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2020
Naieme Hazrati; Mehdi Elahi
Addressing the New Item problem in video recommender systems by incorporation of visual features with restricted Boltzmann machines. Journal Article
In: Expert Systems, vol. e12645, pp. 1-20, 2020, (Pre SFI).
Abstract | BibTeX | Tags: Cold Start, Multimedia, New Item, Recommender systems, Visually Aware, WP2: User Modeling Personalization and Engagement | Links:
@article{Hazrati2020,
title = { Addressing the New Item problem in video recommender systems by incorporation of visual features with restricted Boltzmann machines.},
author = {Naieme Hazrati and Mehdi Elahi},
url = {https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.12645},
doi = {https://doi.org/10.1111/exsy.12645},
year = {2020},
date = {2020-10-19},
journal = {Expert Systems},
volume = {e12645},
pages = {1-20},
abstract = {Over the past years, the research of video recommender systems (RSs) has been mainly focussed on the development of novel algorithms. Although beneficial, still any algorithm may fail to recommend video items that the system has no form of data associated to them (New Item Cold Start). This problem occurs when a new item is added to the catalogue of the system and no data are available for that item. In content‐based RSs, the video items are typically represented by semantic attributes, when generating recommendations. These attributes require a group of experts or users for annotation, and still, the generated recommendations might not capture a complete picture of the users' preferences, for example, the visual tastes of users on video style. This article addresses this problem by proposing recommendation based on novel visual features that do not require human annotation and can represent visual aspects of video items. We have designed a novel evaluation methodology considering three realistic scenarios, that is, (a) extreme cold start, (b) moderate cold start and (c) warm‐start scenario. We have conducted a set of comprehensive experiments, and our results have shown the superior performance of recommendations based on visual features, in all of the evaluation scenarios.},
note = {Pre SFI},
keywords = {Cold Start, Multimedia, New Item, Recommender systems, Visually Aware, 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.