Social Ranking Techniques for the Web

The proliferation of social media has the potential for changing the structure and organization of the web. In the past, scientists have looked at the web as a large connected component to understand how the topology of hyperlinks correlates with the quality of information contained in the page and they proposed techniques to rank information contained in web pages. We argue that information from web pages and network data on social relationships can be combined to create a personalized and socially connected web. In this paper, we look at the web as a composition of two networks, one consisting of information in web pages and the other of personal data shared on social media web sites. Together, they allow us to analyze how social media tunnels the flow of information from person to person and how to use the structure of the social network to rank, deliver, and organize information specifically for each individual user. We validate our social ranking concepts through a ranking experiment conducted on web pages that users shared on Google Buzz and Twitter.Comment: 7 pages, ASONAM 201

Improving Search Rank by Optimizing TTFB

Now a days, everyone is in a great hurry and no one waits for turtles. Studies have shown that the search ranking are affected by how fast the web page loads. The simple logic behind this is “ A website which takes too much time to load provide poor user experience, today's user would quickly shift to next search result and you will end up loosing one“. The big search engine giant, Google search uses large number of parameters for determining search rankings which are mostly related to the content on your website, URL , social metrics, quality, quantity, SSL certificates, etc. In 2010, Google added one more factor in search ranking which is websites speed. TTFB (Time To First Byte) provides a clear way to determine how fast/slow there web page loads. Hence, here I'm focusing on improving website search engine ranking by applying various techniques to optimize TTF

Enrichment 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 annotations for objects with a minimum amount of effort. Tagging allows communities to organise their multimedia items into browseable sets, but these tags may be poorly chosen and related tags may be omitted. Current techniques to retrieve, integrate and present this media to users are deficient and could do with improvement. In this paper, we describe a framework for semantic enrichment, ranking and integration of web video tags using Semantic Web technologies. Semantic enrichment of folksonomies can bridge the gap between the uncontrolled and flat structures typically found in user-generated content and structures provided by the Semantic Web. The enhancement of tag spaces with semantics has been accomplished through two major tasks: a tag space expansion and ranking step; and through concept matching and integration with the Linked Data cloud. We have explored social, temporal and spatial contexts to enrich and extend the existing tag space. The resulting semantic tag space is modelled via a local graph based on co-occurrence distances for ranking. A ranked tag list is mapped and integrated with the Linked Data cloud through the DBpedia resource repository. Multi-dimensional context filtering for tag expansion means that tag ranking is much easier and it provides less ambiguous tag to concept matching

Multi-Stage Search Architectures for Streaming Documents

The web is becoming more dynamic due to the increasing engagement and contribution of Internet users in the age of social media. A more dynamic web presents new challenges for web search--an important application of Information Retrieval (IR). A stream of new documents constantly flows into the web at a high rate, adding to the old content. In many cases, documents quickly lose their relevance. In these time-sensitive environments, finding relevant content in response to user queries requires a real-time search service; immediate availability of content for search and a fast ranking, which requires an optimized search architecture. These aspects of today's web are at odds with how academic IR researchers have traditionally viewed the web, as a collection of static documents. Moreover, search architectures have received little attention in the IR literature. Therefore, academic IR research, for the most part, does not provide a mechanism to efficiently handle a high-velocity stream of documents, nor does it facilitate real-time ranking. This dissertation addresses the aforementioned shortcomings. We present an efficient mech- anism to index a stream of documents, thereby enabling immediate availability of content. Our indexer works entirely in main memory and provides a mechanism to control inverted list con- tiguity, thereby enabling faster retrieval. Additionally, we consider document ranking with a machine-learned model, dubbed "Learning to Rank" (LTR), and introduce a novel multi-stage search architecture that enables fast retrieval and allows for more design flexibility. The stages of our architecture include candidate generation (top k retrieval), feature extraction, and docu- ment re-ranking. We compare this architecture with a traditional monolithic architecture where candidate generation and feature extraction occur together. As we lay out our architecture, we present optimizations to each stage to facilitate low-latency ranking. These optimizations include a fast approximate top k retrieval algorithm, document vectors for feature extraction, architecture- conscious implementations of tree ensembles for LTR using predication and vectorization, and algorithms to train tree-based LTR models that are fast to evaluate. We also study the efficiency- effectiveness tradeoffs of these techniques, and empirically evaluate our end-to-end architecture on microblog document collections. We show that our techniques improve efficiency without degrading quality

A Review on Personalized Tag based Image based Search Engines

The development of social media based on Web 2.0, amounts of images and videos spring up everywhere on the Internet. This phenomenon has brought great challenges to multimedia storage, indexing and retrieval. Generally speaking, tag-based image search is more commonly used in social media than content based image retrieval and content understanding. Thanks to the low relevance and diversity performance of initial retrieval results, the ranking problem in the tag-based image retrieval has gained researchers� wide attention. We will review some of techniques proposed by different authors for image retrieval in this paper