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2019
Maurizio Ferrari Dacrema; Paolo Cremonesi; Dietmar Jannach
Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches Conference
Proceedings of the 2019 ACM Conference on Recommender Systems (RecSys 2019), Copenhagen, 2019, (Pre SFI).
Abstract | BibTeX | Tags: Collaborative filtering, General and referance, Information Systems, Recommender systems, WP2: User Modeling Personalization and Engagement | Links:
@conference{Dacrema2019,
title = {Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches},
author = {Maurizio Ferrari Dacrema and Paolo Cremonesi and Dietmar Jannach},
url = {https://arxiv.org/pdf/1907.06902.pdf},
year = {2019},
date = {2019-08-16},
booktitle = {Proceedings of the 2019 ACM Conference on Recommender Systems (RecSys 2019)},
address = {Copenhagen},
abstract = {Deep learning techniques have become the method of choice for
researchers working on algorithmic aspects of recommender systems. With the strongly increased interest in machine learning in
general, it has, as a result, become difficult to keep track of what
represents the state-of-the-art at the moment, e.g., for top-n recommendation tasks. At the same time, several recent publications
point out problems in today’s research practice in applied machine
learning, e.g., in terms of the reproducibility of the results or the
choice of the baselines when proposing new models.
In this work, we report the results of a systematic analysis of algorithmic proposals for top-n recommendation tasks. Specifically,
we considered 18 algorithms that were presented at top-level research conferences in the last years. Only 7 of them could be reproduced with reasonable effort. For these methods, it however
turned out that 6 of them can often be outperformed with comparably simple heuristic methods, e.g., based on nearest-neighbor or
graph-based techniques. The remaining one clearly outperformed
the baselines but did not consistently outperform a well-tuned nonneural linear ranking method. Overall, our work sheds light on a
number of potential problems in today’s machine learning scholarship and calls for improved scientific practices in this area.},
note = {Pre SFI},
keywords = {Collaborative filtering, General and referance, Information Systems, Recommender systems, WP2: User Modeling Personalization and Engagement},
pubstate = {published},
tppubtype = {conference}
}
researchers working on algorithmic aspects of recommender systems. With the strongly increased interest in machine learning in
general, it has, as a result, become difficult to keep track of what
represents the state-of-the-art at the moment, e.g., for top-n recommendation tasks. At the same time, several recent publications
point out problems in today’s research practice in applied machine
learning, e.g., in terms of the reproducibility of the results or the
choice of the baselines when proposing new models.
In this work, we report the results of a systematic analysis of algorithmic proposals for top-n recommendation tasks. Specifically,
we considered 18 algorithms that were presented at top-level research conferences in the last years. Only 7 of them could be reproduced with reasonable effort. For these methods, it however
turned out that 6 of them can often be outperformed with comparably simple heuristic methods, e.g., based on nearest-neighbor or
graph-based techniques. The remaining one clearly outperformed
the baselines but did not consistently outperform a well-tuned nonneural linear ranking method. Overall, our work sheds light on a
number of potential problems in today’s machine learning scholarship and calls for improved scientific practices in this area.
2018
Massimo Quadrana; Paolo Cremonesi; Dietmar Jannach
Sequence-Aware Recommender Systems Journal Article
In: ACM Computing Surveys, vol. 51, no. 4, pp. 1-35, 2018, (Pre SFI).
Abstract | BibTeX | Tags: Collaborative filtering, Computing Methodology, Information Systems, Recommender systems, WP2: User Modeling Personalization and Engagement | Links:
@article{Quadrana2018,
title = {Sequence-Aware Recommender Systems},
author = {Massimo Quadrana and Paolo Cremonesi and Dietmar Jannach},
url = {https://arxiv.org/pdf/1802.08452.pdf},
year = {2018},
date = {2018-02-23},
journal = {ACM Computing Surveys},
volume = {51},
number = {4},
pages = {1-35},
abstract = {Recommender systems are one of the most successful applications of data mining and machine learning technology in practice. Academic research in the field is historically often based on the matrix completion problem formulation, where for each user-item-pair only one interaction (e.g., a rating) is considered. In many application domains, however, multiple user-item interactions of different types can be recorded over time. And, a number of recent works have shown that this information can be used to build richer individual user models and to discover additional behavioral patterns that can be leveraged in the recommendation process. In this work we review existing works that consider information from such sequentially-ordered user- item interaction logs in the recommendation process. Based on this review, we propose a categorization of the corresponding recommendation tasks and goals, summarize existing algorithmic solutions, discuss methodological approaches when benchmarking what we call sequence-aware recommender systems, and outline open challenges in the area.},
note = {Pre SFI},
keywords = {Collaborative filtering, Computing Methodology, Information Systems, Recommender systems, WP2: User Modeling Personalization and Engagement},
pubstate = {published},
tppubtype = {article}
}
2016
Mehdi Elahi; Francesco Ricci; Neil Rubens
A survey of active learning in collaborative filtering recommender systems Journal Article
In: Computer Science Review, vol. 20, pp. 29-50, 2016, (Pre SFI).
Abstract | BibTeX | Tags: Active Learning, Cold Start, Collaborative filtering, New Start, New User, Preference Elicitation, Rating Elicitation, Recommender system, WP2: User Modeling Personalization and Engagement | Links:
@article{Elahi2016,
title = {A survey of active learning in collaborative filtering recommender systems},
author = {Mehdi Elahi and Francesco Ricci and Neil Rubens},
url = {https://reader.elsevier.com/reader/sd/pii/S1574013715300150?token=EA12A462FC07F42733F4F13375217A57D3FDC7F6047C133156CB1F4E4487DF24C5366547DF4530A25942F690233F2E30},
doi = {10.1016/j.cosrev.2016.05.002},
year = {2016},
date = {2016-06-02},
journal = {Computer Science Review},
volume = {20},
pages = {29-50},
abstract = {In collaborative filtering recommender systems user’s preferences are expressed as ratings for items, and each additional rating extends the knowledge of the system and affects the system’s recommendation accuracy. In general, the more ratings are elicited from the users, the more effective the recommendations are. However, the usefulness of each rating may vary significantly, i.e., different ratings may bring a different amount and type of information about the user’s tastes. Hence, specific techniques, which are defined as “active learning strategies”, can be used to selectively choose the items to be presented to the user for rating. In fact, an active learning strategy identifies and adopts criteria for obtaining data that better reflects users’ preferences and enables to generate better recommendations.
So far, a variety of active learning strategies have been proposed in the literature. In this article, we survey recent strategies by grouping them with respect to two distinct dimensions: personalization, i.e., whether the system selected items are different for different users or not, and, hybridization, i.e., whether active learning is guided by a single criterion (heuristic) or by multiple criteria. In addition, we present a comprehensive overview of the evaluation methods and metrics that have been employed by the research community in order to test active learning strategies for collaborative filtering. Finally, we compare the surveyed strategies and provide guidelines for their usage in recommender systems.},
note = {Pre SFI},
keywords = {Active Learning, Cold Start, Collaborative filtering, New Start, New User, Preference Elicitation, Rating Elicitation, Recommender system, WP2: User Modeling Personalization and Engagement},
pubstate = {published},
tppubtype = {article}
}
So far, a variety of active learning strategies have been proposed in the literature. In this article, we survey recent strategies by grouping them with respect to two distinct dimensions: personalization, i.e., whether the system selected items are different for different users or not, and, hybridization, i.e., whether active learning is guided by a single criterion (heuristic) or by multiple criteria. In addition, we present a comprehensive overview of the evaluation methods and metrics that have been employed by the research community in order to test active learning strategies for collaborative filtering. Finally, we compare the surveyed strategies and provide guidelines for their usage in recommender systems.
Ignacio Fernandez Tobias; Matthias Braunhofer; Mehdi Elahi; Francesco Ricci; Ivan Cantador
Alleviating the new user problem in collaborative filtering by exploiting personality information Journal Article
In: User Modeling and User-Adapted Interaction, vol. 26, no. 2-3, pp. 221-255, 2016, (Pre SFI).
Abstract | BibTeX | Tags: Active Learning, Cold-start, Collaborative filtering, Cross-domain, Recommender systems, User Personality, WP2: User Modeling Personalization and Engagement | Links:
@article{Tobias2016,
title = {Alleviating the new user problem in collaborative filtering by exploiting personality information},
author = {Ignacio Fernandez Tobias and Matthias Braunhofer and Mehdi Elahi and Francesco Ricci and Ivan Cantador},
url = {https://www.researchgate.net/publication/285574429_Alleviating_the_New_User_Problem_in_Collaborative_Filtering_by_Exploiting_Personality_Information},
doi = {10.1007/s11257-016-9172-z},
year = {2016},
date = {2016-06-01},
journal = {User Modeling and User-Adapted Interaction},
volume = {26},
number = {2-3},
pages = {221-255},
abstract = {The new user problem in recommender systems is still challenging, and there is not yet a unique solution that can be applied in any domain or situation. In this paper we analyze viable solutions to the new user problem in collaborative filtering (CF) that are based on the exploitation of user personality information: (a) personality-based CF, which directly improves the recommendation prediction model by incorporating user personality information, (b) personality-based active learning, which utilizes personality information for identifying additional useful preference data in the target recommendation domain to be elicited from the user, and (c) personality-based cross-domain recommendation, which exploits personality information to better use user preference data from auxiliary domains which can be used to compensate the lack of user preference data in the target domain. We benchmark the effectiveness of these methods on large datasets that span several domains, namely movies, music and books. Our results show that personality-aware methods achieve performance improvements that range from 6 to 94 % for users completely new to the system, while increasing the novelty of the recommended items by 3–40 % with respect to the non-personalized popularity baseline. We also discuss the limitations of our approach and the situations in which the proposed methods can be better applied, hence providing guidelines for researchers and practitioners in the field.},
note = {Pre SFI},
keywords = {Active Learning, Cold-start, Collaborative filtering, Cross-domain, Recommender systems, User Personality, WP2: User Modeling Personalization and Engagement},
pubstate = {published},
tppubtype = {article}
}