Ranking for Social Semantic Media
نویسندگان
چکیده
This diploma thesis is devoted to the ranking of results returned by search engines. We present a State of the Art which covers the ranking for various datatypes, including the Web, XML, RDF, and folksonomies. For every datatype the calculation of a popularity score as well as the computing of a content score is presented. For most datatypes we also discuss the relevance of two objects to each other. This is especially of interest for RDF data. The purpose of this State of the Art is to provide a decent understanding of the technologies in ranking that exist, so based on that it is easier to either come up with an enhanced ranking algorithm or use the technologies provided here as a basis for developing new algorithms for new datatypes. In particular, to the best of our knowledge there is apparently no ranking scheme available for Social Semantic Wikis, which have both semantic annotations as well as structured data. We provide some suggestions for developing such a new algorithm in the section Conclusion and future work.
منابع مشابه
Social Image Search with Diverse Relevance Ranking
Recent years have witnessed the success of many online social media websites, which allow users to create and share media information as well as describe the media content with tags. However, the existing ranking approaches for tag-based image search frequently return results that are irrelevant or lack of diversity. This paper proposes a diverse relevance ranking scheme which is able to simult...
متن کاملTag-Based Social Image Search: Toward Relevant and Diverse Results
Recent years have witnessed a great success of social media websites. Tag-based image search is an important approach to access the image content of interest on these websites. However, the existing ranking methods for tag-based image search frequently return results that are irrelevant or lack of diversity. This chapter presents a diverse relevance ranking scheme which simultaneously takes rel...
متن کاملD-Sempre: Learning Deep Semantic-Preserving Embeddings for User interests-Social Contents Modeling
Exponential growth of social media consumption demands eective user interests-social contents modeling for more personalized recommendation and social media summarization. However, due to the heterogeneous nature of social contents, traditional approaches lack the ability of capturing the hidden semantic correlations across these multi-modal data, which leads to semantic gaps between social co...
متن کاملEnrichment and Ranking of the YouTube Tag Space and Integration with the Linked Data Cloud
The increase of personal digital cameras with video functionality and video-enabled camera phones has increased the amount of user-generated videos on the Web. People are spending more and more time viewing online videos as a major source of entertainment and “infotainment”. Social websites allow users to assign shared free-form tags to user-generated multimedia resources, thus generating annot...
متن کاملRanking Responses Oriented to Conversational Relevance in Chat-bots
For automatic chatting systems, it is indeed a great challenge to reply the given query considering the conversation history, rather than based on the query only. This paper proposes a deep neural network to address the context-aware response ranking problem by end-to-end learning, so as to help to select conversationally relevant candidate. By combining the multi-column convolutional layer and...
متن کامل