Titlebook: E-Commerce and Web Technologies; 16th International C Heiner Stuckenschmidt,Dietmar Jannach Conference proceedings 2015 Springer Internatio

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Parabola

China 40 Years Infrastructure Constructionechnique outperforms content-based approaches, it may fail when the query keywords does not match the textual content of many documents containing relevant images. In addition, users are usually not experts and provide ambiguous queries that lead to heterogeneous results. To solve these problems, re

Bravado

Guileless

China Arbitration Yearbook (2022)olarity information complement each other in affecting consumers’ review evaluation. Few extant studies consider the interplay of review rating and text sentiment on perceived review helpfulness. In this study, we attempt to investigate this potential interaction effect and examine whether it is con

指耕作

s, we show that events and news about the stock market are capable of generating peaks of publications by Twitter users and that the frequency of posts follows the starting of the exchange trading day and maintains for about three hours after the stock market closing hour. Moreover, based on a surve

penance

The Basic Questions on Danxia Landformas constraints to be maintained. However, these constraints can be violated at any time due to various unforeseen events that may stem from the process evolving context, leading the process to end up in unfortunate situations. In this paper, we present our framework that correlates critical business

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遣返回國

Product Recommendation for Small-Scale Retailersion. In this work we address a particular product recommendation setting — small-scale retail websites where the small amount of returning customers makes traditional user-centric personalization techniques inapplicable. We apply an item-centric product recommendation strategy which combines two wel

蒸發(fā)

Using Graph Metrics for Linked Open Data Enabled Recommender Systemsproaches transform that data into a propositional form, we investigate how the graph nature of Linked Open Data can be exploited when building recommender systems. In particular, we use path lengths, the K-Step Markov approach, as well as weighted NI paths to compute item relevance and perform a con

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