1,481 research outputs found
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.GarcĂa GarcĂa, I.; Pajares Ferrando, S.; Sebastiá TarĂn, 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
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
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Interactive product catalogue with user preference tracking
In the context of m-commerce, small screen size poses serious difficulty for users to browse effectively through a product catalogue, given the limited number of products that may be presented on-screen. Despite the availability of search engines, filters and recommender systems to aid users, these techniques focus on a narrow segment of product offering. The users are thus denied the opportunity to do a more expansive exploration of the products available. This paper describes a novel approach to overcome the constraints of small screen size. Through integration of a product catalogue with a recommender system, an adaptive system has been created that guides users through the process of product browsing. An original technique has been developed to cluster similar positive examples together to identify areas of interest of a user. The performance of this technique has been evaluated and the results proved to be promising
Evaluating recommender systems from the user's perspective: survey of the state of the art
A recommender system is a Web technology that proactively suggests items of interest to users based on their objective behavior or explicitly stated preferences. Evaluations of recommender systems (RS) have traditionally focused on the performance of algorithms. However, many researchers have recently started investigating system effectiveness and evaluation criteria from users' perspectives. In this paper, we survey the state of the art of user experience research in RS by examining how researchers have evaluated design methods that augment RS's ability to help users find the information or product that they truly prefer, interact with ease with the system, and form trust with RS through system transparency, control and privacy preserving mechanisms finally, we examine how these system design features influence users' adoption of the technology. We summarize existing work concerning three crucial interaction activities between the user and the system: the initial preference elicitation process, the preference refinement process, and the presentation of the system's recommendation results. Additionally, we will also cover recent evaluation frameworks that measure a recommender system's overall perceptive qualities and how these qualities influence users' behavioral intentions. The key results are summarized in a set of design guidelines that can provide useful suggestions to scholars and practitioners concerning the design and development of effective recommender systems. The survey also lays groundwork for researchers to pursue future topics that have not been covered by existing method
The interplay between food knowledge, nudges, and preference elicitation methods determines the evaluation of a recipe recommender system
Domain knowledge can affect how a user evaluates different aspects of a recommender system. Recipe recommendations might be difficult to understand, as some health aspects are implicit. The appropriateness of a recommender’s preference elicitation (PE) method, whether users rate individual items or item attributes, may depend on the user’s knowledge level. We present an online recipe recommender experiment. Users (𝑁=360) with varying levels of subjective food knowledge faced different cognitive digital nudges (i.e., food labels) and PE methods. In a 3 (recipes annotated with no labels, Multiple Traffic Light (MTL) labels, or full nutrition labels) x2 (PE method : content-based PE or knowledge-based) between-subjects design. We observed a main effect of knowledge-based PE on the healthiness of chosen recipes, while MTL label only helped marginally. A Structural Equation Model analysis revealed that the interplay between user knowledge and the PE method reduced the perceived effort of using the system and in turn, affected choice difficulty and satisfaction. Moreover, the evaluation of health labels depends on a user’s level of food knowledge. Our findings emphasize the importance of user characteristics in the evaluation of food recommenders and the merit of interface and inter action aspects
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