Multimedia uploaded content is tagged and recommended by users of collaborative systems, resulting in informal classifications also known as folksonomies. Faceted web ranking has been proved a reasonable alternative to a single ranking which does not take into account a personalized context. In this paper we analyze the online computation of rankings of users associated to facets made up of multiple tags. Possible applications are user reputation evaluation (ego-ranking) and improvement of content quality in case of retrieval. We propose a solution based on PageRank as centrality measure: (i) a ranking for each tag is computed offline on the basis of the corresponding tag-dependent subgraph; (ii) a faceted order is generated by merging rankings corresponding to all the tags in the facet. The fundamental assumption, validated by empirical observations, is that step (i) is scalable. We also present algorithms for part (ii) having time complexity O(k), where k is the number of tags in the facet, well suited to online computation.
@cite_16: In ranking algorithms for web graphs, such as PageRank and HITS, the lack of attention to concepts topics representing web page content causes problems such as topic drift and mutually reinforcing relationships between hosts. This paper proposes a novel approach to expand the Web graph to incorporate conceptual information encoded by links (anchor text) between web pages. Using web graph link structure and conceptual information associated with each web page (automatically extracted from anchor text of inlinks), a new graph is defined where each node represents a unique pair of a web page and concept associated with that web page, and an edge represents an explicit or implicit link between two such nodes. This graph captures inter-concept relationships, which is then utilized by ranking algorithms. Our experimental results show that such an approach improves accuracy (e.g., first X precision) by retrieving links which are more authoritative given a user?s context.
There has also been some work done on faceted ranking of web pages. For example, the approach of DeLong, Mane and Srivastava @cite_16 involves the construction of a larger multigraph using the hyperlink graph with each vertex corresponding to a pair webpage-concept and each edge to a hyperlink associated with a concept. Subgraph ideas are suggested by them: It might be faster to simply run PageRank on sub-graphs pertaining to each individual concept (assuming there are a small number of concepts).'' Although DeLong @cite_16 obtain good ranking results for single-keyword facets, they do not support multi-keyword queries.