By Derek Bridge, Heiner Stuckenschmidt
This booklet constitutes revised complaints of the seventeenth overseas convention on E-Commerce and internet applied sciences, EC-Web 2016, held in Porto, Portugal, in September 2016, together with DEXA.
The thirteen papers awarded during this quantity have been conscientiously reviewed and chosen from 21 submissions. They have been geared up in topical sections named: recommender structures; info administration and information research; and enterprise techniques, 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 resources for E-Commerce and Web Technologies: 17th International Conference, EC-Web 2016, Porto, Portugal, September 5-8, 2016, Revised Selected Papers
The eﬀect of measurement task transparency on preference construction and evaluations of personalized recommendations. J. Mark. Res. 44(2), 224– 233 (2007) 29. : Critiquing-based recommenders: survey and emerging trends. User Model. User Adapt. Interact. 22(1–2), 125–150 (2012) 30. : Social recommender systems. , Shapira, B. ) Recommender Systems for the Social Web, pp. 3–42. Springer, New York (2012) How to Combine Visual Features with Tags to Improve Movie Recommendation Accuracy? it Abstract.
However, basic critiquing schemes can lead to a high number of required iterations until a suitable product is found. Compound critiques, on the other hand, can induce higher cognitive load for the users. Finally, similar to form-based approaches the problem can arise that no more suitable items remain that can be recommended, which means that the system has to implement a recovery strategy for the user. 3 Control in the Context of Recommendation Results Dynamically Adjusting the Recommendations.
1. Illustration of the proposed video recommender system based on stylistic lowlevel visual feature and user-generated tag using a fusion method based on CCA Figure 1 illustrates the building block of the developed video recommender system. Color variance and lighting key are the extracted static features and camera and object motion are the dynamic features. 4 Recommendation Algorithm To generate recommendations, we adopted a classical “k-nearest neighbor” content-based algorithm. Given a set of users U and a catalogue of items I, a set of preference scores rui has been collected.