524 research outputs found
Towards Question-based Recommender Systems
Conversational and question-based recommender systems have gained increasing
attention in recent years, with users enabled to converse with the system and
better control recommendations. Nevertheless, research in the field is still
limited, compared to traditional recommender systems. In this work, we propose
a novel Question-based recommendation method, Qrec, to assist users to find
items interactively, by answering automatically constructed and algorithmically
chosen questions. Previous conversational recommender systems ask users to
express their preferences over items or item facets. Our model, instead, asks
users to express their preferences over descriptive item features. The model is
first trained offline by a novel matrix factorization algorithm, and then
iteratively updates the user and item latent factors online by a closed-form
solution based on the user answers. Meanwhile, our model infers the underlying
user belief and preferences over items to learn an optimal question-asking
strategy by using Generalized Binary Search, so as to ask a sequence of
questions to the user. Our experimental results demonstrate that our proposed
matrix factorization model outperforms the traditional Probabilistic Matrix
Factorization model. Further, our proposed Qrec model can greatly improve the
performance of state-of-the-art baselines, and it is also effective in the case
of cold-start user and item recommendations.Comment: accepted by SIGIR 202
Preference elicitation techniques for group recommender systems
A key issue in group recommendation is how to combine the individual preferences of different users that form a group and elicit a profile that accurately reflects the tastes of all members in the group. Most Group Recommender Systems (GRSs) make use of some sort of method for aggregating the preference models of individual users to elicit a recommendation that is satisfactory for the whole group. In general, most GRSs offer good results, but each of them have only been tested in one application domain. This paper describes a domain-independent GRS that has been used in two different application domains. In order to create the group preference model, we select two techniques that are widely used in other GRSs and we compare them with two novel techniques. Our aim is to come up with a model that weighs the preferences of all the individuals to the same extent in such a way that no member in the group is particularly satisfied or dissatisfied with the final recommendations. © 2011 Elsevier Inc. All rights reserved.Partial support provided by Consolider Ingenio 2010 CSD2007-00022, Spanish Government Project MICINN TIN2008-6701-C03-01 and Valencian Government Project Prometeo 2008/051. FPU grant reference AP2009-1896 awarded to Sergio Pajares-Ferrando.García García, I.; Pajares Ferrando, S.; Sebastiá Tarín, L.; Onaindia De La Rivaherrera, E. (2012). Preference elicitation techniques for group recommender systems. Information Sciences. 189:155-175. https://doi.org/10.1016/j.ins.2011.11.037S15517518
On the exploitation of user personality in recommender systems
Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) Proceedings of the First International Workshop on Decision Making and Recommender Systems (DMRS2014)In this paper we revise the state of the art on personality-aware
recommender systems, identifying main research trends and achievements up to
date, and discussing open issues that may be addressed in the future.This work was supported by the Spanish Ministry of Science and Innovation
(TIN2013-47090-C3-2)
Enhancing User Personalization in Conversational Recommenders
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 multi-groundtruth
multi-round conversational recommendation setting.Comment: To Appear On TheWebConf (WWW) 202
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