top-N Recommendation Lists on User Preference Ratings
Prior research has shown a robust effect of personalized product recommendations on user preference judgments for items. Specifically, the display of system-predicted preference ratings as item recommendations has been shown in multiple studies to bias ...
Comments