1,698 research outputs found
Multiobjective e-commerce recommendations based on hypergraph ranking
© 2018 Recommender systems are emerging in e-commerce as important promotion tools to assist customers to discover potentially interesting items. Currently, most of these are single-objective and search for items that fit the overall preference of a particular user. In real applications, such as restaurant recommendations, however, users often have multiple objectives such as group preferences and restaurant ambiance. This paper highlights the need for multi-objective recommendations and provides a solution using hypergraph ranking. A general User–Item–Attribute–Context data model is proposed to summarize different information resources and high-order relationships for the construction of a multipartite hypergraph. This study develops an improved balanced hypergraph ranking method to rank different types of objects in hypergraph data. An overall framework is then proposed as a guideline for the implementation of multi-objective recommender systems. Empirical experiments are conducted with the dataset from a review site Yelp.com, and the outcomes demonstrate that the proposed model performs very well for multi-objective recommendations. The experiments also demonstrate that this framework is still compatible for traditional single-objective recommendations and can improve accuracy significantly. In conclusion, the proposed multi-objective recommendation framework is able to handle complex and changing demands for e-commerce customers
Intent-Aware Contextual Recommendation System
Recommender systems take inputs from user history, use an internal ranking
algorithm to generate results and possibly optimize this ranking based on
feedback. However, often the recommender system is unaware of the actual intent
of the user and simply provides recommendations dynamically without properly
understanding the thought process of the user. An intelligent recommender
system is not only useful for the user but also for businesses which want to
learn the tendencies of their users. Finding out tendencies or intents of a
user is a difficult problem to solve.
Keeping this in mind, we sought out to create an intelligent system which
will keep track of the user's activity on a web-application as well as
determine the intent of the user in each session. We devised a way to encode
the user's activity through the sessions. Then, we have represented the
information seen by the user in a high dimensional format which is reduced to
lower dimensions using tensor factorization techniques. The aspect of intent
awareness (or scoring) is dealt with at this stage. Finally, combining the user
activity data with the contextual information gives the recommendation score.
The final recommendations are then ranked using filtering and collaborative
recommendation techniques to show the top-k recommendations to the user. A
provision for feedback is also envisioned in the current system which informs
the model to update the various weights in the recommender system. Our overall
model aims to combine both frequency-based and context-based recommendation
systems and quantify the intent of a user to provide better recommendations.
We ran experiments on real-world timestamped user activity data, in the
setting of recommending reports to the users of a business analytics tool and
the results are better than the baselines. We also tuned certain aspects of our
model to arrive at optimized results.Comment: Presented at the 5th International Workshop on Data Science and Big
Data Analytics (DSBDA), 17th IEEE International Conference on Data Mining
(ICDM) 2017; 8 pages; 4 figures; Due to the limitation "The abstract field
cannot be longer than 1,920 characters," the abstract appearing here is
slightly shorter than the one in the PDF fil
Learning Task Relatedness in Multi-Task Learning for Images in Context
Multimedia applications often require concurrent solutions to multiple tasks.
These tasks hold clues to each-others solutions, however as these relations can
be complex this remains a rarely utilized property. When task relations are
explicitly defined based on domain knowledge multi-task learning (MTL) offers
such concurrent solutions, while exploiting relatedness between multiple tasks
performed over the same dataset. In most cases however, this relatedness is not
explicitly defined and the domain expert knowledge that defines it is not
available. To address this issue, we introduce Selective Sharing, a method that
learns the inter-task relatedness from secondary latent features while the
model trains. Using this insight, we can automatically group tasks and allow
them to share knowledge in a mutually beneficial way. We support our method
with experiments on 5 datasets in classification, regression, and ranking tasks
and compare to strong baselines and state-of-the-art approaches showing a
consistent improvement in terms of accuracy and parameter counts. In addition,
we perform an activation region analysis showing how Selective Sharing affects
the learned representation.Comment: To appear in ICMR 2019 (Oral + Lightning Talk + Poster
Text Classification: A Review, Empirical, and Experimental Evaluation
The explosive and widespread growth of data necessitates the use of text
classification to extract crucial information from vast amounts of data.
Consequently, there has been a surge of research in both classical and deep
learning text classification methods. Despite the numerous methods proposed in
the literature, there is still a pressing need for a comprehensive and
up-to-date survey. Existing survey papers categorize algorithms for text
classification into broad classes, which can lead to the misclassification of
unrelated algorithms and incorrect assessments of their qualities and behaviors
using the same metrics. To address these limitations, our paper introduces a
novel methodological taxonomy that classifies algorithms hierarchically into
fine-grained classes and specific techniques. The taxonomy includes methodology
categories, methodology techniques, and methodology sub-techniques. Our study
is the first survey to utilize this methodological taxonomy for classifying
algorithms for text classification. Furthermore, our study also conducts
empirical evaluation and experimental comparisons and rankings of different
algorithms that employ the same specific sub-technique, different
sub-techniques within the same technique, different techniques within the same
category, and categorie
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