Proceedings of the 13th ACM Conference on Recommender Systems最新文献

{"title":"Future of in-vehicle recommendation systems @ Bosch","authors":"J. Luettin, Susanne Rothermel, Mark Andrew","doi":"10.1145/3298689.3346958","DOIUrl":"https://doi.org/10.1145/3298689.3346958","url":null,"abstract":"Future in-vehicle recommendation systems will assist the driver or passenger in all situations before, along, and after a trip. Based on preferences and needs of the user and by taking the current situation and available context information into account, they will provide the right recommendation at the right time. Bosch is the world's largest automotive supplier, delivering a full range of products and services from power-train, infotainment, HMI, connected mobility, driver assistance to automated driving. This talk will present challenges, concepts and recent technical progress in in-vehicle recommendation systems developed at Bosch including details of a combined routing, charging, and point-of-interest (POI) recommendation system. There has been tremendous progress in the field of location-independent recommendation systems, such as recommending films, music, news or shopping articles. The ubiquity of user location information, provided by connected devices, has paved the way for location-based services (LBS), and their combination with social networks have extended these to location-based social network (LBSN) services, see [1, 6] for recent surveys about recommender systems in LBSN. In-vehicle recommendation systems go a step further by extending LBSN services with vehicle context and vehicle specific applications. This can support the user in various applications, such as routing (e.g. route and point of interest recommendation), infotainment (e.g. music or news recommendation), communication (finding a contact, fast call) and in-vehicle control (e.g. seat position, ambient light or HVAC settings). Out-of-vehicle assistance includes the control of connected devices in smart buildings such as alarm systems, heating, kitchen and entertainment devices. We present an important application of in-vehicle recommending systems, a combined routing, charging and POI recommender developed at Bosch. Routing and charging optimization for electric vehicles was described for optimizing the shortest feasible path [2], optimizing constrained shortest path [4], optimizing charging grid demand and opportunities [5], and optimizing minimum cost [3]. These approaches focus on single criteria based optimization. We describe the first system with combined route optimization, charging station search and POI recommendation. It optimizes three criteria: finding the optimal route with the optimal charging stations, so that the vehicle always has enough energy, and finding the optimal POIs along the route, where 'optimal' depends on the drivers preferences and rich context information covering user, vehicle and environment.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128345127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Whose data traces, whose voices? Inequality in online participation and why it matters for recommendation systems research","authors":"E. Hargittai","doi":"10.1145/3298689.3347066","DOIUrl":"https://doi.org/10.1145/3298689.3347066","url":null,"abstract":"As research relies on data traces about people's online behavior, it is important to take a step back and ask: who uses the systems where these traces appear? This talk will discuss online participation from a digital-inequality perspective showing how differences in online behavior vary by socio-demographic characteristics as well as people's Internet skills. The presentation breaks down the various steps necessary for engagement - the pipeline of online participation - and shows that different factors explain different parts of the pipeline with skills mattering at all stages. Drawing on several data sets, the talk explores whose traces are most likely to show up on various systems and what this means for potential biases in what researchers draw from analyzing digital trace data.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"271 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132924884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recommendations in a marketplace","authors":"Rishabh Mehrotra, Ben Carterette","doi":"10.1145/3298689.3346952","DOIUrl":"https://doi.org/10.1145/3298689.3346952","url":null,"abstract":"In recent years, two sided marketplaces have emerged as viable business models in many real world applications (e.g. Uber, AirBnb), wherein the platforms have customers not only on the demand side (e.g. users), but also on the supply side (e.g. drivers, hosts). Such multi-sided marketplace involves interaction between multiple stakeholders among which there are different individuals with assorted needs. While traditional recommender systems focused specifically towards increasing consumer satisfaction by providing relevant content to consumers, two-sided marketplaces face an interesting problem of optimizing their models for supplier preferences, and visibility. In this tutorial, we consider a number of research problems which need to be address when developing a recommendation framework powering a multi-stakeholder marketplace, and provides audience with a profound introduction to this upcoming area and presents directions of further research. Tutorial material available at: https://rishabhmehrotra.github.io/recs-in-marketplace/","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131112190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Workshop on recommender systems in fashion (fashionXrecsys2019)","authors":"Shatha Jaradat, Nima Dokoohaki, Humberto Jesús Corona Pampín, Reza Shirvany","doi":"10.1145/3298689.3347056","DOIUrl":"https://doi.org/10.1145/3298689.3347056","url":null,"abstract":"Online Fashion retailers have significantly increased in popularity over the last decade, making it possible for customers to explore hundreds of thousands of products without the need to visit multiple stores or stand in long queues for checkout. Recommender Systems are often used to solve different complex problems in this scenario, such as social fashion-aware recommendations (outfits inspired by influencers), product recommendations, or size and fit recommendations. However, relatively little research has been done on these complex problems. The very First fashionXrecsys Workshop aims at addressing these issues by providing a avenue for discussing novel approaches to recommendations in fashion and e-commerce applications.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"152 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114841466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Compositional network embedding for link prediction","authors":"Tianshu Lyu, Fei Sun, Peng Jiang, Wenwu Ou, Yan Zhang","doi":"10.1145/3298689.3347023","DOIUrl":"https://doi.org/10.1145/3298689.3347023","url":null,"abstract":"Almost all the existing network embedding methods learn to map the node IDs to their corresponding node embeddings. This design principle, however, hinders the existing methods from being applied in real cases. Node ID is not generalizable and, thus, the existing methods have to pay great effort in cold-start problem. The heterogeneous network usually requires extra work to encode node types, as node type is not able to be identified by node ID. Node ID carries rare information, resulting in the criticism that the existing methods are not robust to noise. To address this issue, we introduce Compositional Network Embedding, a general inductive network representation learning framework that generates node embeddings by combining node features based on the \"principle of compositionally\". Instead of directly optimizing an embedding lookup based on arbitrary node IDs, we learn a composition function that infers node embeddings by combining the corresponding node attribute embeddings through a graph-based loss. For evaluation, we conduct the experiments on link prediction under three different settings. The results verified the effectiveness and generalization ability of compositional network embeddings, especially on unseen nodes.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132143332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards interactive recommending in model-based collaborative filtering systems","authors":"Benedikt Loepp, J. Ziegler","doi":"10.1145/3298689.3346949","DOIUrl":"https://doi.org/10.1145/3298689.3346949","url":null,"abstract":"Numerous attempts have been made for increasing the interactivity in recommender systems, but the features actually available in today's systems are in most cases limited to rating or re-rating single items. We present a demonstrator that showcases how model-based collaborative filtering recommenders may be enhanced with advanced interaction and preference elicitation mechanisms in a holistic manner. Hereby, we underline that by employing methods we have proposed in the past it becomes possible to easily extend any matrix factorization recommender into a fully interactive, user-controlled system. By presenting and deploying our demonstrator, we aim at gathering further insights, both into how the different mechanisms may be intertwined even more closely, and how interaction behavior and resulting user experience are influenced when users can choose from these mechanisms at their own discretion.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133281181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The 7th international workshop on news recommendation and analytics (INRA 2019)","authors":"Özlem Özgöbek, B. Kille, J. Gulla, A. Lommatzsch","doi":"10.1145/3298689.3346972","DOIUrl":"https://doi.org/10.1145/3298689.3346972","url":null,"abstract":"Publishing news represents a vital function for societal health. News recommender systems, which support readers finding relevant content, face challenges beyond those encountered by other types of recommender systems. They have to deal with a dynamic flow of unstructured, fragmentary, and potentially unreliable news stories. The International Workshop on News Recommendation and Analytics (INRA) focuses on the challenges of news recommender systems and aims to connect researchers, practitioners and journalists. The seventh edition of INRA takes place as a half-day workshop in conjunction with thirteenth ACM Conference on Recommender Systems (RecSys '19) on September 16--20, 2019 in Copenhagen, Denmark. INRA 2019 focuses on the news recommender systems under three main categories: News recommendation, news analytics, and ethical aspects of news recommendation.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132813883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pace my race: recommendations for marathon running","authors":"J. Berndsen, B. Smyth, A. Lawlor","doi":"10.1145/3298689.3346991","DOIUrl":"https://doi.org/10.1145/3298689.3346991","url":null,"abstract":"We propose marathon running as a novel domain for recommender systems and machine learning. Using high-resolution marathon performance data from multiple marathon races (n = 7931), we build in-race recommendations for runners. We show that we can outperform the existing techniques which are currently employed for in-race finish-time prediction, and we demonstrate how such predictions may be used to make real time recommendations to runners. The recommendations are made at critical points in the race to provide personalised guidance so the runner can adjust their race strategy. Through the association of model features and the expert domain knowledge of marathon runners we generate explainable, adaptable pacing recommendations which can guide runners to their best possible finish time and help them avoid the potentially catastrophic effects of hitting the wall.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132901044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Designing for the better by taking users into account: a qualitative evaluation of user control mechanisms in (news) recommender systems","authors":"Jaron Harambam, D. Bountouridis, M. Makhortykh, J. Hoboken","doi":"10.1145/3298689.3347014","DOIUrl":"https://doi.org/10.1145/3298689.3347014","url":null,"abstract":"Recommender systems (RS) are on the rise in many domains. While they offer great promises, they also raise concerns: lack of transparency, reduction of diversity, little to no user control. In this paper, we align with the normative turn in computer science which scrutinizes the ethical and societal implications of RS. We focus and elaborate on the concept of user control because that mitigates multiple problems at once. Taking the news industry as our domain, we conducted four focus groups, or moderated think-aloud sessions, with Dutch news readers (N=21) to systematically study how people evaluate different control mechanisms (at the input, process, and output phase) in a News Recommender Prototype (NRP). While these mechanisms are sometimes met with distrust about the actual control they offer, we found that an intelligible user profile (including reading history and flexible preferences settings), coupled with possibilities to influence the recommendation algorithms is highly valued, especially when these control mechanisms can be operated in relation to achieving personal goals. By bringing (future) users' perspectives to the fore, this paper contributes to a richer understanding of why and how to design for user control in recommender systems.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"4 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132444111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pick & merge: an efficient item filtering scheme for Windows store recommendations","authors":"Adi Makmal, Jonathan Ephrath, Hilik Berezin, Liron Allerhand, Nir Nice, Noam Koenigstein","doi":"10.1145/3298689.3347005","DOIUrl":"https://doi.org/10.1145/3298689.3347005","url":null,"abstract":"Microsoft Windows is the most popular operating system (OS) for personal computers (PCs). With hundreds of millions of users, its app marketplace, Windows Store, is one of the largest in the world. As such, special considerations are required in order to improve online computational efficiency and response times. This paper presents the results of an extensive research of effective filtering method for semi-personalized recommendations. The filtering problem, defined here for the first time, addresses an aspect that was so far largely overlooked by the recommender systems literature, namely effective and efficient method for removing items from semi-personalized recommendation lists. Semi-personalized recommendation lists serve a common list to a group of people based on their shared interest or background. Unlike fully personalized lists, these lists are cacheable and constitute the majority of recommendation lists in many online stores. This motivates the following question: can we remove (most of) the users' undesired items without collapsing onto fully personalized recommendations? Our solution is based on dividing the users into few subgroups, such that each subgroup receives a different variant of the original recommendation list. This approach adheres to the principles of semi-personalization and hence preserves simplicity and cacheability. We formalize the problem of finding optimal subgroups that minimize the total number of filtering errors, and show that it is combinatorially formidable. Consequently, a greedy algorithm is proposed that filters out most of the undesired items, while bounding the maximal number of errors for each user. Finally, a detailed evaluation of the proposed algorithm is presented using both proprietary and public datasets.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126006386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}