diff --git a/25_Outline/outline.bib b/25_Outline/outline.bib index 3bbde9f..e8a98d1 100644 --- a/25_Outline/outline.bib +++ b/25_Outline/outline.bib @@ -344,6 +344,22 @@ langid = {english} } +@inproceedings{dingContextAwareRecommender2018, + title = {Context {{Aware Recommender System}} for {{Large Scaled Flash Sale Sites}}}, + booktitle = {2018 {{IEEE International Conference}} on {{Big Data}} ({{Big Data}})}, + author = {Ding, Wanying and Xu, Ran and Ding, Ying and Zhang, Yue and Luo, Chuanjiang and Yu, Zhendong}, + date = {2018-12}, + pages = {993--1000}, + publisher = {{IEEE}}, + location = {{Seattle, WA, USA}}, + doi = {10.1109/BigData.2018.8622062}, + abstract = {Flash Sale Sites popularize because they save great money for users. Good recommender systems can further save users' time to improve their online shopping experiences. Although there exsit a lot of studies on recommender system, very few focus on flash sale sites. Big Data, Context Sensitivity, and Feature Engineering are three key challenges for one to build a good recommender system. This paper proposes two deep learning oriented models: Tensor-AutoRec and HybridAutoRec to cope with the problems within an industrial context. First, these two models can handle storage and speed problem caused by big data. Second, both models incorporate context information, so they can generate more relevant recommendations by adapting to specific contextual situations. Third, our deep learning-based models can be trained end-to-end without tedious feature engineerings. Extensive experiments with a half year real transcation data demonstrate that our models can outperform classifcal ones in terms of different evaluation metrices. Finally, online A/B testing results showed that our model can improve our old recommendation system over various online performance indicators.}, + eventtitle = {2018 {{IEEE International Conference}} on {{Big Data}} ({{Big Data}})}, + file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\HMKXV8NX\\Ding et al. - 2018 - Context Aware Recommender System for Large Scaled .pdf}, + isbn = {978-1-5386-5035-6}, + langid = {english} +} + @article{elahiSurveyActiveLearning2016, title = {A Survey of Active Learning in Collaborative Filtering Recommender Systems}, author = {Elahi, Mehdi and Ricci, Francesco and Rubens, Neil}, @@ -1053,7 +1069,7 @@ OCLC: 935904837}, langid = {english} } -@article{scholzConfigurationbasedRecommenderSystem2017, +@article{scholzConfigurationbasedRecommenderSystem2017a, title = {A Configuration-Based Recommender System for Supporting e-Commerce Decisions}, author = {Scholz, Michael and Dorner, Verena and Schryen, Guido and Benlian, Alexander}, date = {2017-05}, @@ -1063,12 +1079,27 @@ OCLC: 935904837}, pages = {205--215}, issn = {03772217}, doi = {10.1016/j.ejor.2016.09.057}, - abstract = {Multi-attribute value theory (MAVT)-based recommender systems have been proposed for dealing with issues of existing recommender systems, such as the cold-start problem and changing preferences. However, as we argue in this paper, existing MAVT-based methods for measuring attribute importance weights do not fit the shopping tasks for which recommender systems are typically used. These methods assume well-trained decision makers who are willing to invest time and cognitive effort, and who are familiar with the attributes describing the available alternatives and the ranges of these attribute levels. Yet, recommender systems are most often used by consumers who are usually not familiar with the available attributes and ranges and who wish to save time and effort. Against this background, we develop a new method, based on a product configuration process, which is tailored to the characteristics of these particular decision makers. We empirically compare our method to SWING, ranking-based conjoint analysis and TRADEOFF in a betweensubjects laboratory experiment with 153 participants. Results indicate that our proposed method performs better than TRADEOFF and CONJOINT and at least as well as SWING in terms of recommendation accuracy, better than SWING and TRADEOFF and at least as well as CONJOINT in terms of cognitive load, and that participants were faster with our method than with any other method. We conclude that our method is a promising option to help support consumers' decision processes in e-commerce shopping tasks.}, - file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\TJSJBZU3\\Scholz et al. - 2017 - A configuration-based recommender system for suppo.pdf}, + abstract = {Multi-attribute value theory (MAVT)-based recommender systems have been proposed for dealing with issues of existing recommender systems, such as the cold-start problem and changing preferences. However, as we argue in this paper, existing MAVT-based methods for measuring attribute importance weights do not fit the shopping tasks for which recommender systems are typically used. These methods assume well-trained decision makers who are willing to invest time and cognitive effort, and who are familiar with the attributes describing the available alternatives and the ranges of these attribute levels. Yet, recommender systems are most often used by consumers who are usually not familiar with the available attributes and ranges and who wish to save time and effort. Against this background, we develop a new method, based on a product configuration process, which is tailored to the characteristics of these particular decision makers. We empirically compare our method to SWING, ranking-based conjoint analysis and TRADEOFF in a between-subjects laboratory experiment with 153 participants. Results indicate that our proposed method performs better than TRADEOFF and CONJOINT and at least as well as SWING in terms of recommendation accuracy, better than SWING and TRADEOFF and at least as well as CONJOINT in terms of cognitive load, and that participants were faster with our method than with any other method. We conclude that our method is a promising option to help support consumers' decision processes in e-commerce shopping tasks.}, + file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\MUE33V9K\\Scholz et al. - 2017 - A configuration-based recommender system for suppo.pdf}, langid = {english}, number = {1} } +@article{scholzEffectsDecisionSpace2017, + title = {Effects of Decision Space Information on {{MAUT}}-Based Systems That Support Purchase Decision Processes}, + author = {Scholz, Michael and Franz, Markus and Hinz, Oliver}, + date = {2017-05}, + journaltitle = {Decision Support Systems}, + shortjournal = {Decision Support Systems}, + volume = {97}, + pages = {43--57}, + issn = {01679236}, + doi = {10.1016/j.dss.2017.03.004}, + abstract = {This paper shows that decision makers often have a misconception of the decision space. The decision space is constituted by the relations among the attributes describing the alternatives available in a decision situation. The paper demonstrates that these misconceptions negatively affect the usage and perceptions of MAUT-based decision support systems. To overcome these negative effects, this paper proposes to use a visualization method based on singular value decomposition to give decision makers insights into the attribute relations. In a laboratory experiment in cooperation with Germany's largest Internet real estate website, this paper moreover evaluates the proposed solution and shows that our solution improves decision makers' usage and perceptions of MAUT-based decision support systems. We further show that information about the decision space ultimately affects variables relevant for the economic success of decision support system providers such as reuse intention and the probability to act as a promoter for the systems.}, + file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\939QYBKB\\Scholz et al. - 2017 - Effects of decision space information on MAUT-base.pdf}, + langid = {english} +} + @article{schulz-hardtProductiveConflictGroup2002, title = {Productive Conflict in Group Decision Making: Genuine and Contrived Dissent as Strategies to Counteract Biased Information Seekingq}, author = {Schulz-Hardt, Stefan and Jochims, Marc and Frey, Dieter},