By Jonathan Gemmell, Thomas Schimoler, Bamshad Mobasher, Robin Burke (auth.), Francesco Buccafurri, Giovanni Semeraro (eds.)
After the lesson realized in the course of final years and following the winning version of EC-Web 2009, for its eleventh version EC-Web attempted to supply a clearer description of the digital trade universe concentrating on a few suitable subject matters. the focus used to be not just on Internet-related innovations and methods. the purpose of EC-Web 2010 used to be to additionally hide facets on the topic of theoretical foundations of e-commerce, company methods in addition to new ways exploiting lately emerged applied sciences and eventualities similar to the Semantic internet, net companies, SOA architectures, cellular and ubiquitous computing, simply to cite a number of. as a result of their valuable position in any lifelike e-commerce infrastructure, protection and privateness concerns have been generally thought of, with out except for criminal and regulatory points. the alternative of the above appropriate issues without delay displays the truth that digital trade (EC), within the previous couple of years, has replaced and advanced right into a well-established and based fact either from a technological standpoint and from a systematic one. however, including its evolution, new demanding situations and subject matters have emerged in addition to new questions were raised relating to many elements of EC. preserving in brain the adventure of the final variation of EC-Web, we maintained, for its eleventh variation, the constitution and the clinical association of EC-Web 2009, aiming to spotlight the self reliant position of the several (sometimes heterogeneous) elements of EC, with out lacking their interdisciplinary scope.
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Additional info for E-Commerce and Web Technologies: 11th International Conference, EC-Web 2010, Bilbao, Spain, September 1-3, 2010. Proceedings
Algo4, diﬀerently from our algorithm, does not exploit textual and link analysis. Moreover, when it queries external information sources, instead of the forp 1 ,uri2 mula (1), it uses the co-occurrence formula: puri +puri uri2 −puri1 ,uri2 1 Algo5 is equivalent to Algo4, but it uses similarity distance  instead of co-occurrence. We did not choose to use either co-occurrence or similarity distance in DBpediaRanker since they do not work well when one of the two resources is extremely more popular than the other, while formula (1) allows to catch this situation.
The user evaluation is run in two sessions. Session I focuses on photo content. Information provided for user evaluation includes the title, picture and tags of the test photos. A list of groups is also given to the evaluators to identify related ones for each photo. Group title, topic photos (if available) and top 5 tags of that group are provided to assist the judgment. All the camera groups are not included in the list given. As most of the groups have an unbiased, content based central concept, it is relatively easy for an evaluator to associate those groups with a photo by the information we provide.
Photo query and group query APIs provided by Flickr are used to obtain training dataset for the SVM classiﬁer. Once the photos concepts are identiﬁed, the top ’n’ concept names are taken as keywords to recommend groups. This approach relies mainly on visual features of the photo. It also rely on a manually selected “popular” concepts such as “animals”, “architecture”, “dog”, “cat”, “snake”, “portrait”, etc. This kind of supervised learning with a pre-deﬁned category list is not suitable for dynamic and open system as Flickr or other social tagging system.