By Derek Bridge, Heiner Stuckenschmidt
This e-book constitutes revised lawsuits of the seventeenth foreign convention on E-Commerce and net applied sciences, EC-Web 2016, held in Porto, Portugal, in September 2016, along side DEXA.
The thirteen papers provided during this quantity have been rigorously reviewed and chosen from 21 submissions. They have been equipped in topical sections named: recommender structures; information administration and knowledge research; and company approaches, net companies and cloud computing.
Read or Download E-Commerce and Web Technologies: 17th International Conference, EC-Web 2016, Porto, Portugal, September 5-8, 2016, Revised Selected Papers PDF
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Extra info for E-Commerce and Web Technologies: 17th International Conference, EC-Web 2016, Porto, Portugal, September 5-8, 2016, Revised Selected Papers
Fig. 4. Explanation-based control mechanism at Amazon User Control in Recommender Systems 29 In Fig. 4 a recommended item is displayed along with an explanation why it is recommended. The explanation in this case refers to another item that the user already owns. The user can then give feedback on the recommended item by rating it or indicating no interest in the item. Furthermore, the users can instruct the system not to use the already owned item for future recommendations. , because it was a gift.
Table 1. Hierarchical and modality-wise classiﬁcation of multimedia features Level Visual Aural High events, concepts events, concepts semantic similarity Text summary, tag Metadata Mid objects/people, objects’ interaction objects/people, source sentences, keywords genre, tag, cast Low motion, color, shape, lighting timbre, pitch spectral frequency nouns, verbs, adjectives genre, tag Recommender systems in the movie domain typically use high-level or midlevel features such as genre or tag which appears in the form of metadata [19,25, 35].
35–42 (2012) 19. : Inspection mechanisms for community-based content discovery in microblogs. In: RecSys IntRS 2015 Workshop, pp. 21–28 (2015) 20. : A survey of explanations in recommender systems. In: IEEE ICDEW Data Engineering Workshop, pp. 801–810 (2007) 21. : Rapid development of knowledge-based conversational recommender applications with Advisor Suite. J. Web Eng. 6(2), 165–192 (2007) 22. : Interactive explanations in mobile shopping recommender systems. In: RecSys IntRS 2014 Workshop, pp.