An empirical evaluation of active learning strategies for profile elicitation in a conversational recommender system

Iovine, Andrea; Lops, Pasquale; Narducci, Fedelucio; Gemmis, Marco de; Semeraro, Giovanni

doi:10.1007/s10844-021-00683-4

Cited by 5 publications

(6 citation statements)

References 40 publications

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“…We implement our proposed methodology as a food recommendation system where users can evaluate recipes by specifying their preferences over recipe attributes. The proposed methodology is not restricted to food recommendation and could be implemented for several domains like e-commerce, book, movie [20], and fashion recommendation in which recommendation items are either contain visual or textual descriptive information that can be evaluated by users to express preferences.…”

Section: Discussionmentioning

confidence: 99%

“…Rubens et al describe existing AL methods with the viewpoint of the classical new-user problem in RSs that considers ratings by available users to estimate a rating vector of a user over all items [37]. Such studies on AL for RSs rely on data of available users in the systems, following the idea of collaborative filtering [10,11,20]. For example, rating variance, popularity, rating entropy, and log-popularity-entropy, which balances popularity and entropy, are the metrics to determine which instances are selected to ask the new user to label them.…”

Section: Related Workmentioning

confidence: 99%

“…These metrics are calculated based on the ratings and/or activity of other users in the system. Alternatively, Iovine et al also use AL with content-based filtering by selecting dissimilar items from pre-existing items in the user profile for rating them by the user [20]. Despite AL being relatively more adopted with collaborative filtering, Hernández-Rubio et al propose a hybrid filtering approach with AL, Aspect-based AL [19].…”

Section: Related Workmentioning

confidence: 99%

“…Subsequently, an ML model is updated by including those informative samples into the training dataset. As getting information from users to learn their preferences can be tiresome for them, AL could be a convenient information acquisition method for preference elicitation [20,37], considering the cost of obtaining labels for items from users. In addition to AL, we consider the explainability of RS as a second component to fertilize the information acquired through user interactions.…”

Section: Introductionmentioning

confidence: 99%

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Explainable Active Learning for Preference Elicitation

Cantürk,

Aydoğan

2023

Preprint

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Gaining insights into the preferences of new users and subsequently personalizing recommendations necessitate managing user interactions intelligently, namely, posing pertinent questions to elicit valuable information effectively. In this study, our focus is on a specific scenario of the cold-start problem, where the recommendation system lacks adequate user presence or access to other users' data is restricted, obstructing employing user profiling methods utilizing existing data in the system. We employ Active Learning (AL) to solve the addressed problem with the objective of maximizing information acquisition with minimal user effort. AL operates for selecting informative data from a large unlabeled set to inquire an oracle to label them and eventually updating a machine learning (ML) model. We operate AL in an integrated process of unsupervised, semi-supervised, and supervised ML within an explanatory preference elicitation process. It harvests user feedback (given for the system's explanations on the presented items) over informative samples to update an underlying ML model estimating user preferences. The designed user interaction facilitates personalizing the system by incorporating user feedback into the ML model and also enhances user trust by refining the system's explanations on recommendations. We implement the proposed preference elicitation methodology for food recommendation. We conducted human experiments to assess its efficacy in the short term and also experimented with several AL strategies over synthetic user profiles that we created for two food datasets, aiming for long-term performance analysis. The experimental results demonstrate the efficiency of the proposed preference elicitation with limited user-labeled data while also enhancing user trust through accurate explanations.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Explainable Active Learning for Preference Elicitation

Cantürk,

Aydoğan

2023

Preprint

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“…In this work, we propose a novel attributeselection and a preference fusion strategy that jointly address these two issues. Note that while there are other research directions in CRSs including dialogue understanding [10,14,28,37,40], response generation [16,18,20,39], and exploration-exploitation trade-offs [7,11,31,32,38] those are not the focus of this work.…”

Section: Related Workmentioning

confidence: 99%

Enhancing User Personalization in Conversational Recommenders

Lin¹,

Zhu²,

Wang³

et al. 2023

Preprint

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Conversational recommenders are emerging as a powerful tool to personalize a user's recommendation experience. Through a back-and-forth dialogue, users can quickly hone in on just the right items. Many approaches to conversational recommendation, however, only partially explore the user preference space and make limiting assumptions about how user feedback can be best incorporated, resulting in long dialogues and poor recommendation performance. In this paper, we propose a novel conversational recommendation framework with two unique features: (i) a greedy NDCG attribute selector, to enhance user personalization in the interactive preference elicitation process by prioritizing attributes that most effectively represent the actual preference space of the user; and (ii) a user representation refiner, to effectively fuse together the user preferences collected from the interactive elicitation process to obtain a more personalized understanding of the user. Through extensive experiments on four frequently used datasets, we find the proposed framework not only outperforms all the state-of-the-art conversational recommenders (in terms of both recommendation performance and conversation efficiency), but also provides a more personalized experience for the user under the proposed multigroundtruth multi-round conversational recommendation setting. CCS CONCEPTS• Information systems → Recommender systems.

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