2011 Proceeding- Conference Chairs:
- Iván Cantador,
- Peter Brusilovsky,
- Tsvi Kuflik
The 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec 2011) was held in Chicago, IL, USA, on the 27th of October 2011, under the frame of the 5th ACM Conference on Recommender Systems (RecSys 2011).
HetRec workshop represented a meeting point for researchers and practitioners interested in addressing the challenges posed by information heterogeneity in recommender systems and studying information fusion in this context.
In this second edition, we made available on-line datasets with heterogeneous information from several social systems. These datasets could be used by participants to experiment and evaluate their recommendation approaches, and can be enriched with additional data, which may be published at the workshop website for future use. These datasets are kindly hosted by GroupLens research group at University of Minnesota.
A total of ten papers were presented at the workshop. There were six long papers, and four short papers. Fourteen submissions were received, and each of them was reviewed by two members of the Program Committee.
Proceeding Downloads
Information market based recommender systems fusion
Recommender Systems have emerged as a way to tackle the overload of information reflected in the increasing volume of information artefacts in the web and elsewhere. Recommender Systems analyse existing information on the user activities in order to ...
A kernel-based approach to exploiting interaction-networks in heterogeneous information sources for improved recommender systems
Pairwise interaction networks capture inter-user dependencies (e.g. social networks) and inter-item dependencies (e.g item categories) that provide insight into user and item behavior. It is often assumed that such interaction information is informative ...
Learning multiple models for exploiting predictive heterogeneity in recommender systems
Collaborative filtering approaches exploit information about historical affinities or ratings to predict unknown affinities between sets of "users" and "items" and make recommendations. However a model that also incorporates heterogeneous sources of ...
A generic semantic-based framework for cross-domain recommendation
In this paper, we present an ongoing research work on the design and development of a generic knowledge-based description framework built upon semantic networks. It aims at integrating and exploiting knowledge on several domains to provide cross-domain ...
Hybrid algorithms for recommending new items
Despite recommender systems based on collaborative filtering typically outperform content-based systems in terms of recommendation quality, they suffer from the new item problem, i.e., they are not able to recommend items that have few or no ratings. ...
Expert recommendation based on social drivers, social network analysis, and semantic data representation
Knowledge networks and recommender systems are especially important for expert finding within organizations and scientific communities. Useful recommendation of experts, however, is not an easy task for many reasons: It requires reasoning about multiple ...
Experience Discovery: hybrid recommendation of student activities using social network data
The aim of the Experience Discovery project is to recommend extracurricular activities to high school and middle school students in urban areas. In implementing this system, we have been able to make use of both usage data and data drawn from a social ...
Personalizing tags: a folksonomy-like approach for recommending movies
Movie recommender systems attempt to find movies which are of interest for their users. However, as new movies are added, and new users join movie recommendation services, the problem of recommending suitable items becomes increasingly harder. In this ...
Personalized pricing recommender system: multi-stage epsilon-greedy approach
Many e-commerce sites use recommender systems, which suggest items that customers prefer. Though recommender systems have achieved great success, their potential is not yet fulfilled. One weakness of current systems is that the actions of the system ...
Matrix co-factorization for recommendation with rich side information and implicit feedback
Most recommender systems focus on the areas of leisure activities. As the Web evolves into omnipresent utility, recommender systems penetrate more serious applications such as those in online scientific communities. In this paper, we investigate the ...
Cited By
-
Zhang H, Wang D, Sun Z, Li Y, Sun Y, Liang H and Wang W (2025). KG4RecEval: Does Knowledge Graph Really Matter for Recommender Systems?, ACM Transactions on Information Systems, 10.1145/3713071
- Li Z, Liu M and Lui J FedConPE Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, (4533-4541)
- Rampisela T, Ruotsalo T, Maistro M and Lioma C Can We Trust Recommender System Fairness Evaluation? The Role of Fairness and Relevance Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, (271-281)
- Jin X, Zhou W, Wang J, XU D and Zheng Y An Order-Complexity Aesthetic Assessment Model for Aesthetic-aware Music Recommendation Proceedings of the 31st ACM International Conference on Multimedia, (6938-6947)
- Wang X, Pan Z, Zhou Y, Chen H, Ge C and Zhu W Curriculum co-disentangled representation learning across multiple environments for social recommendation Proceedings of the 40th International Conference on Machine Learning, (36174-36192)
- Zhuo J, Lian J, Xu L, Gong M, Shou L, Jiang D, Xie X and Yue Y Tiger: Transferable Interest Graph Embedding for Domain-Level Zero-Shot Recommendation Proceedings of the 31st ACM International Conference on Information & Knowledge Management, (2806-2816)
- Petrov A and Macdonald C A Systematic Review and Replicability Study of BERT4Rec for Sequential Recommendation Proceedings of the 16th ACM Conference on Recommender Systems, (436-447)
- Zhang Y, Li C, Xie X, Wang X, Shi C, Liu Y, Sun H, Zhang L, Deng W and Zhang Q Geometric Disentangled Collaborative Filtering Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, (80-90)
- Huang Z and Chen S (2022). Density-optimized intersection-free mapping and matrix multiplication for join-project operations, Proceedings of the VLDB Endowment, 15:10, (2244-2256), Online publication date: 1-Jun-2022.
- Zhang Y, Wang X, Liu N and Shi C (2021). Embedding Heterogeneous Information Network in Hyperbolic Spaces, ACM Transactions on Knowledge Discovery from Data, 16:2, (1-23), Online publication date: 30-Apr-2022.
- Montanari M, Bernardis C and Cremonesi P On the impact of data sampling on hyper-parameter optimisation of recommendation algorithms Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing, (1399-1402)
- Dervishaj E and Cremonesi P GAN-based matrix factorization for recommender systems Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing, (1373-1381)
- Zhang M, Ren Z, Wang Z, Ren P, Chen Z, Hu P and Zhang Y Membership Inference Attacks Against Recommender Systems Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security, (864-879)
-
Wu J, Zhao C, Yu T, Li J and Li S (2021). Clustering of Conversational Bandits for User Preference Learning and Elicitation CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, 10.1145/3459637.3482328, 9781450384469, (2129-2139), Online publication date: 26-Oct-2021.
- Khan F, Flanagan A, Tan K, Alamgir Z and Ammad-ud-din M A Payload Optimization Method for Federated Recommender Systems Proceedings of the 15th ACM Conference on Recommender Systems, (432-442)
- Lv Q, Ding M, Liu Q, Chen Y, Feng W, He S, Zhou C, Jiang J, Dong Y and Tang J Are we really making much progress? Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, (1150-1160)
- Du B, Liu L and Tong H Sylvester Tensor Equation for Multi-Way Association Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, (311-321)
- Gabbolini G, D'Amico E, Bernardis C and Cremonesi P On the instability of embeddings for recommender systems Proceedings of the 36th Annual ACM Symposium on Applied Computing, (1363-1370)
- Chen B, Guo W, Tang R, Xin X, Ding Y, He X and Wang D TGCN: Tag Graph Convolutional Network for Tag-Aware Recommendation Proceedings of the 29th ACM International Conference on Information & Knowledge Management, (155-164)
- Zhu Z, Sefati S, Saadatpanah P and Caverlee J Recommendation for New Users and New Items via Randomized Training and Mixture-of-Experts Transformation Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, (1121-1130)
- Lin Y, Ren P, Chen Z, Ren Z, Yu D, Ma J, Rijke M and Cheng X Meta Matrix Factorization for Federated Rating Predictions Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, (981-990)
- Fu X, Zhang J, Meng Z and King I MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding Proceedings of The Web Conference 2020, (2331-2341)
- Akbar M, Huang Z, Yu Z, Mehmood F, Hussain Y and Hamza M Towards continues code recommendation and implementation system Proceedings of the 24th International Conference on Evaluation and Assessment in Software Engineering, (439-444)
- Silva F and Cravino N Improving on Neighborhood based Recommendations with a Small World Network derived Novelty metric IEEE/WIC/ACM International Conference on Web Intelligence - Companion Volume, (119-126)
- Dara S and Chowdary C (2022). A study on the role of flexible preferences in group recommendations, Applied Intelligence, 49:9, (3293-3307), Online publication date: 1-Sep-2019.
- Sajadmanesh S, Bazargani S, Zhang J and Rabiee H (2019). Continuous-Time Relationship Prediction in Dynamic Heterogeneous Information Networks, ACM Transactions on Knowledge Discovery from Data, 13:4, (1-31), Online publication date: 31-Aug-2019.
- Wang X, Zhang Y and Shi C Hyperbolic heterogeneous information network embedding Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, (5337-5344)
-
Meng Y, Naeem M, Ali R and Kim B EHCP: An Efficient Hybrid Content Placement Strategy in Named Data Network Caching, IEEE Access, 10.1109/ACCESS.2019.2946184, 7, (155601-155611)
-
Milani Fard A, Bagheri E and Wang K (2019). Relationship Prediction in Dynamic Heterogeneous Information Networks Advances in Information Retrieval, 10.1007/978-3-030-15712-8_2, (19-34),
-
Yang K and Toni L (2018). GRAPH-BASED RECOMMENDATION SYSTEM 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 10.1109/GlobalSIP.2018.8646359, 978-1-7281-1295-4, (798-802)
- Manca M, Boratto L and Carta S (2018). Behavioral data mining to produce novel and serendipitous friend recommendations in a social bookmarking system, Information Systems Frontiers, 20:4, (825-839), Online publication date: 1-Aug-2018.
- Miraftabzadeh S, Rad P, Jamshidi M and Prevost J Customer Review Analytics using Subjective Loss Function for Conceptual-based Learning 2018 13th Annual Conference on System of Systems Engineering (SoSE), (211-218)
- Kralj J, Robnik-źIkonja M and Lavraăź N (2018). HINMINE, Journal of Intelligent Information Systems, 50:1, (29-61), Online publication date: 1-Feb-2018.
- Liu J, Wu C and Wang J (2018). Gated recurrent units based neural network for time heterogeneous feedback recommendation, Information Sciences: an International Journal, 423:C, (50-65), Online publication date: 1-Jan-2018.
-
Silva A, da Silva Costa F, Diaz A and Peres S (2018). Exploring Coclustering for Serendipity Improvement in Content-Based Recommendation Intelligent Data Engineering and Automated Learning – IDEAL 2018, 10.1007/978-3-030-03493-1_34, (317-327),
- Park J, Sharma H, Mahajan D, Kim J, Olds P and Esmaeilzadeh H Scale-out acceleration for machine learning Proceedings of the 50th Annual IEEE/ACM International Symposium on Microarchitecture, (367-381)
- Xu Z, Lukasiewicz T, Chen C, Miao Y and Meng X Tag-aware personalized recommendation using a hybrid deep model Proceedings of the 26th International Joint Conference on Artificial Intelligence, (3196-3202)
-
Zhu T, Peng Z, Wang S, Yu P and Hong X (2017). Measuring the relevance of different-typed objects in weighted signed heterogeneous information networks 2017 IEEE 21st International Conference on Computer Supported Cooperative Work in Design (CSCWD), 10.1109/CSCWD.2017.8066754, 978-1-5090-6199-0, (556-561)
- Yogev S, Shani G and Tractinsky N HiveRel Proceedings of the 2017 ACM Workshop on Exploratory Search and Interactive Data Analytics, (17-24)
- Wu C, Wang J, Liu J and Liu W (2016). Recurrent neural network based recommendation for time heterogeneous feedback, Knowledge-Based Systems, 109:C, (90-103), Online publication date: 1-Oct-2016.
- Manzato M, Domingues M, Fortes A, Sundermann C, D'addio R, Conrado M, Rezende S and Pimentel M (2016). Mining unstructured content for recommender systems, Information Retrieval, 19:4, (378-415), Online publication date: 1-Aug-2016.
- Zahra S, Ghazanfar M, Khalid A, Azam M, Naeem U and Prugel-Bennett A (2015). Novel centroid selection approaches for KMeans-clustering based recommender systems, Information Sciences: an International Journal, 320:C, (156-189), Online publication date: 1-Nov-2015.
- de Gemmis M, Lops P, Semeraro G and Musto C (2015). An investigation on the serendipity problem in recommender systems, Information Processing and Management: an International Journal, 51:5, (695-717), Online publication date: 1-Sep-2015.
- Liu X Modeling users' dynamic preference for personalized recommendation Proceedings of the 24th International Conference on Artificial Intelligence, (1785-1791)
- Wu H, Pei Y, Li B, Kang Z, Liu X and Li H (2015). Item recommendation in collaborative tagging systems via heuristic data fusion, Knowledge-Based Systems, 75:C, (124-140), Online publication date: 1-Feb-2015.
- Movahedian H and Khayyambashi M (2015). A semantic recommender system based on frequent tag pattern, Intelligent Data Analysis, 19:1, (109-126), Online publication date: 1-Jan-2015.
- Stanescu A, Nagar S and Caragea D A Hybrid Recommender System Proceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 01, (73-80)
- Markchom T, Liang H and Ferryman J Explainable Meta-Path Based Recommender Systems, ACM Transactions on Recommender Systems, 0:0
