22,189 research outputs found
A Faster Algorithm to Build New Users Similarity List in Neighbourhood-based Collaborative Filtering
Neighbourhood-based Collaborative Filtering (CF) has been applied in the
industry for several decades, because of the easy implementation and high
recommendation accuracy. As the core of neighbourhood-based CF, the task of
dynamically maintaining users' similarity list is challenged by cold-start
problem and scalability problem. Recently, several methods are presented on
solving the two problems. However, these methods applied an algorithm
to compute the similarity list in a special case, where the new users, with
enough recommendation data, have the same rating list. To address the problem
of large computational cost caused by the special case, we design a faster
() algorithm, TwinSearch Algorithm, to avoid computing and
sorting the similarity list for the new users repeatedly to save the
computational resources. Both theoretical and experimental results show that
the TwinSearch Algorithm achieves better running time than the traditional
method
Information Filtering on Coupled Social Networks
In this paper, based on the coupled social networks (CSN), we propose a
hybrid algorithm to nonlinearly integrate both social and behavior information
of online users. Filtering algorithm based on the coupled social networks,
which considers the effects of both social influence and personalized
preference. Experimental results on two real datasets, \emph{Epinions} and
\emph{Friendfeed}, show that hybrid pattern can not only provide more accurate
recommendations, but also can enlarge the recommendation coverage while
adopting global metric. Further empirical analyses demonstrate that the mutual
reinforcement and rich-club phenomenon can also be found in coupled social
networks where the identical individuals occupy the core position of the online
system. This work may shed some light on the in-depth understanding structure
and function of coupled social networks
OBOE: Collaborative Filtering for AutoML Model Selection
Algorithm selection and hyperparameter tuning remain two of the most
challenging tasks in machine learning. Automated machine learning (AutoML)
seeks to automate these tasks to enable widespread use of machine learning by
non-experts. This paper introduces OBOE, a collaborative filtering method for
time-constrained model selection and hyperparameter tuning. OBOE forms a matrix
of the cross-validated errors of a large number of supervised learning models
(algorithms together with hyperparameters) on a large number of datasets, and
fits a low rank model to learn the low-dimensional feature vectors for the
models and datasets that best predict the cross-validated errors. To find
promising models for a new dataset, OBOE runs a set of fast but informative
algorithms on the new dataset and uses their cross-validated errors to infer
the feature vector for the new dataset. OBOE can find good models under
constraints on the number of models fit or the total time budget. To this end,
this paper develops a new heuristic for active learning in time-constrained
matrix completion based on optimal experiment design. Our experiments
demonstrate that OBOE delivers state-of-the-art performance faster than
competing approaches on a test bed of supervised learning problems. Moreover,
the success of the bilinear model used by OBOE suggests that AutoML may be
simpler than was previously understood
FARS: Fuzzy Ant based Recommender System for Web Users
Recommender systems are useful tools which provide an
adaptive web environment for web users. Nowadays, having a
user friendly website is a big challenge in e-commerce
technology. In this paper, applying the benefits of both
collaborative and content based filtering techniques is proposed by presenting a fuzzy recommender system based on
collaborative behavior of ants (FARS). FARS works in two
phases: modeling and recommendation. First, user’s behaviors
are modeled offline and the results are used in second phase for online recommendation. Fuzzy techniques provide the possibility of capturing uncertainty among user interests and ant based algorithms provides us with optimal solutions. The performance of FARS is evaluated using log files of “Information and Communication Technology Center” of Isfahan municipality in Iran and compared with ant based recommender system (ARS). The results shown are promising and proved that integrating fuzzy Ant approach provides us with more functional and robust recommendations
On Recommendation of Learning Objects using Felder-Silverman Learning Style Model
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The e-learning recommender system in learning institutions is increasingly becoming the preferred mode of delivery, as it enables learning anytime, anywhere. However, delivering personalised course learning objects based on learner preferences is still a challenge. Current mainstream recommendation algorithms, such as the Collaborative Filtering (CF) and Content-Based Filtering (CBF), deal with only two types of entities, namely users and items with their ratings. However, these methods do not pay attention to student preferences, such as learning styles, which are especially important for the accuracy of course learning objects prediction or recommendation. Moreover, several recommendation techniques experience cold-start and rating sparsity problems. To address the challenge of improving the quality of recommender systems, in this paper a novel recommender algorithm for machine learning is proposed, which combines students actual rating with their learning styles to recommend Top-N course learning objects (LOs). Various recommendation techniques are considered in an experimental study investigating the best technique to use in predicting student ratings for e-learning recommender systems. We use the Felder-Silverman Learning Styles Model (FSLSM) to represent both the student learning styles and the learning object profiles. The predicted rating has been compared with the actual student rating. This approach has been experimented on 80 students for an online course created in the MOODLE Learning Management System, while the evaluation of the experiments has been performed with the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results of the experiment verify that the proposed approach provides a higher prediction rating and significantly increases the accuracy of the recommendation
Emergence of scale-free leadership structure in social recommender systems
The study of the organization of social networks is important for
understanding of opinion formation, rumor spreading, and the emergence of
trends and fashion. This paper reports empirical analysis of networks extracted
from four leading sites with social functionality (Delicious, Flickr, Twitter
and YouTube) and shows that they all display a scale-free leadership structure.
To reproduce this feature, we propose an adaptive network model driven by
social recommending. Artificial agent-based simulations of this model highlight
a "good get richer" mechanism where users with broad interests and good
judgments are likely to become popular leaders for the others. Simulations also
indicate that the studied social recommendation mechanism can gradually improve
the user experience by adapting to tastes of its users. Finally we outline
implications for real online resource-sharing systems
Evolutionary intelligent agents for e-commerce: Generic preference detection with feature analysis
Product recommendation and preference tracking systems have been adopted extensively in e-commerce businesses. However, the heterogeneity of product attributes results in undesired impediment for an efficient yet personalized e-commerce product brokering. Amid the assortment of product attributes, there are some intrinsic generic attributes having significant relation to a customer’s generic preference. This paper proposes a novel approach in the detection of generic product attributes through feature analysis. The objective is to provide an insight to the understanding of customers’ generic preference. Furthermore, a genetic algorithm is used to find the suitable feature weight set, hence reducing the rate of misclassification. A prototype has been implemented and the experimental results are promising
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