Publikationen

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen

2018

Lacic Emanuel, Kowald Dominik, Reiter-Haas Markus, Slawicek Valentin, Lex Elisabeth

Beyond Accuracy Optimization: On the Value of Item Embeddings for Student Job Recommendation

In Proceedings of the International Workshop on Multi-dimensional Information Fusion for User Modeling and Personalization (IFUP'2018) co-located with the 11th ACM International Conference on Web Search and Data Mining, WSDM'2018, ACM, Los Angeles, USA, 2018

Konferenz
In this work, we address the problem of recommending jobs touniversity students. For this, we explore the impact of using itemembeddings for a content-based job recommendation system. Fur-thermore, we utilize a model from human memory theory to integratethe factors of frequency and recency of job posting interactions forcombining item embeddings. We evaluate our job recommendationsystem on a dataset of the Austrian student job portal Studo usingprediction accuracy, diversity as well as adapted novelty, which isintroduced in this work. We find that utilizing frequency and recencyof interactions with job postings for combining item embeddingsresults in a robust model with respect to accuracy and diversity, butalso provides the best adapted novelty results
2018

Lacic Emanuel, Traub Matthias, Duricic Tomislav, Haslauer Eva, Lex Elisabeth

Gone in 30 Days! Predictions for Car Import Planning

it - Information Technology, De Gruyter Oldenbourg, 2018

Journal
A challenge for importers in the automobile industry is adjusting to rapidly changing market demands. In this work, we describe a practical study of car import planning based on the monthly car registrations in Austria. We model the task as a data driven forecasting problem and we implement four different prediction approaches. One utilizes a seasonal ARIMA model, while the other is based on LSTM-RNN and both compared to a linear and seasonal baselines. In our experiments, we evaluate the 33 different brands by predicting the number of registrations for the next month and for the year to come.
2018

Duricic Tomislav, Lacic Emanuel, Kowald Dominik, Lex Elisabeth

Trust-Based Collaborative Filtering: Tackling the Cold Start Problem Using Regular Equivalenc

RecSys 2018, ACM, Vancouver, Canada, 2018

Konferenz
User-based Collaborative Filtering (CF) is one of the most popularapproaches to create recommender systems. Œis approach is basedon €nding the most relevant k users from whose rating history wecan extract items to recommend. CF, however, su‚ers from datasparsity and the cold-start problem since users o‰en rate only asmall fraction of available items. One solution is to incorporateadditional information into the recommendation process such asexplicit trust scores that are assigned by users to others or implicittrust relationships that result from social connections betweenusers. Such relationships typically form a very sparse trust network,which can be utilized to generate recommendations for users basedon people they trust. In our work, we explore the use of a measurefrom network science, i.e. regular equivalence, applied to a trustnetwork to generate a similarity matrix that is used to select thek-nearest neighbors for recommending items. We evaluate ourapproach on Epinions and we €nd that we can outperform relatedmethods for tackling cold-start users in terms of recommendationaccuracy
2018

Kowald Dominik, Lacic Emanuel, Theiler Dieter, Lex Elisabeth

AFEL-REC: A Recommender System for Providing Learning Resource Recommendations in Social Learning Environments.

CIKM 2018 Workshop Proceedings, CEUR, Turin, Italy, 2018

Konferenz
In this paper, we present preliminary results of AFEL-REC, a rec-ommender system for social learning environments. AFEL-RECis build upon a scalable so‰ware architecture to provide recom-mendations of learning resources in near real-time. Furthermore,AFEL-REC can cope with any kind of data that is present in sociallearning environments such as resource metadata, user interactionsor social tags. We provide a preliminary evaluation of three rec-ommendation use cases implemented in AFEL-REC and we €ndthat utilizing social data in form of tags is helpful for not only im-proving recommendation accuracy but also coverage. ‘is papershould be valuable for both researchers and practitioners inter-ested in providing resource recommendations in social learningenvironments
2018

Lacic Emanuel, Kowald Dominik, Lex Elisabeth

Neighborhood Troubles: On the Value of User Pre-Filtering ToSpeed Up and Enhance Recommendation

CIKM 2018 Workshop Proceedings, Turin, Italy, 2018

Konferenz
In this paper, we present work-in-progress on applying user pre-filtering to speed up and enhance recommendations based on Collab-orative Filtering. We propose to pre-filter users in order to extracta smaller set of candidate neighbors, who exhibit a high numberof overlapping entities and to compute the final user similaritiesbased on this set. To realize this, we exploit features of the high-performance search engine Apache Solr and integrate them into ascalable recommender system. We have evaluated our approachon a dataset gathered from Foursquare and our evaluation resultssuggest that our proposed user pre-filtering step can help to achieveboth a better runtime performance as well as an increase in overallrecommendation accuracy
2017

Lacic Emanuel, Kowald Dominik, Lex Elisabeth

Tailoring Recommendations for a Multi-Domain Environment

ACM International Conference on Recommender Systems 2017, RecSys'2017, ACM, Como, Italy, 2017

Konferenz
Recommender systems are acknowledged as an essential instrumentto support users in finding relevant information. However,the adaptation of recommender systems to multiple domain-specificrequirements and data models still remains an open challenge. Inthe present paper, we contribute to this sparse line of research withguidance on how to design a customizable recommender systemthat accounts for multiple domains with heterogeneous data. Usingconcrete showcase examples, we demonstrate how to setup amulti-domain system on the item and system level, and we reportevaluation results for the domains of (i) LastFM, (ii) FourSquare,and (iii) MovieLens. We believe that our findings and guidelinescan support developers and researchers of recommender systemsto easily adapt and deploy a recommender system in distributedenvironments, as well as to develop and evaluate algorithms suitedfor multi-domain settings
2017

Reiter-Haas Markus, Slawicek Valentin, Lacic Emanuel

Studo Jobs: Enriching Data With Predicted Job Labels

ACM, Graz, 2017

Konferenz
2016

Lacic Emanuel

Real-Time Recommendations in a Multi-Domain Environment

ACM Hypertext Doctoral Consortium, ACM, 2016

Konferenz
Recommender systems are acknowledged as an essential instru- ment to support users in finding relevant information. However, adapting to different domain specific data models is a challenge, which many recommender frameworks neglect. Moreover, the ad- vent of the big data era has posed the need for high scalability and real-time processing of frequent data updates, and thus, has brought new challenges for the recommender systems’ research community. In this work, we show how different item, social and location data features can be utilized and supported to provide real-time recom- mendations. We further show how to process data updates online and capture user’s real-time interest without recalculating recom- mendations. The presented recommendation framework provides a scalable and customizable architecture suited for providing real- time recommendations to multiple domains. We further investigate the impact of an increasing request load and show how the runtime can be decreased by scaling the framework.
2016

Lacic Emanuel, Kowald Dominik, Lex Elisabeth

High Enough? Explaining and Predicting Traveler Satisfaction Using Airline Reviews.

27th ACM Conference on Hypertext and Hypermedia, Hypertext'2016, ACM, Halifax, 2016

Konferenz
Air travel is one of the most frequently used means of transportation in our every-day life. Thus, it is not surprising that an increasing number of travelers share their experiences with airlines and airports in form of online reviews on the Web. In this work, we thrive to explain and uncover the features of airline reviews that contribute most to traveler satisfaction. To that end, we examine reviews crawled from the Skytrax air travel review portal. Skytrax provides four review categories to review airports, lounges, airlines and seats. Each review category consists of several five-star ratings as well as free-text review content. In this paper, we conducted a comprehensive feature study and we find that not only five-star rating information such as airport queuing time and lounge comfort highly correlate with traveler satisfaction but also textual features in the form of the inferred review text sentiment. Based on our findings, we created classifiers to predict traveler satisfaction using the best performing rating features. Our results reveal that given our methodology, traveler satisfaction can be predicted with high accuracy. Additionally, we find that training a model on the sentiment of the review text provides a competitive alternative when no five star rating information is available. We believe that our work is of interest for researchers in the area of modeling and predicting user satisfaction based on available review data on the Web.
2016

Traub Matthias, Lacic Emanuel, Kowald Dominik, Kahr Martin, Lex Elisabeth

Need Help? Recommending Social Care Institutions

Workshop on Recommender Systems and Big Data Analytics co-located with i-know 2016 conference, RSBDA'16, ACM, Graz, 2016

Konferenz
In this paper, we present work-in-progress on a recommender system designed to help people in need find the best suited social care institution for their personal issues. A key requirement in such a domain is to assure and to guarantee the person's privacy and anonymity in order to reduce inhibitions and to establish trust. We present how we aim to tackle this barely studied domain using a hybrid content-based recommendation approach. Our approach leverages three data sources containing textual content, namely (i) metadata from social care institutions, (ii) institution specific FAQs, and (iii) questions that a specific institution has already resolved. Additionally, our approach considers the time context of user questions as well as negative user feedback to previously provided recommendations. Finally, we demonstrate an application scenario of our recommender system in the form of a real-world Web system deployed in Austria.
2015

Lacic Emanuel, Kowald Dominik, Eberhard Lukas, Trattner Christoph, Parra Denis, Marinho Leandro

Utilizing Online Social Network and Location-Based Data to Recommend Products and Categories in Online Marketplaces

Mining, Modeling, and Recommending'Things' in Social Media, MSM'2015, Springer, 2015

Buch
Recent research has unveiled the importance of online social networks for improving the quality of recommender systems and encouraged the research community to investigate better ways of exploiting the social information for recommendations. To contribute to this sparse field of research, in this paper we exploit users’ interactions along three data sources (marketplace, social network and location-based) to assess their performance in a barely studied domain: recommending products and domains of interests (i.e., product categories) to people in an online marketplace environment. To that end we defined sets of content- and network-based user similarity features for each data source and studied them isolated using an user-based Collaborative Filtering (CF) approach and in combination via a hybrid recommender algorithm, to assess which one provides the best recommendation performance. Interestingly, in our experiments conducted on a rich dataset collected from SecondLife, a popular online virtual world, we found that recommenders relying on user similarity features obtained from the social network data clearly yielded the best results in terms of accuracy in case of predicting products, whereas the features obtained from the marketplace and location-based data sources also obtained very good results in case of predicting categories. This finding indicates that all three types of data sources are important and should be taken into account depending on the level of specialization of the recommendation task.
2015

Lacic Emanuel, Luzhnica Granit, Simon Jörg Peter, Traub Matthias, Lex Elisabeth, Kowald Dominik

Tackling Cold-Start Users in Recommender Systems with Indoor Positioning Systems

Proceedings of 9th International Conference on Recommender Systems, RecSys'2015, ACM, Vienna, Austria, 2015

Konferenz
In this paper, we present work-in-progress on a recommender system based on Collaborative Filtering that exploits location information gathered by indoor positioning systems. This approach allows us to provide recommendations for "extreme" cold-start users with absolutely no item interaction data available, where methods based on Matrix Factorization would not work. We simulate and evaluate our proposed system using data from the location-based FourSquare system and show that we can provide substantially better recommender accuracy results than a simple MostPopular baseline that is typically used when no interaction data is available.
2015

Lacic Emanuel, Traub Matthias, Kowald Dominik, Lex Elisabeth

ScaR: Towards a Real-Time Recommender Framework Following the Microservices Architecture

In the Large-Scale Recommender Systems Workshop (LSRS'15) at the 9th International Conference on Recommender Systems, RecSys'2015, ACM, Vienna, Austria, 2015

Konferenz
In this paper, we present our approach towards an effective scalable recommender framework termed ScaR. Our framework is based on the microservices architecture and exploits search technology to provide real-time recommendations. Since it is our aim to create a system that can be used in a broad range of scenarios, we designed it to be capable of handling various data streams and sources. We show its efficacy and scalability with an initial experiment on how the framework can be used in a large-scale setting.
2015

Dennerlein_Bildungskarenz Sebastian, Kowald Dominik, Lex Elisabeth, Lacic Emanuel, Theiler Dieter, Ley Tobias

The Social Semantic Server: A Flexible Framework to Support Informal Learning at the Workplace

In Proceedings of the 15th International Conference on Knowledge Technologies and Data-Driven Business, i-know 2015, ACM, Graz, Austria, 2015

Konferenz
Informal learning at the workplace includes a multitude of processes. Respective activities can be categorized into multiple perspectives on informal learning, such as reflection, sensemaking, help seeking and maturing of collective knowledge. Each perspective raises requirements with respect to the technical support, this is why an integrated solution relying on social, adaptive and semantic technologies is needed. In this paper, we present the Social Semantic Server, an extensible, open-source application server that equips clientside tools with services to support and scale informal learning at the workplace. More specifically, the Social Semantic Server semantically enriches social data that is created at the workplace in the context of user-to-user or user-artifact interactions. This enriched data can then in turn be exploited in informal learning scenarios to, e.g., foster help seeking by recommending collaborators, resources, or experts. Following the design-based research paradigm, the Social Semantic Server has been implemented based on design principles, which were derived from theories such as Distributed Cognition and Meaning Making. We illustrate the applicability and efficacy of the Social Semantic Server in the light of three real-world applications that have been developed using its social semantic services. Furthermore, we report preliminary results of two user studies that have been carried out recently.
2015

Traub Matthias, Kowald Dominik, Lacic Emanuel, Lex Elisabeth, Schoen Pepjin, Supp Gernot

Smart booking without looking: providing hotel recommendations in the TripRebel portal

Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business, i-know 2015, ACM, Graz, Austria, 2015

Konferenz
In this paper, we present a scalable hotel recommender system for TripRebel, a new online booking portal. On the basis of the open-source enterprise search platform Apache Solr, we developed a system architecture with Web-based services to interact with indexed data at large scale as well as to provide hotel recommendations using various state-of-the-art recommender algorithms. We demonstrate the efficiency of our system directly using the live TripRebel portal where, in its current state, hotel alternatives for a given hotel are calculated based on data gathered from the Expedia AffiliateNetwork (EAN).
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