1,645 research outputs found
Social tag relevance learning via ranking-oriented neighbor voting
Ministry of Education, Singapore under its Academic Research Funding Tier
Image Tagging using Modified Association Rule based on Semantic Neighbors
With the rapid development of the internet, mobiles, and social image-sharing websites, a large number of images are generated daily. The huge repository of the images poses challenges for an image retrieval system. On image-sharing social websites such as Flickr, the users can assign keywords/tags to the images which can describe the content of the images. These tags play important role in an image retrieval system. However, the user-assigned tags are highly personalized which brings many challenges for retrieval of the images. Thus, it is necessary to suggest appropriate tags to the images.
Existing methods for tag recommendation based on nearest neighbors ignore the relationship between tags. In this paper, the method is proposed for tag recommendations for the images based on semantic neighbors using modified association rule. Given an image, the method identifies the semantic neighbors using random forest based on the weight assigned to each category. The tags associated with the semantic neighbors are used as candidate tags. The candidate tags are expanded by mining tags using modified association rules where each semantic neighbor is considered a transaction. In modified association rules, the probability of each tag is calculated using TF-IDF and confidence value.
The experimentation is done on Flickr, NUS-WIDE, and Corel-5k datasets. The result obtained using the proposed method gives better performance as compared to the existing tag recommendation methods
Exploring Social Recommendations with Visual Diversity-Promoting Interfaces
The beyond-relevance objectives of recommender systems have been drawing more and more attention. For example, a diversity-enhanced interface has been shown to associate positively with overall levels of user satisfaction. However, little is known about how users adopt diversity-enhanced interfaces to accomplish various real-world tasks. In this article, we present two attempts at creating a visual diversity-enhanced interface that presents recommendations beyond a simple ranked list. Our goal was to design a recommender system interface to help users explore the different relevance prospects of recommended items in parallel and to stress their diversity. Two within-subject user studies in the context of social recommendation at academic conferences were conducted to compare our visual interfaces. Results from our user study show that the visual interfaces significantly reduced the exploration efforts required for given tasks and helped users to perceive the recommendation diversity. We show that the users examined a diverse set of recommended items while experiencing an improvement in overall user satisfaction. Also, the users’ subjective evaluations show significant improvement in many user-centric metrics. Experiences are discussed that shed light on avenues for future interface designs
Learning to recommend descriptive tags for questions in social forums
10.1145/2559157ACM Transactions on Information Systems321-ATIS
Tagging Personal Photos with Transfer Deep Learning
The advent of mobile devices and media cloud services has led to the unprecedented growing of personal photo collections. One of the fundamental problems in managing the increasing number of photos is automatic image tagging. Existing research has pre-dominantly focused on tagging general Web images with a well-labelled image database, e.g., ImageNet. However, they can only achieve limited success on personal photos due to the domain gap-s between personal photos and Web images. These gaps originate from the differences in semantic distribution and visual appearance. To deal with these challenges, in this paper, we present a novel transfer deep learning approach to tag personal photos. Specifi-cally, to solve the semantic distribution gap, we have designed an ontology consisting of a hierarchical vocabulary tailored for per-sonal photos. This ontology is mined from 10, 000 active users i
Recommender systems in industrial contexts
This thesis consists of four parts: - An analysis of the core functions and
the prerequisites for recommender systems in an industrial context: we identify
four core functions for recommendation systems: Help do Decide, Help to
Compare, Help to Explore, Help to Discover. The implementation of these
functions has implications for the choices at the heart of algorithmic
recommender systems. - A state of the art, which deals with the main techniques
used in automated recommendation system: the two most commonly used algorithmic
methods, the K-Nearest-Neighbor methods (KNN) and the fast factorization
methods are detailed. The state of the art presents also purely content-based
methods, hybridization techniques, and the classical performance metrics used
to evaluate the recommender systems. This state of the art then gives an
overview of several systems, both from academia and industry (Amazon, Google
...). - An analysis of the performances and implications of a recommendation
system developed during this thesis: this system, Reperio, is a hybrid
recommender engine using KNN methods. We study the performance of the KNN
methods, including the impact of similarity functions used. Then we study the
performance of the KNN method in critical uses cases in cold start situation. -
A methodology for analyzing the performance of recommender systems in
industrial context: this methodology assesses the added value of algorithmic
strategies and recommendation systems according to its core functions.Comment: version 3.30, May 201
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