55 research outputs found
Directional Multivariate Ranking
User-provided multi-aspect evaluations manifest users' detailed feedback on
the recommended items and enable fine-grained understanding of their
preferences. Extensive studies have shown that modeling such data greatly
improves the effectiveness and explainability of the recommendations. However,
as ranking is essential in recommendation, there is no principled solution yet
for collectively generating multiple item rankings over different aspects. In
this work, we propose a directional multi-aspect ranking criterion to enable a
holistic ranking of items with respect to multiple aspects. Specifically, we
view multi-aspect evaluation as an integral effort from a user that forms a
vector of his/her preferences over aspects. Our key insight is that the
direction of the difference vector between two multi-aspect preference vectors
reveals the pairwise order of comparison. Hence, it is necessary for a
multi-aspect ranking criterion to preserve the observed directions from such
pairwise comparisons. We further derive a complete solution for the
multi-aspect ranking problem based on a probabilistic multivariate tensor
factorization model. Comprehensive experimental analysis on a large TripAdvisor
multi-aspect rating dataset and a Yelp review text dataset confirms the
effectiveness of our solution.Comment: Accepted as a full research paper in KDD'2
Recommender Systems for Online and Mobile Social Networks: A survey
Recommender Systems (RS) currently represent a fundamental tool in online
services, especially with the advent of Online Social Networks (OSN). In this
case, users generate huge amounts of contents and they can be quickly
overloaded by useless information. At the same time, social media represent an
important source of information to characterize contents and users' interests.
RS can exploit this information to further personalize suggestions and improve
the recommendation process. In this paper we present a survey of Recommender
Systems designed and implemented for Online and Mobile Social Networks,
highlighting how the use of social context information improves the
recommendation task, and how standard algorithms must be enhanced and optimized
to run in a fully distributed environment, as opportunistic networks. We
describe advantages and drawbacks of these systems in terms of algorithms,
target domains, evaluation metrics and performance evaluations. Eventually, we
present some open research challenges in this area
Intelligent techniques for recommender systems
This thesis focuses on the data sparsity issue and the temporal dynamic issue in the context of collaborative filtering, and addresses them with imputation techniques, low-rank subspace techniques and optimizations techniques from the machine learning perspective. A comprehensive survey on the development of collaborative filtering techniques is also included
Review on recent advances in information mining from big consumer opinion data for product design
In this paper, based on more than ten years' studies on this dedicated research thrust, a comprehensive review concerning information mining from big consumer opinion data in order to assist product design is presented. First, the research background and the essential terminologies regarding online consumer opinion data are introduced. Next, studies concerning information extraction and information utilization of big consumer opinion data for product design are reviewed. Studies on information extraction of big consumer opinion data are explained from various perspectives, including data acquisition, opinion target recognition, feature identification and sentiment analysis, opinion summarization and sampling, etc. Reviews on information utilization of big consumer opinion data for product design are explored in terms of how to extract critical customer needs from big consumer opinion data, how to connect the voice of the customers with product design, how to make effective comparisons and reasonable ranking on similar products, how to identify ever-evolving customer concerns efficiently, and so on. Furthermore, significant and practical aspects of research trends are highlighted for future studies. This survey will facilitate researchers and practitioners to understand the latest development of relevant studies and applications centered on how big consumer opinion data can be processed, analyzed, and exploited in aiding product design
Multicriteria Evaluation for Top-k and Sequence-based Recommender Systems
L'abstract è presente nell'allegato / the abstract is in the attachmen
Recommender systems and time context: Characterization of a robust evaluation protocol to increase reliability of measured improvements
Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, octubre de 201
Using contextual information to understand searching and browsing behavior
There is great imbalance in the richness of information on the web and the succinctness and poverty of search requests of web users, making their queries only a partial description of the underlying complex information needs. Finding ways to better leverage contextual information and make search context-aware holds the promise to dramatically improve the search experience of users. We conducted a series of studies to discover, model and utilize contextual information in order to understand and improve users' searching and browsing behavior on the web. Our results capture important aspects of context under the realistic conditions of different online search services, aiming to ensure that our scientific insights and solutions transfer to the operational settings of real world applications
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