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Movie genome: alleviating new item cold start in movie recommendation

Deldjoo, Yashar ; Dacrema, Maurizio Ferrari ; Constantin, Mihai Gabriel ; [et al.]

Springer Science and Business Media LLC ; 2019

In: User Modeling and User-Adapted Interaction Vol. 29, No. 2 ( 2019-4), p. 291-343

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In: User Modeling and User-Adapted Interaction, Springer Science and Business Media LLC, Vol. 29, No. 2 ( 2019-4), p. 291-343

Type of Medium: Online Resource

ISSN: 0924-1868 , 1573-1391

URL: Article

DOI: 10.1007/s11257-019-09221-y

Language: English

Publisher: Springer Science and Business Media LLC

Publication Date: 2019

detail.hit.zdb_id: 1475734-5

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From Data Analysis to Intent-Based Recommendation: An Industrial Case Study in the Video Domain

Bernardis, Cesare ; Dacrema, Maurizio Ferrari ; Maurera, Fernando Benjamin Perez ; [et al.]

Institute of Electrical and Electronics Engineers (IEEE) ; 2022

In: IEEE Access Vol. 10 ( 2022), p. 14779-14796

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In: IEEE Access, Institute of Electrical and Electronics Engineers (IEEE), Vol. 10 ( 2022), p. 14779-14796

Type of Medium: Online Resource

ISSN: 2169-3536

URL: Article

DOI: 10.1109/ACCESS.2022.3148434

Language: Unknown

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Publication Date: 2022

detail.hit.zdb_id: 2687964-5

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A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research

Ferrari Dacrema, Maurizio ; Boglio, Simone ; Cremonesi, Paolo ; [et al.]

Association for Computing Machinery (ACM) ; 2021

In: ACM Transactions on Information Systems Vol. 39, No. 2 ( 2021-04-30), p. 1-49

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In: ACM Transactions on Information Systems, Association for Computing Machinery (ACM), Vol. 39, No. 2 ( 2021-04-30), p. 1-49

Abstract: The design of algorithms that generate personalized ranked item lists is a central topic of research in the field of recommender systems. In the past few years, in particular, approaches based on deep learning (neural) techniques have become dominant in the literature. For all of them, substantial progress over the state-of-the-art is claimed. However, indications exist of certain problems in today’s research practice, e.g., with respect to the choice and optimization of the baselines used for comparison, raising questions about the published claims. To obtain a better understanding of the actual progress, we have compared recent results in the area of neural recommendation approaches based on collaborative filtering against a consistent set of existing simple baselines. The worrying outcome of the analysis of these recent works—all were published at prestigious scientific conferences between 2015 and 2018—is that 11 of the 12 reproducible neural approaches can be outperformed by conceptually simple methods, e.g., based on the nearest-neighbor heuristic or linear models. None of the computationally complex neural methods was actually consistently better than already existing learning-based techniques, e.g., using matrix factorization or linear models. In our analysis, we discuss common issues in today’s research practice, which, despite the many papers that are published on the topic, have apparently led the field to a certain level of stagnation. 1

Type of Medium: Online Resource

ISSN: 1046-8188 , 1558-2868

URL: Article

DOI: 10.1145/3434185

Language: English

Publisher: Association for Computing Machinery (ACM)

Publication Date: 2021

detail.hit.zdb_id: 602352-6

detail.hit.zdb_id: 2006337-4

SSG: 24,1

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Demonstrating the Equivalence of List Based and Aggregate Metrics to Measure the Diversity of Recommendations (Student Abstract)

Ferrari Dacrema, Maurizio

Association for the Advancement of Artificial Intelligence (AAAI) ; 2021

In: Proceedings of the AAAI Conference on Artificial Intelligence Vol. 35, No. 18 ( 2021-05-18), p. 15779-15780

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In: Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 35, No. 18 ( 2021-05-18), p. 15779-15780

Abstract: The evaluation of recommender systems is frequently focused on accuracy metrics, but this is only part of the picture. The diversity of recommendations is another important dimension that has received renewed interest in recent years. It is known that accuracy and diversity can be conflicting goals and finding appropriate ways to combine them is still an open research question. Several ways have been proposed to measure the diversity of recommendations and to include its optimization in the loss function used to train the model. Methods optimizing list based diversity suffer from two drawbacks: the high computational cost of the loss function and the lack of an efficient way to optimize them. In this paper we show the equivalence of the list based diversity metrics Hamming and Mean Inter-List diversity to the aggregate diversity metric measured with the Herfindahl index, providing a formulation that allows to compute and optimize them easily.

Type of Medium: Online Resource

ISSN: 2374-3468 , 2159-5399

URL: Article

DOI: 10.1609/aaai.v35i18.17886

Language: Unknown

Publisher: Association for the Advancement of Artificial Intelligence (AAAI)

Publication Date: 2021

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Feature Selection for Recommender Systems with Quantum Computing

Nembrini, Riccardo ; Ferrari Dacrema, Maurizio ; Cremonesi, Paolo

MDPI AG ; 2021

In: Entropy Vol. 23, No. 8 ( 2021-07-28), p. 970-

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In: Entropy, MDPI AG, Vol. 23, No. 8 ( 2021-07-28), p. 970-

Abstract: The promise of quantum computing to open new unexplored possibilities in several scientific fields has been long discussed, but until recently the lack of a functional quantum computer has confined this discussion mostly to theoretical algorithmic papers. It was only in the last few years that small but functional quantum computers have become available to the broader research community. One paradigm in particular, quantum annealing, can be used to sample optimal solutions for a number of NP-hard optimization problems represented with classical operations research tools, providing an easy access to the potential of this emerging technology. One of the tasks that most naturally fits in this mathematical formulation is feature selection. In this paper, we investigate how to design a hybrid feature selection algorithm for recommender systems that leverages the domain knowledge and behavior hidden in the user interactions data. We represent the feature selection as an optimization problem and solve it on a real quantum computer, provided by D-Wave. The results indicate that the proposed approach is effective in selecting a limited set of important features and that quantum computers are becoming powerful enough to enter the wider realm of applied science.

Type of Medium: Online Resource

ISSN: 1099-4300

URL: Article

DOI: 10.3390/e23080970

Language: English

Publisher: MDPI AG

Publication Date: 2021

detail.hit.zdb_id: 2014734-X

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fdata-05-910030-g0007.tif

Ferrari Dacrema, Maurizio ; Felicioni, Nicolò ; Cremonesi, Paolo

Frontiers Media SA ; 2022

In: Frontiers in Big Data Vol. 5 ( 2022-6-9)

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In: Frontiers in Big Data, Frontiers Media SA, Vol. 5 ( 2022-6-9)

Abstract: Many video-on-demand and music streaming services provide the user with a page consisting of several recommendation lists, i.e., widgets or swipeable carousels , each built with specific criteria (e.g., most recent, TV series, etc.). Finding efficient strategies to select which carousels to display is an active research topic of great industrial interest. In this setting, the overall quality of the recommendations of a new algorithm cannot be assessed by measuring solely its individual recommendation quality. Rather, it should be evaluated in a context where other recommendation lists are already available, to account for how they complement each other. The traditional offline evaluation protocol however does not take this into account. To address this limitation, we propose an offline evaluation protocol for a carousel setting in which the recommendation quality of a model is measured by how much it improves upon that of an already available set of carousels. We also propose to extend ranking metrics to the two-dimensional carousel setting in order to account for a known position bias, i.e., users will not explore the lists sequentially, but rather concentrate on the top-left corner of the screen. Finally, we describe and evaluate two strategies for the ranking of carousels in a scenario where the technique used to generate the two-dimensional layout is agnostic on the algorithms used to generate each carousel. We report experiments on publicly available datasets in the movie domain to show how the relative effectiveness of several recommendation models compares. Our results indicate that under a carousel setting the ranking of the algorithms changes sometimes significantly. Furthermore, when selecting the optimal carousel layout accounting for the two dimensional layout of the user interface leads to very different selections.

Type of Medium: Online Resource

ISSN: 2624-909X

URL: Article

DOI: 10.3389/fdata.2022.910030

DOI: 10.3389/fdata.2022.910030.s001

DOI: 10.3389/fdata.2022.910030.s002

DOI: 10.3389/fdata.2022.910030.s003

DOI: 10.3389/fdata.2022.910030.s004

DOI: 10.3389/fdata.2022.910030.s005

DOI: 10.3389/fdata.2022.910030.s006

DOI: 10.3389/fdata.2022.910030.s007

DOI: 10.3389/fdata.2022.910030.s008

DOI: 10.3389/fdata.2022.910030.s009

Language: Unknown

Publisher: Frontiers Media SA

Publication Date: 2022

detail.hit.zdb_id: 2957497-3

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Evaluating the job shop scheduling problem on a D-wave quantum annealer

Carugno, Costantino ; Ferrari Dacrema, Maurizio ; Cremonesi, Paolo

Springer Science and Business Media LLC ; 2022

In: Scientific Reports Vol. 12, No. 1 ( 2022-04-21)

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In: Scientific Reports, Springer Science and Business Media LLC, Vol. 12, No. 1 ( 2022-04-21)

Abstract: Job Shop Scheduling is a combinatorial optimization problem of particular importance for production environments where the goal is to complete a production task in the shortest possible time given limitations in the resources available. Due to its computational complexity it quickly becomes intractable for problems of interesting size. The emerging technology of Quantum Annealing provides an alternative computational architecture that promises improved scalability and solution quality. However, several limitations as well as open research questions exist in this relatively new and rapidly developing technology. This paper studies the application of quantum annealing to solve the job shop scheduling problem, describing each step required from the problem formulation to the fine-tuning of the quantum annealer and compares the solution quality with various classical solvers. Particular attention is devoted to aspects that are often overlooked, such as the computational cost of representing the problem in the formulation required by the quantum annealer, the relative qubits requirements and how to mitigate chain breaks. Furthermore, the impact of advanced tools such as reverse annealing is presented and its effectiveness discussed. The results indicate several challenges emerging at various stages of the experimental pipeline which bring forward important research questions and directions of improvement.

Type of Medium: Online Resource

ISSN: 2045-2322

URL: Article

DOI: 10.1038/s41598-022-10169-0

Language: English

Publisher: Springer Science and Business Media LLC

Publication Date: 2022

detail.hit.zdb_id: 2615211-3

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