1,335 research outputs found
A Temporal Usage Pattern-based Tag Recommendation Approach
While social tagging can benefit Internet users managing their resources, it suffers the problems such as diverse and/or unchecked vocabulary and unwillingness to tag. Use of freely new tags and/or reuse of frequent tags have degraded coherence of corresponding resources of each tag that further frustrates people in retrieving information due to cognitive dissonance. Tag recommender systems can recommend users the most relevant tags to the resource they intend to annotate, and drastically transfer the tagging process from generation to recognition to reduce user’s cognitive effort and time. Prior research on tag recommendation has addressed the time-dependence issues of tags by applying a time decaying measure to determine the recurrence probability of a tag according to its recency instead of its usage pattern. In response, this study intends to propose the temporal usage pattern-based tag recommendation technique to consider the usage patterns and temporal characteristic of tags for making recommendations
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People-Powered Music: Using User-Generated Tags and Structure in Recommendations
Music recommenders often rely on experts to classify song facets like genre and mood, but user-generated folksonomies hold some advantages over expert classifications—folksonomies can reflect the same real-world vocabularies and categorizations that end users employ. We present an approach for using crowd-sourced common sense knowledge to structure user-generated music tags into a folksonomy, and describe how to use this approach to make music recommendations. We then empirically evaluate our “people-powered” structured content recommender against a more traditional recommender. Our results show that participants slightly preferred the unstructured recommender, rating more of its recommendations as “perfect” than they did for our approach. An exploration of the reasons behind participants’ ratings revealed that users behaved differently when tagging songs than when evaluating recommendations, and we discuss the implications of our results for future tagging and recommendation approaches
VISIR : visual and semantic image label refinement
The social media explosion has populated the Internet with a wealth of images. There are two existing paradigms for image retrieval: 1) content-based image retrieval (CBIR), which has traditionally used visual features for similarity search (e.g., SIFT features), and 2) tag-based image retrieval (TBIR), which has relied on user tagging (e.g., Flickr tags). CBIR now gains semantic expressiveness by advances in deep-learning-based detection of visual labels. TBIR benefits from query-and-click logs to automatically infer more informative labels. However, learning-based tagging still yields noisy labels and is restricted to concrete objects, missing out on generalizations and abstractions. Click-based tagging is limited to terms that appear in the textual context of an image or in queries that lead to a click. This paper addresses the above limitations by semantically refining and expanding the labels suggested by learning-based object detection. We consider the semantic coherence between the labels for different objects, leverage lexical and commonsense knowledge, and cast the label assignment into a constrained optimization problem solved by an integer linear program. Experiments show that our method, called VISIR, improves the quality of the state-of-the-art visual labeling tools like LSDA and YOLO
Overcoming the Imbalance Between Tag Recommendation Approaches and Real-World Folksonomy Structures with Cognitive-Inspired Algorithms
In this paper, we study the imbalance between current state-of-the-art tag
recommendation algorithms and the folksonomy structures of real-world social
tagging systems. While algorithms such as FolkRank are designed for dense
folksonomy structures, most social tagging systems exhibit a sparse nature. To
overcome this imbalance, we show that cognitive-inspired algorithms, which
model the tag vocabulary of a user in a cognitive-plausible way, can be
helpful. Our present approach does this via implementing the activation
equation of the cognitive architecture ACT-R, which determines the usefulness
of units in human memory (e.g., tags). In this sense, our long-term research
goal is to design hybrid recommendation approaches, which combine the
advantages of both worlds in order to adapt to the current setting (i.e.,
sparse vs. dense ones).Comment: Presented at the European Symposium for Computational Social Science
Validation and Evaluation
In this technical report, we present prototypical implementations of
innovative tools and methods for personalized and contextualized (multimedia)
search, collaborative ontology evolution, ontology evaluation and cost models,
and dynamic access and trends in distributed (semantic) knowledge, developed
according to the working plan outlined in Technical Report TR-B-12-04. The
prototypes complete the next milestone on the path to an integral Corporate
Semantic Web architecture based on the three pillars Corporate Ontology
Engineering, Corporate Semantic Collaboration, and Corporate Semantic Search,
as envisioned in TR-B-08-09
DoMoRe – A recommender system for domain modeling
Domain modeling is an important activity in early phases of software projects to achieve a shared understanding of the problem field among project participants. Domain models describe concepts and relations of respective application fields using a modeling language and domain-specific terms. Detailed knowledge of the domain as well as expertise in model-driven development is required for software engineers to create these models. This paper describes DoMoRe, a system for automated modeling recommendations to support the domain modeling process. We describe an approach in which modeling benefits from formalized knowledge sources and information extraction from text. The system incorporates a large network of semantically related terms built from natural language data sets integrated with mediator-based knowledge base querying in a single recommender system to provide context-sensitive suggestions of model elements
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